Remote Delphi Technique

online Delphi
e-Delphi
virtual Delphi
distributed Delphi

1950s (classic Delphi); remote/e-Delphi from late 1990s

Olaf Helmer and Norman Dalkey (RAND Corporation; classic Delphi); e-Delphi adapted by various methodologists from late 1990s onward

The Remote Delphi Technique applies the structured iterative consensus process of the classic Delphi method entirely through remote communication channels — email, web-based survey platforms, or dedicated collaboration tools — eliminating the need for geographic co-presence. Experts complete successive questionnaire rounds asynchronously, receiving anonymised statistical summaries of the group's prior responses before each new round, until a pre-defined consensus threshold is reached.

Iterative expert consensus technique — remote administration

Structured questionnaire responses, numerical ratings, open-ended comments from remote expert panels

Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.

Donohoe, H., Stellefson, M., & Tennant, B. (2012). Advantages and limitations of the e-Delphi technique: Implications for health education researchers. American Journal of Health Education, 43(1), 38–46.

delphi-technique online-survey remote-focus-group remote-structured-interview remote-semi-structured-interview expert-panel


Remote Document Collection

digital document retrieval
online archival collection
virtual document gathering
remote archival research

2000s–present (digital shift of traditional document collection)

Rooted in classical document analysis (Bowen 2009; Scott 1990); remote modality formalized in digital humanities and qualitative online research from the 2000s onward

Remote Document Collection is a data collection technique in which researchers gather written, visual, or multimedia documents from digital sources — online archives, institutional repositories, cloud storage, email, or government databases — without requiring physical presence. It extends classical document analysis into digital environments, enabling access to geographically dispersed or restricted materials and making it especially valuable for large-scale, cross-national, or time-sensitive research projects.

Qualitative / mixed-methods data collection technique

Textual, visual, and multimedia documents accessed via digital channels

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.

Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage.

document-collection online-document-collection web-scraping api-based-data-collection remote-structured-interview remote-survey


Remote Focus Group

virtual focus group
online focus group
video-mediated focus group
distributed focus group

Late 1990s (synchronous online); mainstream adoption 2020

Adaptation of traditional focus groups (Robert K. Merton, 1940s); remote modality formalized in the late 1990s–2000s and widely adopted post-2020

A Remote Focus Group is a synchronous, moderated group discussion conducted via video or audio conferencing rather than in a shared physical space. Participants — typically 5 to 10 people — join from separate locations and discuss a topic guided by a trained moderator. The method preserves the core strengths of in-person focus groups (group interaction, idea building, spontaneous reactions) while eliminating geographic barriers and reducing recruitment costs. It has become a mainstream qualitative data collection approach, especially following the widespread adoption of video conferencing platforms.

Synchronous verbal and text discussion data via video/audio conferencing

Lobe, B., Morgan, D., & Hoffman, K. A. (2020). Qualitative data collection in an era of social distancing. International Journal of Qualitative Methods, 19, 1–8.

Krueger, R. A., & Casey, M. A. (2015). Focus Groups: A Practical Guide for Applied Research (5th ed.). Sage.

focus-group online-focus-group remote-semi-structured-interview mobile-focus-group telephone-assisted-focus-group online-semi-structured-interview


Remote Non-participant Observation

remote unobtrusive observation
virtual non-participant observation
online non-participant observation
digital observer-only observation

Adapted from classical non-participant observation; remote digital application developed through internet research methodology (Hine, Mann, Stewart, and others, 1990s–2000s)

Remote non-participant observation is a qualitative data collection technique in which the researcher observes naturally occurring behavior, interaction, or activity from a distance — via video conferencing platforms, live-streamed sessions, online communities, or recorded media — without joining or influencing the setting. The researcher maintains a purely observer role throughout, recording field notes without actively participating in the observed context.

Observational field notes, video recordings, screen captures, digital interaction logs

Salmons, J. (2015). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage.

Hine, C. (2015). Ethnography for the Internet: Embedded, Embodied and Everyday. Bloomsbury Academic.

non-participant-observation online-non-participant-observation participant-observation remote-participant-observation digital-ethnography structured-observation


Remote Participant Observation

virtual participant observation
online ethnography
digital participant observation
remote ethnographic observation

Christine Hine (virtual ethnography); Robert Kozinets (netnography)

Remote Participant Observation is a qualitative data collection method in which the researcher joins and participates in an online or digitally mediated social setting — such as a video-based community, online forum, virtual world, or remote work environment — to observe and record social interactions, practices, and meanings as they occur naturally, without requiring physical co-presence.

Field notes, video recordings, chat logs, digital artefacts

Hine, C. (2000). Virtual Ethnography. Sage.

Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.

participant-observation online-participant-observation netnography digital-ethnography face-to-face-participant-observation remote-semi-structured-interview


Remote Research Diary

remote reflexive journal
remote diary study
distributed research diary
online researcher diary

Adapted from diary/journal traditions; remote administration formalized by qualitative and health researchers from the 1990s onward

A Remote Research Diary is a qualitative data collection method in which participants record their thoughts, experiences, and reflections in a structured or semi-structured journal over time, submitting entries to the researcher without face-to-face contact. Conducted via email, secure web platforms, or dedicated apps, this approach captures longitudinal, in-situ accounts of lived experience while eliminating geographic barriers and reducing observer effects.

Qualitative longitudinal data collection instrument

Participant-generated written or multimedia entries submitted remotely

Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.

Williamson, K., & Johanson, G. (Eds.). (2018). Research Methods: Information, Systems, and Contexts (2nd ed.). Chandos Publishing.

diary-method research-diary remote-diary-method mobile-experience-sampling online-diary-method longitudinal-research-diary


Remote Semi-structured Interview

Late 1990s–2000s (widespread adoption post-2020)

Adapted from classical semi-structured interviewing (Kvale, 1996); remote delivery formalised in qualitative methods literature from the late 1990s onward

A remote semi-structured interview is a qualitative data collection method in which a researcher conducts a guided, flexible conversation with a participant over a distance-bridging medium — telephone, video conferencing, or voice-over-IP — using a prepared topic guide with open-ended questions while allowing natural conversational elaboration. It combines the structure and comparability of a protocol-driven approach with the depth and flexibility characteristic of qualitative inquiry, delivered without physical co-presence.

Verbal/text data (audio or video recording, transcripts)

Kvale, S. (1996). InterViews: An Introduction to Qualitative Research Interviewing. Sage.


Remote Sensing Classification

land cover classification
image classification
satellite image classification
spectral classification

Swain & Davis (1978); Lillesand & Kiefer (classical textbook treatments)

Remote sensing classification assigns discrete thematic labels — such as forest, urban, water, or cropland — to pixels in a satellite or aerial image based on their spectral, spatial, and temporal properties. It underpins land-use/land-cover mapping, change detection, environmental monitoring, and disaster response at local to global scales.

Supervised / unsupervised image classification

Multispectral or hyperspectral satellite/aerial imagery (raster)

Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation (7th ed.). Wiley.


Remote Sensor Data Collection

remote sensing data acquisition
wireless sensor data collection
distributed sensor data collection
telemetric data collection

1990s–2000s (proliferated with wireless and IoT technologies)

Multiple contributors; foundational wireless sensor network (WSN) survey by Akyildiz et al.

Remote sensor data collection is the systematic acquisition of measurements from geographically distributed sensing devices without requiring direct human presence at each location. Sensors continuously or periodically record physical, chemical, or biological variables — temperature, pressure, motion, light, GPS coordinates — and transmit readings wirelessly or via network to a central repository for analysis. Widely used in environmental monitoring, precision agriculture, health informatics, and smart infrastructure.

Continuous or event-triggered numeric/time-series readings from sensors

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

sensor-data-collection api-based-data-collection mobile-sensor-data-collection longitudinal-sensor-data-collection web-scraping mobile-experience-sampling


Remote Survey

distance survey
self-administered remote questionnaire
remote questionnaire
distributed survey

1970s–present (formalised by Dillman 1978; expanded to internet surveys 2000s)

Don A. Dillman (Tailored Design Method for mail/remote surveys)

A remote survey is a structured data collection method in which respondents complete a questionnaire without the researcher being physically present. Delivered via mail, telephone, email, web platforms, or mobile apps, it enables researchers to reach geographically dispersed samples at relatively low cost. The method is central to social-science, public-health, and organisational research and is codified in Dillman's widely used Tailored Design Method.

Quantitative / mixed-methods data collection technique

Structured questionnaire responses (Likert, closed, open-ended items)

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.

Tourangeau, R., Conrad, F. G., & Couper, M. P. (2013). The Science of Web Surveys. Oxford University Press.

online-survey telephone-assisted-survey mobile-survey structured-interview longitudinal-survey delphi-technique


Remote Web Scraping

Remote Web Scraping for Research Data Collection

cloud web scraping
server-side scraping
remote automated data extraction
distributed web scraping

Distributed computing and web automation communities

Remote web scraping is a data collection approach in which automated scripts or bots harvest publicly accessible web content — text, tables, metadata, or links — running on remote servers or cloud infrastructure rather than on the researcher's local machine. This separation allows continuous, large-scale, or geographically distributed crawling that local setups cannot sustain, making it particularly suited to longitudinal or high-volume data collection tasks.

Automated remote data collection technique

Structured and semi-structured web content (HTML, JSON, XML)

Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media.

web-scraping api-based-data-collection remote-api-based-data-collection online-web-scraping mobile-web-scraping sensor-data-collection


Renormalization Group Equations

RGE
running couplings
beta function evolution

Renormalization Group Equations (RGEs) describe how the coupling constants and masses of a quantum field theory evolve with energy scale. They are fundamental tools for understanding the scale dependence of physics, predicting the behavior of coupling strengths at different energies, and connecting high-energy physics to low-energy precision measurements.

