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case_images: Distributions all of variables of interest for all participants (A, C, E, G) and for participants who were diagnosed with cancer (B, D, F, H) during the five-year VITAL study period.
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case_images: For a complete description of variable proportions for the ethnic groups of interest, see  Figure 3 .
case_images: Distribution of vitamin D and placebo as a function of a race for (A) all participants, and (B) for participants who were diagnosed with cancer at some point during the VITAL study.
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case_images: To investigate the impact of vitamin D on cancer incidence, data was analyzed from 25,871 participants in the VITAL study.  Figure 4A  shows that the distribution of individuals receiving vitamin D and placebo was identical among all ethnic groups.  Figure 4B  displays the vitamin D intake of participants who were diagnosed with cancer during the 5-year period of the study.
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case_images: Outline of data manipulation performed in the analysis of the VITAL data set. Details on undersampling method (Step 1) performed to balance cancer diagnosis response variables.
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case_images: To address this issue, we employed the undersampling technique as outlined in Section 2.5 (Step 1); distributional properties of the obtained sample are shown in (a) and (b) of  Figure 1 , and are provided to verify that it does indeed form a representative statistical sample of the entire cancer negative (majority) class.
case_images: General procedure for determining the relative importance of risk factors in an imbalanced data set. For details on the undersampling technique (Step 1), see 
  Figure 1
  .
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case_images: Furthermore, once the model is successfully calibrated to each ethnic group, our goal is to compare the relative importance of each risk factor between ethnic groups as discussed in Section 2.4 and more broadly in  Figure 2 .
case_images: Relative variable importance plots (Step 3) for the logistic model using (A) all participants, (B) NHW participants, (C) NHB participants, and (D) Hispanic participants in the balanced data set.
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case_images: The results of this analysis are illustrated in  Figure 5 , where age and sex were identified as the most significant contributing factors for the "All", NHW, and NHB models. When race was disregarded in  Figure 5A , smoking emerged as the third most important factor across all three models.
case_images: A visual comparison of the age and BMI distributions between the original VITAL dataset and the transformed datasets, which include the upsampled SMOTE dataset, and the undersampling technique outlined in Section 2.5 (Step 1). Plot (A) shows the comparison of the distribution of age in the original VITAL data set compared to the undersampled data. Plot (B) makes the comparison for BMI in the original VITAL data set compared to the undersampled data. Plots (C) and (D) make the same comparisons, as (A) and (B) respectively, but for the VITAL data and SMOTE.
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case_images: Results are provided in  Figures 6C, D , and demonstrate that both SMOTE and the undersampling method exhibited distributions consistent with the original VITAL data, effectively achieving class balance.