Callan, C. G. (1970). Broken scale invariance in scalar field theory. Physical Review D, 2(6), 1541.

Gross, D. J., & Wilczek, F. (1973). Ultraviolet behavior of non-abelian gauge theories. Physical Review Letters, 30(26), 1343.

Cheng, T. P., & Li, L. F. (2005). Gauge Theory of Elementary Particle Physics. Oxford University Press.


Repeated-measures ANOVA

within-subjects ANOVA
repeated measures analysis of variance
rm-ANOVA
Tekrarlı Ölçüm ANOVA

Girden (textbook treatment); Field (2013)

Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013).

Parametric within-subjects mean comparison

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE.

Girden, E. R. (1992). ANOVA: Repeated Measures. SAGE.

one-way-anova paired-t-test friedman-test linear-mixed-model two-way-anova


Repertory Grid

The Repertory Grid is a qualitative-quantitative method derived from Personal Construct Theory that elicits how individuals construe (interpret and evaluate) a domain of interest—people, concepts, events, or objects—through their own idiosyncratic dimensions or 'constructs.' Introduced by George Kelly in 1955, the method generates a grid of elements (e.g., people) rated along personally meaningful bipolar constructs, revealing cognitive structures, values, and reasoning patterns without imposing researcher-defined categories.

Kelly, G. A. (1955). The psychology of personal constructs. Norton.

Fransella, F., Bell, R., & Bannister, D. (2004). A manual for repertory grid technique (2nd ed.). Wiley.

Grice, J. W. (2002). Idiogrid: Software for the management and analysis of repertory grids. Behavior Research Methods, Instruments, and Computers, 34(3), 338-341.

qualitative-analysis phenomenological-methods semi-structured-interview personal-construct-theory


Representational Similarity Analysis

RSA
representational geometry
similarity structure analysis

Representational Similarity Analysis (RSA) is a framework for comparing representational geometry across brain regions, computational models, and behavioral measures. Introduced by Kriegeskorte and colleagues in 2008, RSA measures how similarly a brain region represents different stimuli or concepts by examining pairwise similarity structure rather than absolute activity patterns.

Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.

Nili, H., Wingfield, C., Walther, A., et al. (2014). Inferring population attitude towards candidates from social media and electoral history. PLOS ONE, 9(5), e95809.


Reproduction Number

Basic and Effective Reproduction Number (R0, Rt)

Basic Reproduction Ratio
Effective Reproduction Number
Net Reproduction Number
Temel Üreme Sayısı

The basic reproduction number R0 is the expected number of secondary infections produced by a single infectious individual introduced into a fully susceptible population. Formally defined and computationally grounded by Diekmann, Heesterbeek, and Metz in 1990 using the next-generation matrix approach, R0 serves as the central threshold parameter in mathematical epidemiology: if R0 > 1, an epidemic can establish itself; if R0 < 1, the outbreak dies out. The effective reproduction number Rt extends this to partially immune or partially susceptible populations over time.

R0 = 1 (epidemic vs. extinction boundary)

Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990). On the definition and the computation of the basic reproduction ratio R0. Journal of Mathematical Biology, 28(4), 365–382.


Rescorla-Wagner Model

Rescorla-Wagner Model of Associative Learning

Rescorla-Wagner Theory
Delta Rule
Error-Correction Learning

The Rescorla-Wagner Model is a quantitative theory of associative learning that predicts how organisms learn associations between stimuli (e.g., tone and shock in fear conditioning). The model proposes that learning is driven by prediction error—the difference between what is expected to occur and what actually occurs. When prediction error is large, learning is rapid; when prediction error is small, learning slows. The model captures asymptotic learning curves, blocking effects, and stimulus interactions, providing a principled framework for understanding learning dynamics.

Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II (pp. 64-99). Appleton-Century-Crofts.

Simonetta, S. H., Schaafsma, S. M., & Meffert, H. (2010). The Rescorla-Wagner model of Pavlovian conditioning: Some current issues and applications. Neuroscience & Biobehavioral Reviews, 34(6), 821-835.

Gluck, M. A., & Myers, C. E. (1993). Hippocampal mediation of stimulus representation: A computational theory. Hippocampus, 3(4), 491-516.

classical-conditioning associative-learning error-prediction learning-models


Research Design Types

Classification and Selection of Research Design Types

research designs
experimental and observational designs

Donald T. Campbell and Julian Stanley (1963); William Shadish, Thomas Cook, & Donald Campbell (2002)

Research design is the overall structure and strategy of a study, encompassing decisions about how to collect, organize, and analyze data to answer research questions. Major design types include experimental (randomized controlled trials), quasi-experimental (non-random assignment), observational (no manipulation), and qualitative (exploratory, interpretive). Donald T. Campbell and Julian Stanley's 1963 seminal work established systematic terminology for internal validity threats in each design type. Modern classifications (Campbell et al., 2002; Creswell & Plano Clark, 2011) also include mixed-methods designs combining quantitative and qualitative elements.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.

Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). SAGE Publications.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.


Research Diary

researcher diary
field diary
research journal
reflexive diary

1981 (methodological codification); diary use in research dates to 19th-century anthropology

Robert G. Burgess (systematic methodological treatment)

A research diary is a systematic, dated log maintained by the researcher throughout a study to record methodological decisions, emergent observations, analytical hunches, and reflections on researcher positionality. Unlike a participant diary, it is authored by the researcher and functions simultaneously as a data source, an audit trail, and a reflexivity instrument.

Qualitative data collection and reflexivity tool

Burgess, R. G. (1981). Keeping a research diary. Cambridge Journal of Education, 11(1), 75–83.

Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.

diary-method field-notes participant-observation ethnography reflexive-thematic-analysis narrative-analysis


Research Front Identification

Research Front Identification and Emerging Trend Detection

emerging research detection
research frontier mapping
hot topic identification
emerging field analysis

Research front identification is a bibliometric method for detecting emerging or cutting-edge research areas within a larger research landscape. A 'research front' is a cluster of recently published, highly-cited papers that define the current active research direction in a field. Unlike established research communities (identifiable through co-citation networks and slow-changing patterns), research fronts are characterized by rapid growth, high citation velocity (papers accumulating citations quickly), and weak historical ties to established literature. Developed systematically by Chen and others in the 1990s–2000s, research front identification enables researchers, funders, and policy makers to track where scientific activity is concentrating and where breakthrough research is emerging.

Chen, C., & Paul, R. J. (1997). Visualizing a knowledge domain's intellectual structure. IEEE Computer, 30(3), 65–71.

Chen, C., Ciliberto, G., & Chen, Y. (2009). Detecting science hot topics by aggregating publication metadata. Journal of Informetrics, 3(2), 74–89.

Small, H., Boyack, K. W., & Klavans, R. (2005). Identifying emerging topics in science and technology. Research Policy, 43(8), 1232–1241.

science-mapping co-citation-analysis keyword-co-occurrence vosviewer-citespace research-front-identification


Research Integrity Principles

Core Principles and Standards for Research Integrity and Responsible Conduct

Responsible Conduct of Research
RCR
Research Ethics Standards

Multiple (National Academies, NIH/ORI, ESOMAR, individual discipline standards)

Research integrity encompasses the ethical and professional standards that guide responsible conduct in all aspects of research—from study design and data collection through analysis, reporting, and publication. The core principles—honesty, transparency, accountability, respect, and stewardship—ensure that research is trustworthy, reproducible, and contributes legitimate knowledge. These principles are universal across disciplines and are enforced through institutional policies, professional standards, and regulatory oversight. Violations of research integrity undermine scientific credibility and can harm subjects, institutions, and public trust.

National Academies of Sciences, Engineering, and Medicine. (2017). Fostering Integrity in Research. The National Academies Press.

U.S. Office of Research Integrity. (2007). Introduction to RCR: Responsible Conduct of Research. NIH Training Module.

ESOMAR. (2016). International Code on Market, Opinion and Social Research and Data Analytics. Ethical standards for research professionals.

research-misconduct conflict-of-interest-research belmont-report data-fabrication-falsification


Research Misconduct

Definition, Investigation, and Consequences of Research Misconduct

FFP
Research Fraud
Scientific Misconduct

U.S. Office of Research Integrity (ORI) / National Science Foundation; International standards via COPE

Research misconduct comprises intentional or reckless fabrication, falsification, or plagiarism in proposing, conducting, or reporting research. Formally defined by U.S. federal policy (42 CFR Part 93, Office of Research Integrity), misconduct is distinguished from honest error, negligence, and good-faith disagreements about research methods or interpretation. Misconduct undermines scientific integrity, harms subjects and institutions, wastes research resources, and erodes public trust in science. Allegations are investigated formally with due process; proven misconduct results in sanctions ranging from publication correction to career-ending bans.

U.S. Office of Research Integrity. (2005). Public Health Service Policy on Research Misconduct. 42 CFR Part 93. Federal Register.

National Science Foundation. (2007). NSF Research Misconduct Policy. PAPPG Significant Changes.

Retraction Watch. (2023). Anatomy of a Retraction: What Can We Learn From Retractions? Retractions database and analysis.


Research Question Formulation

Research Question Formulation and Development

Kerlinger, Campbell, & Fisher (1950s–1990s research methodology literature)

Research question formulation is the process of defining clear, focused, and answerable questions that guide a research study. A well-formulated research question specifies what a researcher seeks to investigate, distinguishing between independent and dependent variables (or phenomena), and sets the scope for literature review, methodological design, and data collection. Established in behavioral research literature in the mid-20th century, this framework remains foundational because it transforms vague research interests into testable, empirically grounded inquiries.

Kerlinger, F. N., & Lee, H. B. (1999). Foundations of Behavioral Research (4th ed.). Wadsworth.

Morse, J. M. (1991). Approaches to qualitative-quantitative methodological triangulation. Nursing Research, 40(2), 120–123.

Battiam, R., & Sarkar, U. (2016). Crafting research questions for healthcare research. Health Affairs, 35(8), 1413–1418.

hypothesis-development research-design-types pico-framework literature-search-strategy


Connor-Davidson Resilience Scale

CD-RISC
Connor-Davidson Scale
Resilience Scale

The Connor-Davidson Resilience Scale (CD-RISC) is a 25-item self-report measure of psychological resilience—the capacity to cope with stress, adversity, and trauma while maintaining psychological functioning. Developed by Kathryn Connor and Jonathan Davidson in 2003, the CD-RISC operationalizes resilience as a multidimensional construct encompassing personal competence, trust in instincts, positive adaptation, and meaning-making. A brief 10-item version (CD-RISC-10) is also widely available. The scale has become standard in clinical research on trauma, anxiety, depression, and recovery from adversity.

Psychological resilience and stress coping measure

Connor, K. M., & Davidson, J. R. (2003). Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depression and Anxiety, 18(2), 76–82.

Campbell-Sills, L., & Stein, M. B. (2007). Psychometric analysis and refinement of the Connor-Davidson Resilience Scale (CD-RISC): Validation of a 10-item measure of resilience. Journal of Traumatic Stress, 20(6), 1019–1028.

Notario-Pacheco, B., Solera-Martínez, M., Serrano-Parra, M. D., Bartolomé-Gutiérrez, R., García-Campayo, J., & Martínez-Vizcaíno, V. (2014). Reliability and validity of the Spanish version of the 10-item Connor-Davidson Resilience Scale in young adults. Health and Quality of Life Outcomes, 12(1), 165.


ResNet

ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision.

Deep Convolutional Neural Network with skip connections

Image classification, object detection, segmentation, feature extraction

Residual (skip) connection: H(x) = F(x) + x

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv:1512.03385.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press.

vgg-network densenet inception-network efficientnet alexnet convolutional-neural-network


ResNeXt

ResNeXt: Aggregated Residual Transformations for Deep Neural Networks

ResNeXt
Aggregated Residual Transformations
grouped convolution residual network
cardinality-based ResNet

Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.

ResNeXt is a deep convolutional neural network architecture introduced by Xie, Girshick, Dollár, Tu, and He at CVPR 2017. It extends the residual network (ResNet) design by introducing a new architectural dimension called cardinality — the number of independent, parallel transformation paths within each residual block — enabling higher accuracy with fewer parameters and a simpler, more uniform design than its predecessors.

Convolutional neural network with grouped/cardinality-based residual blocks

Image classification, object detection, feature extraction

Cardinality (number of parallel transformation paths)

Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5987–5995.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

resnet vgg inception densenet efficientnet mobilenet se-net


Respiratory Exchange Ratio

Respiratory Exchange Ratio and Substrate Utilization Assessment

RER
respiratory quotient
RQ
substrate oxidation ratio

The respiratory exchange ratio (RER), also called the respiratory quotient (RQ), is the ratio of carbon dioxide produced to oxygen consumed during metabolism. Introduced by J. B. Weir (1949), RER is a non-invasive indirect measure of substrate utilization—indicating whether the body is primarily oxidizing carbohydrate, fat, or protein. RER values range from approximately 0.7 (pure fat oxidation) to 1.0 (pure carbohydrate oxidation) and higher under anaerobic conditions. By measuring exhaled and inhaled gases during exercise, RER reveals which fuel source predominates at different intensities, providing insights into metabolic flexibility and exercise physiology.

Weir, J. B. (1949). New methods for calculating metabolic rate with special reference to protein metabolism. Journal of Physiology, 109(1-2), 1-9.

Frayn, K. N. (1983). Calculation of substrate oxidation rates in vivo from gaseous exchange. Journal of Applied Physiology, 55(2), 628-634.

Jeukendrup, A. E., & Wallis, G. A. (2005). Measurement of substrate oxidation during exercise by means of gas exchange measurements. International Journal of Sports Medicine, 26(1), S28-S37.

vo2-max lactate-threshold epoc critical-power banister-trimp


Respondent-Driven Sampling

Chain-Referral Sampling
Peer-Referral Sampling
Network-Based Sampling
Katılımcı Güdümlü Örnekleme

Respondent-Driven Sampling (RDS) is a probabilistic chain-referral method designed to reach hidden or hard-to-reach populations that lack a sampling frame. Introduced by sociologist Douglas Heckathorn in 1997, RDS combines snowball recruitment with mathematical weighting based on participants' personal network sizes, allowing researchers to generate population-level estimates even when no complete membership list exists.

Probabilistic chain-referral sampling design

Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199.


Response Spectrum Analysis

Response Spectrum Analysis for Earthquake Design

Elastic response spectrum
Design spectrum method
Modal response spectrum

Response spectrum analysis is a linear modal method for estimating earthquake-induced forces and displacements in structures. Introduced by Housner in 1941, this approach uses design spectra that represent the maximum response of single-degree-of-freedom oscillators at different natural frequencies to compute the structural response by combining modal contributions.

Linear modal analysis for earthquake response

Housner, G. W. (1941). Calculating the response of an oscillator to arbitrary ground motion. Bulletin of the Seismological Society of America, 32(2), 143-149.

Newmark, N. M., & Hall, W. J. (1969). Seismic design criteria for nuclear reactor facilities. Building Science Series, National Bureau of Standards, Report NSB-46.

ASCE/SEI (2010). Minimum Design Loads for Buildings and Other Structures (ASCE/SEI 7-10). American Society of Civil Engineers.

pushover-analysis equivalent-static-analysis nonlinear-time-history-analysis incremental-dynamic-analysis


Response Surface Desirability Function

Response Surface Methodology with Desirability Function Optimization

RSM
Desirability function
Multi-response optimization

Response Surface Methodology (RSM) is a set of statistical and mathematical techniques for modeling and optimizing processes with multiple inputs (factors) and outputs (responses). The Desirability Function approach, introduced by Harrington (1965) and refined by Derringer and Suich (1980), extends RSM to solve multi-response optimization problems by combining competing objectives into a single index. This methodology is essential in product and process development where engineers must balance performance, cost, and reliability.

Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, 13(1), 1-45.

Harrington, E. C. (1965). The desirability function. Journal of Quality Technology, 4(6), 494-509.

Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214-219.

Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley.

rainflow-counting first-order-reliability-method highly-accelerated-life-testing topology-optimization


Response Surface Methodology

Yanıt Yüzeyi Yöntemi (RSM — CCD, Box-Behnken)

Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.

Second-order polynomial response surface model

Central Composite Design (CCD), Box-Behnken Design (BBD)

Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.

Myers, R. H., Montgomery, D. C. & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.

factorial-design fractional-factorial one-way-anova two-way-anova multiple-linear-regression taguchi-methods latin-square-design


IRLS

International Restless Legs Syndrome Rating Scale

IRLS
Restless Legs Syndrome Rating Scale

RLS symptom assessment; disease-specific rating

The IRLS is a 10-item clinician-administered rating scale designed to assess the severity of symptoms in patients with restless legs syndrome (RLS). Developed and validated by Walters and colleagues in 2003 for the International Restless Legs Syndrome Study Group, it is the most widely used disease-specific severity measure for RLS in clinical practice and research. The scale captures the cardinal features of RLS—nocturnal leg discomfort, urge to move, sleep disruption, and functional impairment—across multiple dimensions.

Walters, A. S., LeBrocq, C., Dhar, A., et al. (2003). Validation of the International Restless Legs Syndrome Study Group rating scale for restless legs syndrome. Sleep Medicine, 4(2), 121-132.


Restricted Boltzmann Machine

Restricted Boltzmann Machine (RBM) — Bipartite Generative Energy Model

RBM
Harmonium
restricted Boltzmann machine
RBM generative model
stochastic recurrent neural network

Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006)

A Restricted Boltzmann Machine is a two-layer generative probabilistic model consisting of visible (observed) and hidden (latent) binary units connected by an undirected bipartite graph with no within-layer connections. Originally introduced as the 'Harmonium' by Paul Smolensky in 1986 and powerfully revived by Geoffrey Hinton and Ruslan Salakhutdinov in their landmark 2006 Science paper, RBMs became historically pivotal as the building block for greedy layer-wise pre-training of Deep Belief Networks, restarting interest in deep neural networks after years of stagnation.

Generative energy-based probabilistic model

Unsupervised representation learning, dimensionality reduction, generative modelling, pre-training

Two layers: one visible (observed), one hidden (latent)

Bipartite undirected graph; no within-layer connections

Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.

Hinton, G. E. (2002). Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14(8), 1771–1800.

Smolensky, P. (1986). Information Processing in Dynamical Systems: Foundations of Harmony Theory. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel Distributed Processing, Vol. 1 (pp. 194–281). MIT Press.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 20). MIT Press.

deep-belief-network boltzmann-machine variational-autoencoder autoencoder principal-component-analysis


Retail Service Quality Scale

Retail Service Quality
In-Store Service Quality Scale

Pratibha A. Dabholkar, Dayle I. Thorpe, Joseph O. Rentz

The Retail Service Quality Scale (RetSQ) is a 17-item instrument developed by Dabholkar, Thorpe, and Rentz (1996) to measure customer perceptions of service quality in retail store environments. Adapted from SERVQUAL but customized for the unique context of in-store shopping, RetSQ measures five dimensions: Physical Aspects (store appearance, cleanliness, merchandise display), Reliability (accurate pricing, reliable operations), Personal Interaction (staff helpfulness, courtesy), Problem Solving (handling complaints, responding to customer needs), and Policies (convenience, fairness of return policies). The scale captures both the tangible environment and interpersonal service elements critical to retail success.

Multi-dimensional retail store service quality scale

Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1996). A Measure of Service Quality for Retail Stores: Scale Development and Validation. Journal of the Academy of Marketing Science, 24(1), 3-16.

Gagliano, K. B., & Spivey, M. (2003). Differentiation: The Key to Market Success in Grocery Retailing. Journal of Business and Industrial Marketing, 18(4-5), 340-354.

servqual servperf customer-satisfaction-index consumer-involvement-scale


Article Retraction Process

Article Retraction and Correction in Academic Publishing

Retraction Notice
Paper Retraction
Correction Notice

Committee on Publication Ethics (COPE); Retraction Watch initiative

An article retraction is the invalidation of a published article due to serious flaws (data fraud, major methodological errors, ethical violations) that undermine its conclusions. Retractions are distinct from corrections (which address minor errors) and are initiated by authors, editors, or institutions when integrity is compromised. The first modern retraction was published in 1948. COPE published formal Retraction Guidelines in 2009 (updated 2019) that specify when retraction is appropriate, how it is conducted, and how retraction notices are recorded. Retracted articles remain in the literature with a visible 'RETRACTED' watermark, preserving the scientific record and warning readers.

Committee on Publication Ethics (2019). Retraction Guidelines. COPE.

Garfield, E., & Welljams-Dorof, A. (1990). Citation Data: Its Use as a Science Indicator for Measuring and Evaluating the Activity of Scientists. Science and Public Policy, 17(5), 359–375.

Retraction Watch (2023). Database of Retracted Publications. Ivan Oransky & Adam Marcus.

plagiarism-in-research duplicate-publication peer-review-process cope-guidelines


Retrieval-Augmented Generation

RAG
retrieval-augmented LLM
grounded generation
Erişim Destekli Metin Üretimi (RAG)

Lewis, Patrick et al. (Meta AI / Facebook AI Research)

Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model.

Vector knowledge base required; LLM access required; embedding model must be selected

Output: Grounded natural-language response citing retrieved passages

Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.

Gao, Y. et al. (2023). Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv preprint.

bert-embeddings text-summarization question-answering knowledge-graph-nlp transformer-nlp bert-finetuning self-attention-transformer


Retrospective case-control study

case-control study
retrospective case-referent study
case-referent design
trohoc study

Jerome Cornfield; formalized by Brian MacMahon and others in mid-20th-century epidemiology

A retrospective case-control study identifies individuals who already have an outcome of interest (cases) and a comparable group without it (controls), then looks backward in time using existing records to determine prior exposure to a suspected risk factor. The primary measure of association is the odds ratio. This design is especially efficient for studying rare diseases or outcomes with long latency periods, since the outcome has already occurred before the study begins.

Existing records, patient charts, registries, recalled exposures

Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.

Cornfield, J. (1951). A method of estimating comparative rates from clinical data: Applications to cancer of the lung, breast, and cervix. Journal of the National Cancer Institute, 11(6), 1269–1275.

case-control-study nested-case-control cohort-study retrospective-cohort-study case-crossover-design odds-ratio-analysis


Retrospective Case Report

retrospective case study
post-hoc case report
retrospective clinical case
case report

19th century (formalized ~2013 with CARE guidelines)

Case reporting tradition in medicine (formalized by CARE guidelines, Riley et al., 2013)

A retrospective case report is a detailed, structured narrative of a single patient's clinical presentation, diagnosis, management, and outcome, assembled from existing medical records after the clinical events have occurred. It is the most granular and accessible observational design in clinical medicine, serving primarily to document rare presentations, unexpected outcomes, novel treatments, or unusual drug reactions that would not otherwise enter the published literature.

Medical records, clinical notes, imaging, laboratory data (retrospectively collected)

Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Sox, H., & Riley, D. (2013). The CARE guidelines: consensus-based clinical case reporting guideline development. Journal of Medical Case Reports, 7(1), 223.

Vandenbroucke, J. P. (2001). In defense of case reports and case series. Annals of Internal Medicine, 134(4), 330-334.

case-series case-report retrospective-case-series retrospective-cohort-study prospective-case-report case-control-study


Retrospective Case Series

retrospective case series
chart review case series
historical case series
medical records case series

Long-standing practice; codified in EBM frameworks during 1990s–2000s

Clinical medicine tradition (no single originator); formalized in evidence-based medicine literature

A retrospective case series is an observational study that systematically describes the clinical features, treatments, and outcomes of a defined group of patients by examining pre-existing medical records or administrative data. It looks backward in time — data have already been recorded before the study begins. With no control group, no randomization, and no prospective follow-up, it sits near the base of the evidence hierarchy but remains one of the most practical and frequently published study designs in clinical medicine.

Pre-existing medical records, clinical charts, administrative databases

Kooistra, B., Dijkman, B., Einhorn, T. A., & Bhandari, M. (2009). How to design a good case series. Journal of Bone and Joint Surgery, 91(Suppl 3), 21–26.

Mayer, D. (2010). Essential Evidence-Based Medicine (2nd ed.). Cambridge University Press. Chapter on study designs.

case-series prospective-case-series retrospective-cohort-study case-report retrospective-case-control-study chart-review


Retrospective Cohort Study

historical cohort study
non-concurrent cohort study
retrospective follow-up study
historical prospective study

Mid-20th century (widely formalized 1950s–1970s)

Systematic use attributed to early 20th-century occupational epidemiology; formalized in modern epidemiological theory by Brian MacMahon and others

A retrospective cohort study assembles a group of individuals who share a common starting point and reconstructs their exposure history and subsequent outcomes entirely from pre-existing records. Because the data have already been collected before the study begins, the design is far faster and cheaper than a prospective cohort; however, the researcher must work with whatever information was recorded at the time rather than collecting purpose-built measurements.

Existing records, administrative databases, medical charts, registries

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.

cohort-study prospective-cohort-study case-control-study nested-case-control cross-sectional-epidemiological-study survival-analysis


Retrospective competing risks analysis

competing risks survival analysis (retrospective)

cause-specific hazard analysis (retrospective)

subdistribution hazard analysis (retrospective)

1978 (cause-specific); 1999 (subdistribution/Fine-Gray)

Fine & Gray (subdistribution model); Prentice et al. (cause-specific framework)

Retrospective competing risks analysis applies competing risks methodology to historical (already-collected) time-to-event data in which subjects can experience one of several mutually exclusive endpoints. It uses the cumulative incidence function and cause-specific or subdistribution hazard models to estimate the probability of each event type while accounting for the fact that occurrence of one event permanently precludes the others. Widely used in oncology, cardiology, and transplant medicine where administrative or registry records are the data source.

Retrospective observational survival analysis

Time-to-event data with multiple mutually exclusive endpoints from historical records

Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.

Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V., Flournoy, N., Farewell, V. T., & Breslow, N. E. (1978). The analysis of failure time data in the presence of competing risks. Biometrics, 34(4), 541–554.

competing-risks-analysis kaplan-meier-analysis cox-proportional-hazards retrospective-survival-analysis retrospective-cohort-study cause-specific-hazard-model


Retrospective Cox proportional hazards

Retrospective Cox Proportional Hazards Regression

Cox PH regression (retrospective)
retrospective Cox survival model
retrospective hazard regression
Cox model on historical data

Retrospective Cox proportional hazards regression applies Cox's (1972) semi-parametric survival model to time-to-event data extracted from existing records — medical charts, administrative databases, registries, or biobanks. It estimates covariate-adjusted hazard ratios (HRs) without specifying the underlying baseline hazard, making it the dominant analytic tool when the investigator works backward from already-recorded outcomes and exposures.

Time-to-event data with covariates, drawn from retrospective records

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.

Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.

cox-proportional-hazards survival-analysis kaplan-meier-analysis retrospective-cohort-study competing-risks-analysis retrospective-survival-analysis


Retrospective cross-sectional epidemiological study

Retrospective Cross-Sectional Epidemiological Study

retrospective cross-sectional survey
record-based cross-sectional study
retrospective prevalence study
secondary-data cross-sectional study

Epidemiology tradition (formalized in mid-20th century; Rothman, Greenland and others)

A retrospective cross-sectional epidemiological study measures the prevalence of exposures and outcomes at a single analytical time point using data that were originally recorded in the past — such as medical records, administrative databases, or disease registries. It combines the snapshot logic of a cross-sectional design with the efficiency of retrospective data access, making it a practical choice when prospective data collection is unfeasible or when large existing datasets are available.

Existing records, administrative databases, registries, medical charts, survey archives


Retrospective diagnostic accuracy study

retrospective DAS
retrospective test accuracy study
retrospective index test evaluation
historical diagnostic accuracy study

Formalized 2000s; STARD 2003, revised 2015

Formalized through the STARD initiative led by Patrick Bossuyt and colleagues

A retrospective diagnostic accuracy study evaluates how well a diagnostic test (the index test) correctly identifies a target condition by applying it to previously collected data or archived specimens alongside a reference standard. Because both index test results and reference standard results are drawn from existing records or stored material rather than generated prospectively, this design is faster and less costly than a prospective counterpart — but carries specific methodological risks that must be controlled to produce valid estimates of sensitivity, specificity, and related measures.

Observational, retrospective study design

Previously collected biological specimens, medical records, imaging data, or stored samples with known reference standard results

Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., et al. (2015). STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. BMJ, 351, h5527.

Whiting, P. F., Rutjes, A. W., Westwood, M. E., et al. (2011). QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Annals of Internal Medicine, 155(8), 529–536.

diagnostic-accuracy-study prospective-diagnostic-accuracy-study case-control-study retrospective-cohort-study screening-test-evaluation systematic-review


Retrospective Ecological Study

Retrospective Ecological Epidemiological Study

retrospective correlational ecological design

Epidemiological tradition; formalized by Morgenstern and others

A retrospective ecological study examines associations between exposures and outcomes using pre-existing aggregate data from defined populations or geographic units. Rather than following individual subjects, the unit of analysis is a group — a country, region, or time period — and all measurements come from historical records already collected before the study began. It is a rapid, low-cost way to generate hypotheses about environmental, social, or policy determinants of disease at the population level.

Aggregate/group-level historical data (routinely collected statistics, registries, census data)

Morgenstern, H. (1998). Ecologic studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven.


Retrospective Ethics Approval

Obtaining Ethics Committee Approval After Data Has Been Collected

retroactive approval
post-hoc ethics approval
IRB exemption determination
delayed ethics review

U.S. Department of Health and Human Services; International research ethics community

Retrospective ethics approval is the ethics committee's review and determination regarding research conducted or data collected before ethics approval was obtained. This situation arises when researchers collect data without advance ethics review (intentionally, out of oversight, or due to institutional gaps) and then seek ethics approval before analysis or publication. Retrospective approval is generally disfavored; regulations and guidelines strongly recommend prospective review (approval before data collection). However, retrospective determination of exemption (finding that data already collected meets exempt criteria under 45 CFR 46.104, similar frameworks in other jurisdictions) or retrospective approval with specific justifications may be possible. Understanding when retrospective approval can be obtained—and its limitations—is important for researchers facing this ethical and regulatory challenge.

U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Section 46.101(b).

U.S. Department of Health and Human Services, Office for Human Research Protections. (2010). Retrospective Collection and Codification of Existing Data and Specimens. OHRP Guidance.

Health Research Authority. (2021). Research That Has Already Started or Data Already Collected. UK Research Ethics Service Guidance.

International Council for Harmonisation. (2016). ICH Harmonised Guideline: Integrated Addendum to ICH E6(R1). Good Clinical Practice E6(R2).

ethics-committee-application ethics-committee-types waiver-of-informed-consent data-protection-research risk-benefit-assessment


Retrospective Kaplan-Meier Analysis

Retrospective Kaplan-Meier Survival Analysis

retrospective KM analysis
retrospective survival curve estimation
historical Kaplan-Meier
retrospective KM estimator

1958 (method); retrospective application standard in clinical research since 1970s–1980s)

Retrospective Kaplan-Meier analysis applies the Kaplan-Meier product-limit estimator to time-to-event data drawn from existing records — medical charts, registries, or administrative databases — rather than from a prospectively followed cohort. The method estimates the probability of surviving (or remaining event-free) beyond any given time point while accounting for participants whose follow-up ended before the event occurred (censored observations). It is among the most commonly reported analyses in clinical oncology, cardiology, and surgery.

Non-parametric survival analysis applied to historical data

Time-to-event data with censoring, collected retrospectively from records or registries

Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.

Clark, T. G., Bradburn, M. J., Love, S. B., & Altman, D. G. (2003). Survival analysis part I: Basic concepts and first analyses. British Journal of Cancer, 89(2), 232–238.

kaplan-meier-analysis cox-proportional-hazards retrospective-survival-analysis competing-risks-analysis retrospective-cohort-study log-rank-test


Retrospective nested case-control

nested case-control within retrospective cohort

case-control nested in historical cohort

1973 (formal description); widely adopted in epidemiology from 1980s onward

Nested case-control formalized by Mantel (1973); retrospective application via historical cohort records

A retrospective nested case-control study is an efficient observational design in which cases and matched controls are sampled from within an already-assembled retrospective cohort. Exposure data are retrieved from historical records only for selected participants, dramatically reducing data-collection costs while retaining most of the analytic power of the full cohort. It is widely used in pharmacoepidemiology, occupational health, and disease-registry research.

Existing retrospective cohort records, medical records, administrative databases, biobanks

Mantel, N. (1973). Synthetic retrospective studies and related topics. Biometrics, 29(3), 479–486.

nested-case-control retrospective-cohort-study case-control-study cohort-study case-cohort-study retrospective-case-control-study


Retrospective phase II clinical trial

retrospective Phase II study
historical Phase II analysis
retrospective efficacy study
Phase II retrospective analysis

1980s–1990s (with growth in oncology retrospective analyses)

Adapted from standard Phase II trial methodology; retrospective variant formalized in oncology practice

A retrospective Phase II clinical trial evaluates a treatment's preliminary efficacy and safety signals using existing archival data — medical records, registries, or electronic health records — rather than prospectively enrolling new patients. It mirrors the objectives of a standard Phase II trial (estimating response rate, tolerability, and early efficacy) but does so by looking backward at patients who have already received the intervention, making it faster and less costly than a prospective design.

Archival clinical records, medical charts, registries, electronic health records

Simon, R. (1989). Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10(1), 1–10.

phase-ii-clinical-trial retrospective-cohort-study prospective-phase-ii-clinical-trial phase-i-clinical-trial phase-iii-clinical-trial diagnostic-accuracy-study


Retrospective phase III clinical trial

retrospective comparative efficacy trial

Late 20th century (ICH E8 1997; widespread retrospective Phase III use from 1990s onward)

Regulatory framework codified by ICH E8/E9 (1997–1998); retrospective application developed through post-marketing and registry practice

A retrospective Phase III clinical trial evaluates the comparative efficacy and safety of an intervention against a control using data that were collected before the study was designed. Rather than enrolling new patients prospectively, researchers analyze existing records — from registries, hospital databases, or historical trial archives — to address a Phase III-level question: does Treatment A outperform the current standard of care in a large, representative patient population? This design is used when prospective enrollment is infeasible, unethical, or when historical data are sufficiently complete to support a rigorous comparison.

Retrospective comparative clinical study

Existing medical records, registries, electronic health records, historical trial databases

Friedman, L. M., Furberg, C. D., & DeMets, D. L. (2010). Fundamentals of Clinical Trials (4th ed.). Springer.

International Conference on Harmonisation. (1998). ICH E9: Statistical Principles for Clinical Trials. Federal Register, 63(179), 49583–49598.

phase-iii-clinical-trial retrospective-cohort-study randomized-clinical-trial phase-ii-clinical-trial prospective-phase-iii-clinical-trial survival-analysis


Retrospective survival analysis

historical survival study
retrospective time-to-event analysis
retrospective follow-up survival study
archival survival analysis

1970s–1980s (retrospective variant established)

Kaplan & Meier (foundational estimator, 1958); Cox (regression model, 1972); retrospective application is a design variant documented since the 1970s

Retrospective survival analysis applies time-to-event statistical methods — most commonly the Kaplan-Meier estimator and Cox proportional hazards regression — to data collected from past records rather than through prospective follow-up. The researcher looks back at medical records, disease registries, or administrative databases to reconstruct each patient's journey from a defined starting point (e.g., diagnosis or surgery) to an outcome of interest (e.g., death, relapse, or hospital readmission), making it a cost-efficient approach for studying prognosis and risk factors when prospective follow-up is not feasible.

Retrospective observational analytical study

Historical time-to-event data (survival times, censoring indicators, covariates) from medical records, registries, or administrative databases

survival-analysis kaplan-meier-analysis cox-proportional-hazards competing-risks-analysis retrospective-cohort-study retrospective-case-control-study


Retrospective Think-Aloud

Delayed Verbalization
Post-task Thinking Aloud
RTA

K. Anders Ericsson, Herbert Simon, adapted by Gary Olson and colleagues

Retrospective Think-Aloud is a variant of the Think-Aloud Protocol in which participants complete a task without verbalization, then immediately review a video or replay of their task performance and narrate their thoughts, reasoning, and reactions. This method captures post-hoc reflection on decision-making and user experience without disrupting task execution. Particularly valuable for exploring user awareness, emotional reactions, and retrospective sense-making, Retrospective Think-Aloud provides the explanatory richness of concurrent thinking aloud without the disruption.

Post-task verbalization method for reflecting on decision-making

Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87(3), 215–251.

Olson, G. M., Olson, J. S., & Van der Veen, M. R. (1990). The structure of activity. In CHI'90 Conference Proceedings (pp. 3–10).

think-aloud-protocol contextual-inquiry pluralistic-walkthrough nasa-tlx


Return-Oriented Programming

ROP
code reuse attack
Turing-complete gadget

Return-Oriented Programming (ROP) is an exploit technique that chains together short sequences of instructions (gadgets) from existing executable code to perform arbitrary computation, bypassing security defenses like code injection prevention. Introduced by Hovav Shacham in 2007, ROP exploits code reuse to execute malicious logic even when data execution prevention (DEP) and code signing prevent direct code injection. ROP is considered one of the most powerful exploit techniques against modern defense mechanisms and has been demonstrated to be Turing-complete.

Shacham, H. (2007). The geometry of innocent flesh on the bone: Return-into-libc without function calls (on the x86). In Proceedings of the 14th ACM Conference on Computer and Communications Security (CCS 2007), pp. 552-561.

Roemer, R., Buchanan, E., Shacham, H., & Savage, S. (2012). Return-oriented programming: Systems, languages, and applications. ACM Transactions on Information and System Security (TISSEC), 15(1), 1-34.


Revised Children's Anxiety and Depression Scale (RCADS)

The RCADS is a 47-item (or 25-item brief version) self-report measure that assesses the full spectrum of anxiety disorders and major depression in children and adolescents ages 6–18 years. Developed by Bruce Chorpita in 2000, it provides six subscale scores aligned with DSM-IV diagnostic criteria: Separation Anxiety, Generalized Anxiety, Panic Disorder, Social Phobia, Obsessive-Compulsive Disorder, and Major Depressive Disorder. The RCADS is designed to be both a screening tool and a diagnostic aid.

Chorpita, B. F., Yim, L., Moffatt, C., Umemoto, A., & Francis, S. E. (2000). Assessment of symptoms of DSM-IV anxiety and depression in children: A revised child anxiety and depression scale. Behaviour Modification, 24(4), 513–537.

Chorpita, B. F., Moffatt, C., & Gray, J. (2005). Psychometric properties of the Revised Child Anxiety and Depression Scale in a clinical sample. Behaviour Research and Therapy, 43(12), 1541–1549.


REVISED-SIMOS

REVISED-SIMOS (Revised Simos Procedure — deck-of-cards rank-based weight elicitation (Figueira & Roy 2002)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Figueira, J., Roy, B. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Subjective (rank-ordered cards with blank cards for gaps)

Figueira, J., Roy, B. (2002). Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure. European Journal of Operational Research

ahpsort aploco aras aroman artasi cobra cocoso codas


Responding to Peer Reviewer Comments

Guidelines for Addressing Peer Review Feedback and Preparing Revision Letters

revision letter
response to reviewers
rebuttal letter

Journal editors and publishing community; formalized by Clydesdale et al. and ICMJE

A response to reviewers (or 'revision letter') is a formal document that authors submit alongside a revised manuscript, addressing each reviewer comment point-by-point. The response letter shows the editor and reviewers that you have carefully considered their feedback, explained changes made in light of their suggestions, and justified any points of disagreement. A thoughtful, respectful response to reviewers significantly increases the likelihood of acceptance; a dismissive or defensive response can lead to rejection despite good science. The response letter is not an argument but a demonstration of engagement, transparency, and scientific integrity.

Clydesdale, G. J., Seymour, K. J., & Toy, M. S. (2013). How to write a response to reviewers. British Journal of Ophthalmology, 97(1), 1–2.

Wager, E., & Wieland, B. (2011). Responsibilities of journal editors. Lancet, 337(9834), 1807–1809.

International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.

journal-submission-process scientific-writing-clarity imrad-structure statistical-reporting-standards


Reynolds-Averaged Navier-Stokes

The Reynolds-Averaged Navier-Stokes (RANS) equations represent a time-averaged form of the Navier-Stokes equations developed by Osborne Reynolds in 1895. This approach decomposes turbulent flow into mean and fluctuating components, enabling practical simulation of turbulent flows by modeling turbulent stresses rather than resolving all scales. RANS remains the most widely used computational fluid dynamics method in engineering applications due to its computational efficiency.

Computational turbulence modeling approach

Reynolds, O. (1895). On the dynamical theory of incompressible viscous fluids and the determination of the criterion. Philosophical Transactions of the Royal Society A, 186, 123-164.

Boussinesq, J. (1877). Essai sur la théorie des eaux courantes. Mémoires présentés par divers savants à l'Académie des Sciences, 23, 1-680.

Wilcox, D. C. (2006). Turbulence Modeling for CFD (3rd ed.). DCW Industries, Inc.

large-eddy-simulation detached-eddy-simulation direct-numerical-simulation boundary-layer-theory lattice-boltzmann-method


Rheometry

Rheometry is the scientific measurement of how fluids and semi-solids (pastes, gels, suspensions) flow and deform under applied stress. Using a rheometer (a precision instrument that applies controlled shear forces and measures the resulting deformation), rheometry characterizes the viscosity, viscoelasticity, and other flow properties of food products, essential for process design, quality control, and predicting mouthfeel sensations.

Steffe, J. F. (1996). Rheological methods in food process engineering (2nd ed.). Freeman Press.

Barnes, H. A. (2000). A handbook of elementary rheology. Institute of Non-Newtonian Fluid Mechanics.


RA-QoL

The RA-QoL is a disease-specific quality of life measure for rheumatoid arthritis (RA). Developed by Stephen McKenna and colleagues in 1997, this 30-item questionnaire quantifies how RA affects daily activities, emotional well-being, functional independence, and social engagement. It is a standard outcome measure in RA research and clinical trials, capturing dimensions beyond joint inflammation and functional disability.

Self-report quality of life questionnaire

de Jong, Z., van der Heijde, D. M., McKenna, S. P., & Whalley, D. (2000). The Rheumatoid Arthritis Quality of Life Scale (RAQoL): Final Dutch version allows three methods of data handling. Arthritis & Rheumatism, 13(6), 408-413.

McKenna, S. P., Doward, L. C., Whalley, D., Tennant, A., Emery, P., & Veale, D. J. (1997). The Rheumatoid Arthritis Quality of Life Scale: Development and preliminary validation. British Journal of Rheumatology, 36(8), 878-883.

Ekdahl, C., Eberhardt, K., Andersson, S. I., & Svensson, B. (1997). Assessing disability in patients with rheumatoid arthritis. Scandinavian Journal of Rheumatology, 26(1), 16-23.


RQLQ

Rhinoconjunctivitis Quality of Life Questionnaire

Elizabeth F. Juniper, McMaster University

The RQLQ is a 28-item disease-specific quality-of-life instrument developed by Juniper and colleagues at McMaster University in 1996 to assess the impact of allergic rhinitis and allergic conjunctivitis on daily functioning. It captures symptom burden and activity limitation across seven domains: sleep, non-nose/eye symptoms, practical problems, nasal symptoms, eye symptoms, activity limitation, and emotional function. The RQLQ is the preferred outcome measure in allergic rhinitis clinical trials and is widely used in allergy and immunology practice to track treatment response.

Juniper, E. F., Guyatt, G. H., Streiner, D. L., & King, D. R. (1996). Clinical validation and responsiveness of three scales developed to measure the effect of allergic rhinitis on quality of life. Journal of Clinical Epidemiology, 49(3), 339-347.

Juniper, E. F. (2000). How important is quality of life in allergic rhinitis? Current Allergy and Asthma Reports, 2(3), 211-214.

sinonasal-outcome-test st-george-respiratory-questionnaire asthma-control-questionnaire breathlessness-cough-sputum-scale mrc-dyspnoea-scale


Rhizosphere Amplicon Analysis

Rhizosphere Amplicon Sequencing and Community Profiling

rhizosphere 16S amplicon sequencing
root-zone microbiome amplicon profiling
rhizosphere metabarcoding
soil microbiome amplicon analysis

Early 2000s–2010s (accelerated with next-generation sequencing platforms)

Rhizosphere Amplicon Analysis is a molecular-ecological pipeline used to characterise the microbial communities inhabiting the root-adjacent soil zone — the rhizosphere — by sequencing targeted marker genes such as the bacterial 16S rRNA gene or the fungal ITS region. Widely applied in agronomy, soil ecology, and plant pathology, it enables researchers to identify which microorganisms are present, how their composition shifts under different crops, treatments, or soil conditions, and how community structure relates to plant health and productivity.

DNA sequences from soil/root-associated microbial communities

Lundberg, D. S., Lebeis, S. L., Paredes, S. H., Yourstone, S., Gehring, J., Malfatti, S., ... & Dangl, J. L. (2012). Defining the core Arabidopsis thaliana root microbiome. Nature, 488(7409), 86-90.

Berendsen, R. L., Pieterse, C. M., & Bakker, P. A. (2012). The rhizosphere microbiome and plant health. Trends in Plant Science, 17(8), 478-486.

soil-metagenomics 16s-rrna-sequencing operational-taxonomic-unit-clustering diversity-analysis plant-microbiome-profiling amplicon-sequence-variant-analysis


Rich Picture

Rich Picture Diagram
Soft Systems Picture
Situation Summary Diagram
Zengin Resim

A Rich Picture is a free-form, annotated drawing used in the early exploratory stage of Soft Systems Methodology to represent the full complexity of a problematic situation. Developed by Peter Checkland at Lancaster University, it captures people, roles, concerns, processes, conflicts, and environmental factors in a single visual canvas. It is used primarily by systems analysts, organizational consultants, and action researchers who need to surface multiple stakeholder perspectives before imposing any formal structure on a messy, ill-defined problem.

Output: Visual narrative of a messy situation

Checkland, P. (1981). Systems Thinking, Systems Practice. Wiley.


Richmond Agitation-Sedation Scale

The Richmond Agitation-Sedation Scale (RASS), developed by Sessler et al. in 2002, is a 10-level ordinal scale for assessing level of consciousness, agitation, and sedation in critically ill patients. It ranges from +4 (combative/violent) through 0 (alert and calm) to -5 (unarousable), enabling precise titration of sedative and analgesic medications in ICU settings.

Sessler, C. N., Gosnell, M. S., Grap, M. J., et al. (2002). The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients. American Journal of Respiratory and Critical Care Medicine, 166(10), 1338-1344.

Ely, E. W., Inouye, S. K., Bernard, G. R., et al. (2003). Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the ICU (CAM-ICU). JAMA, 286(21), 2703-2710.


Ridge Regression

Ridge Regression (L2-Regularized Linear Regression)

Ridge Regresyonu
ridge regresyonu
L2-regularized regression
Tikhonov regularization

Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.

Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.

lasso-regression elastic-net linear-regression pca logistic-regression


Right-Wing Authoritarianism Scale

The Right-Wing Authoritarianism Scale (RWA) is a self-report measure developed by Bob Altemeyer in 1981 to assess individual differences in authoritarian attitudes, including submission to established authorities, adherence to conventional norms, and aggression toward those perceived to violate social conventions. The scale measures three core dimensions: authoritarian submission, authoritarian aggression, and conventionalism. It has become a cornerstone of research on authoritarianism, political attitudes, and intergroup prejudice.

Altemeyer, B. (1981). Right-wing authoritarianism. University of Manitoba Press.

Altemeyer, B. (1988). Enemies of freedom: Understanding right-wing authoritarianism. Jossey-Bass.

social-dominance-orientation-scale modern-racism-scale ambivalent-sexism-inventory cultural-values-scale


RIM

RIM (Reference Ideal Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Cables, E., Lamata, M. T., Verdegay, J. L. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Distance to user-defined reference intervals (ideal + anti-ideal)

Cables, E., Lamata, M. T., Verdegay, J. L. (2016). RIM-reference ideal method in multicriteria decision making. Information Sciences

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Ring Signature

A ring signature is a digital signature scheme allowing a member of a group (ring) to sign a message on behalf of the group without revealing the signer's identity. Proposed by Rivest, Shamir, and Tauman in 2001, ring signatures provide signer anonymity while still proving that the signature comes from one member of a specified set. This cryptographic primitive is widely used in privacy-preserving applications, whistleblowing systems, and anonymous messaging platforms.

Rivest, R. L., Shamir, A., & Tauman, Y. (2001). How to leak a secret. In Advances in Cryptology - ASIACRYPT 2001, LNCS 2248, pp. 552-565.

Cramer, R., Damgård, I., & Schoenmakers, B. (1994). Proofs of partial knowledge and simplified design of witness hiding protocols. In Advances in Cryptology - CRYPTO 1994, LNCS 839, pp. 174-187.


Ripeness Index

Multi-Parameter Quantification of Fruit Maturity and Harvest Readiness

maturity index
harvest readiness assessment
fruit maturation scoring

Ripeness index combines multiple quality measurements—soluble solids, firmness, color, starch degradation, ethylene production—into a single composite score indicating fruit maturity and harvest readiness. Unlike single-parameter metrics, this integrated approach accounts for cultivar variation and environmental influence to predict consumer acceptability more reliably. It is widely adopted in export industries and research settings to standardize harvest decisions.

Pratt, H. K., & Goeschl, J. D. (2006). Physiological roles of ethylene in plants. Annual Review of Plant Physiology, 20, 541–566.

Blankenship, S. M., & Dole, J. M. (2003). 1-Methylcyclopropene: A review. Postharvest Biology and Technology, 28(1), 1–25.

brix-measurement fruit-color-analysis postharvest-storage-simulation crop-load-management


Ripley K Function

Ripley K Function (Point Pattern Analysis)

Ripley's K Function
Second-Order Intensity Function
K(d) Function
Ripley K Fonksiyonu

The Ripley K function, introduced by Brian Ripley in 1977, is a second-order summary statistic for spatial point patterns. It measures how the number of points within a given distance d of a typical point compares to what would be expected under complete spatial randomness (CSR). Widely used in ecology, epidemiology, criminology, and geography, the K function reveals whether events cluster, disperse, or distribute randomly across a study area at multiple spatial scales simultaneously.

Complete spatial randomness (CSR) — Poisson process

Monte Carlo simulation envelopes for significance

Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B, 39(2), 172–212.


RIPLS

Readiness for Interprofessional Learning Scale

The RIPLS is a 19-item self-report questionnaire designed to measure healthcare students' attitudes and readiness toward interprofessional learning and collaboration. Developed by Parsell and Bligh in 1999, it assesses three core dimensions of interprofessional readiness: teamwork and collaboration, professional identity, and recognition of roles and responsibilities across professions. The RIPLS is widely used in health professions education to evaluate the effectiveness of interprofessional education initiatives.

Parsell, G. & Bligh, J. (1999). The development of a questionnaire to assess the readiness of health care students for interprofessional learning (RIPLS). Med Educ 33(2): 95–100.


Risk-adjusted case-control study

adjusted case-control study
covariate-adjusted case-control
risk-stratified case-control study
matched and adjusted case-control study

1950s–1980s (case-control design from 1950; risk-adjustment conventions established by 1980s)

Doll & Hill (foundational case-control); risk adjustment via multivariate logistic regression systematised by Schlesselman (1982) and Breslow & Day (1980)

A risk-adjusted case-control study is an observational design that identifies individuals with a disease outcome (cases) and comparable individuals without it (controls), then uses statistical adjustment — most commonly multivariable logistic regression — to estimate the association between an exposure and the outcome while controlling for confounding risk factors. The adjustment step is what distinguishes this variant from a simple case-control study, producing odds ratios that better reflect the independent contribution of the exposure of interest.

Categorical and continuous covariates, binary outcome (case/control status)

Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.

case-control-study matched-case-control-study logistic-regression propensity-score-matching cohort-study multivariable-analysis


Risk-adjusted case-crossover design

adjusted case-crossover study
covariate-adjusted case-crossover
risk-controlled case-crossover
case-crossover with risk adjustment

1991 (base design); risk-adjustment extensions from mid-1990s onward

Malcolm Maclure (case-crossover base); extensions incorporating covariate risk adjustment developed in subsequent pharmacoepidemiology literature

The risk-adjusted case-crossover design is a self-matched epidemiological method that compares a person's exposure during a brief hazard window immediately preceding an acute event to their exposure during one or more control windows from the same individual, while formally accounting for time-varying or time-fixed covariates that could confound the exposure-event relationship. By using each case as their own control, stable individual-level confounders are automatically cancelled, while covariate adjustment handles residual time-varying risks.

Observational analytic epidemiological design

Individual-level event data with time-stamped exposure and covariate records

Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.

Navidi, W. (1998). Bidirectional case-crossover designs for exposures with time trends. Biometrics, 54(2), 596–605.

case-crossover-design case-control-study risk-adjusted-cohort-study conditional-logistic-regression propensity-score-matching time-series-analysis


Risk-adjusted case series

risk-stratified case series
adjusted case series
risk-corrected case series

Copeland, Jones & Walters (POSSUM score, 1991); broader risk-adjustment methodology developed across surgical and critical care audit literature

A risk-adjusted case series is an observational study design that reports outcomes for a consecutive or defined group of patients undergoing the same procedure or sharing a condition, while statistically correcting for differences in patient-level baseline risk. Rather than presenting raw complication or mortality rates, it compares observed outcomes against expected rates derived from a validated scoring model (e.g., POSSUM, APACHE, ASA grade), enabling fairer evaluation of clinical performance across institutions or over time.

Observational study design with statistical risk correction

Clinical records, operative/procedure logs, patient-level outcome data with baseline covariates

Copeland, G. P., Jones, D., & Walters, M. (1991). POSSUM: a scoring system for surgical audit. British Journal of Surgery, 78(3), 355–360.

Mayer, E. K., Bottle, A., Darzi, A. W., & Aylin, P. (2004). Case volume and outcome in the surgical treatment of colorectal cancer. British Journal of Surgery, 91(9), 1104–1110.

case-series risk-adjusted-cohort-study propensity-score-matching competing-risks-analysis survival-analysis diagnostic-accuracy-study


Risk-adjusted cohort study

adjusted cohort study
covariate-adjusted cohort
risk-controlled prospective study
propensity-adjusted cohort

Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s)

Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century

A risk-adjusted cohort study is an observational epidemiological design in which a defined group of individuals is followed over time to compare outcomes between exposed and unexposed subgroups, with statistical methods applied to control for measured confounders. Adjustment strategies — including multivariable regression, propensity score matching, inverse probability weighting, or standardization — are used to reduce bias and produce effect estimates that more closely approximate what would be observed in a randomized trial.

Observational epidemiological study design with statistical confounding control

Longitudinal individual-level data with measured covariates (continuous, binary, categorical)

Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.

cohort-study propensity-score-matching multivariable-regression case-control-study randomized-controlled-trial instrumental-variable-analysis


Risk-adjusted competing risks analysis

Risk-adjusted Competing Risks Survival Analysis

competing risks regression
subdistribution hazard model
cause-specific hazard analysis
Fine-Gray model

1999 (subdistribution hazard model); cause-specific hazard framework earlier

Risk-adjusted competing risks analysis extends classical survival analysis to settings where subjects can experience more than one type of terminal event, and where the occurrence of one event prevents the occurrence of another. By modelling cause-specific or subdistribution hazards while adjusting for measured confounders, the method yields unbiased estimates of the absolute probability — the cumulative incidence function — of each event type over time in the presence of competing events.

Regression model for time-to-event data with competing events

Time-to-event data with event type indicator and covariates

Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.

Latouche, A., Allignol, A., Beyersmann, J., Labopin, M., & Fine, J. P. (2013). A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions. Journal of Clinical Epidemiology, 66(6), 648–653.

cox-proportional-hazards kaplan-meier-estimator cause-specific-hazard-model subdistribution-hazard-model survival-analysis propensity-score-matching


Risk-adjusted Cox Proportional Hazards

Risk-adjusted Cox Proportional Hazards Regression

adjusted Cox regression
multivariable Cox model
covariate-adjusted survival analysis
risk-adjusted survival model

1972 (Cox model); risk adjustment widespread from 1980s

D. R. Cox (base model); risk-adjustment as routine practice formalised through clinical epidemiology literature from the 1980s onward

Risk-adjusted Cox proportional hazards regression extends the classical Cox (1972) survival model by simultaneously entering known confounders — age, sex, comorbidities, disease severity — into the model alongside the exposure of primary interest. This adjustment isolates the independent effect of the exposure on the hazard of an event, producing hazard ratios (HRs) that are not distorted by baseline differences between comparison groups. It is the most widely used method for multivariable survival analysis in clinical and epidemiological research.

Time-to-event (survival) data with one binary or multi-level outcome, continuous or categorical covariates

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.

Hosmer, D. W., Lemeshow, S., & May, S. (2008). Applied Survival Analysis: Regression Modeling of Time-to-Event Data (2nd ed.). Wiley.

kaplan-meier-estimator log-rank-test competing-risks-regression cox-proportional-hazards logistic-regression propensity-score-matching


Risk-adjusted cross-sectional epidemiological study

Risk-Adjusted Cross-Sectional Epidemiological Study

risk-adjusted cross-sectional survey
case-mix adjusted cross-sectional study
standardized cross-sectional analysis
adjusted prevalence study

1990s (risk-adjustment integration); cross-sectional design foundational since mid-20th century

Rooted in classical cross-sectional epidemiology (Doll, Hill, Lilienfeld); risk-adjustment formalization attributed to Lisa Iezzoni and colleagues in health outcomes research (1990s)

A risk-adjusted cross-sectional epidemiological study measures the prevalence of health outcomes or exposures in a defined population at a single point in time, then applies statistical risk-adjustment methods — such as regression standardization, direct or indirect standardization, or propensity scoring — to remove the distorting influence of differences in patient case-mix across comparison groups. The approach is widely used in health services research, comparative effectiveness, and clinical quality assessment.

Observational epidemiological design with statistical adjustment

Cross-sectional survey, administrative, or registry data with individual-level covariates

Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.

Iezzoni, L. I. (Ed.). (2003). Risk Adjustment for Measuring Health Care Outcomes (3rd ed.). Health Administration Press.

cross-sectional-study logistic-regression propensity-score-matching direct-standardization multilevel-modeling case-control-study


Risk-adjusted diagnostic accuracy study

case-mix-adjusted diagnostic accuracy
stratified diagnostic accuracy study
covariate-adjusted diagnostic accuracy
risk-stratified DTA study

Conceptual roots 1980s–1990s; covariate-adjusted ROC formally introduced 2009

Margaret Pepe and colleagues; covariate-adjusted ROC formalized by Janes & Pepe (2009)

A risk-adjusted diagnostic accuracy study evaluates how well an index test identifies a target condition while explicitly accounting for patient-level risk factors that influence either disease prevalence or test performance. By adjusting for case-mix, it yields accuracy estimates — sensitivity, specificity, and AUC — that are not confounded by the composition of the study sample, enabling fairer comparisons across populations and clinical settings.

Observational clinical study design with covariate adjustment

Binary or ordinal test results, reference standard classification, and patient-level covariates (risk factors)

Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.

Janes, H., & Pepe, M. S. (2009). Adjusting for covariate effects on classification accuracy using the covariate-adjusted ROC curve. Biometrika, 96(2), 371–382.

diagnostic-accuracy-study prospective-diagnostic-accuracy-study roc-analysis logistic-regression cohort-study screening-test-evaluation


Risk-adjusted dose-response analysis

1980s-1990s (formalized in modern epidemiology)

Sander Greenland; Kenneth Rothman (foundational epidemiological methods)

Risk-adjusted dose-response analysis quantifies the relationship between increasing levels of an exposure (dose) and the probability or magnitude of an outcome (response), while simultaneously controlling for baseline risk factors that could confound or modify this relationship. The method is widely applied in clinical epidemiology, pharmacoepidemiology, and environmental health research to isolate the causal contribution of exposure intensity from background risk heterogeneity among participants.

Individual-level observational or clinical trial data with exposure measures and potential confounders

Greenland, S. (1995). Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology, 6(4), 356-365.

dose-response-analysis cox-proportional-hazards logistic-regression propensity-score-analysis restricted-cubic-splines cohort-study


Risk-adjusted ecological study

ecological regression with risk adjustment

Extension of ecological study methodology; risk adjustment concepts formalized by Morgenstern (1982) and developed further in health outcomes research

A risk-adjusted ecological study is an observational epidemiological design that examines associations between exposures and outcomes measured at the group or area level — such as regions, hospitals, or countries — while statistically controlling for known risk factors also measured at that level. By incorporating risk adjustment through ecological regression or standardization, the design reduces (though cannot eliminate) confounding from group-level variables, enabling more valid comparisons across populations or settings.

Observational ecological design with statistical confounding control

Aggregate/group-level data (population rates, area-level covariates)

Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344.

Wakefield, J. (2008). Ecologic studies revisited. Annual Review of Public Health, 29, 75–90.

ecological-study cohort-study risk-adjusted-cohort-study multilevel-modeling standardized-mortality-ratio spatial-epidemiology


Risk-adjusted Kaplan-Meier analysis

Risk-adjusted Kaplan-Meier Survival Analysis

2001–2004 (formal statistical framework for weighted KM curves)

Conceptual basis: Kaplan & Meier (1958); risk-adjustment via IPTW formalised by Hernán, Brumback & Robins (2001), with practical implementation by Cole & Hernán (2004)

Risk-adjusted Kaplan-Meier analysis combines the non-parametric Kaplan-Meier estimator with inverse probability of treatment weighting (IPTW) or similar risk-adjustment procedures to produce survival curves that are comparable across groups as if the groups had identical distributions of baseline confounders. It is the observational-study analogue of plotting survival curves from a randomised trial.

Time-to-event (survival) data with baseline covariate data for weight estimation

Cole, S. R., & Hernan, M. A. (2004). Adjusted survival curves with inverse probability weights. Computer Methods and Programs in Biomedicine, 75(1), 45–49.

Hernan, M. A., Brumback, B., & Robins, J. M. (2001). Marginal structural models to estimate the joint causal effect of nonrandomized treatments. Journal of the American Statistical Association, 96(454), 440–448.

kaplan-meier-analysis cox-proportional-hazards propensity-score-matching inverse-probability-weighting survival-analysis competing-risks-analysis


Risk-adjusted Nested Case-Control

nested case-control with risk adjustment

1977 (nested case-control); risk-adjusted extensions 1980s–2000s

Thomas (1977) for nested case-control; risk adjustment extensions developed through pharmacoepidemiology literature (1980s–2000s)

A risk-adjusted nested case-control study embeds a case-control comparison inside a defined cohort and explicitly accounts for differences in baseline risk between cases and controls through covariate adjustment — most commonly via risk scores, propensity scores, or stratification. It preserves the efficiency advantages of the nested design while reducing confounding attributable to pre-existing risk differentials, making it especially valuable in pharmacoepidemiology and clinical effectiveness research.

Time-to-event cohort data with covariate and exposure records

Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.

nested-case-control case-cohort-study propensity-score-matching cox-proportional-hazards conditional-logistic-regression cohort-study


Risk-adjusted Phase I clinical trial

risk-stratified Phase I trial
risk-adaptive dose-escalation study
covariate-adjusted Phase I study
risk-based dose-finding trial

Evolved from the Continual Reassessment Method (O'Quigley et al., 1990) extended with patient-level risk covariates

A risk-adjusted Phase I clinical trial is a first-in-human or dose-finding study that explicitly incorporates patient-level risk covariates — such as organ function, prior therapy, or genetic markers — into the dose-escalation model. Rather than treating all enrolled participants as homogeneous, the design accounts for individual differences in tolerance, allowing the recommended dose to vary by risk stratum. This approach is especially common in oncology, where patients with impaired renal function or heavily pre-treated disease may tolerate lower doses than the broader population.

Dose-toxicity outcomes, patient risk covariate data (binary or continuous clinical variables)

Iasonos, A., Wilton, A. S., & Gonen, M. (2008). A review of stochastic dose-finding methods. Statistics in Medicine, 27(25), 5031–5046.

O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: A practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48.

phase-i-clinical-trial adaptive-phase-i-clinical-trial bayesian-phase-i-clinical-trial dose-response-analysis randomized-clinical-trial competing-risks-analysis


Risk-adjusted Phase II clinical trial

Risk-Adjusted Phase II Clinical Trial Design

risk-stratified Phase II trial
covariate-adjusted Phase II design
risk-adjusted two-stage design
RA Phase II trial

Peter Thall, Richard Simon (Bayesian risk-adjusted extension); Simon optimal two-stage as precursor

A risk-adjusted Phase II clinical trial is an early-phase efficacy design that incorporates patient baseline risk strata — such as disease severity, prognostic score, or comorbidity burden — directly into the trial's stopping rules and sample size calculations. By conditioning response targets and futility/efficacy thresholds on risk group membership, the design avoids the bias that arises when a new therapy is evaluated in a population whose prognostic mix differs from the historical control on which the null hypothesis was based.

Binary or continuous efficacy outcomes stratified by baseline risk covariates

Thall, P. F., & Simon, R. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics, 50(2), 337–349.

Simon, R. (1989). Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10(1), 1–10.

simon-two-stage-design bayesian-adaptive-design seamless-phase-ii-iii-design response-adaptive-randomization stratified-randomized-controlled-trial basket-trial