ArticleCancer medicine2025
Collider Bias Is an Insufficient Explanation for the Inverse Obesity Paradox in Prostate Cancer.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Weight trajectories throughout adulthood and prostate cancer incidence, aggressiveness, and death in 258 494 men.Journal of the National Cancer Institute · 2026Article
- Polygenic risk score and 20-year prostate cancer-specific mortality and survival.Communications medicine · 2026Article
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3 authors.
Funding
Abstract
backgroundCollider bias is often considered a potential explanation when the association between obesity and disease diagnosis differs from that with disease outcome, as seen in the "obesity paradox." For prostate cancer (PCa), in particular localized PCa, an "inverse" obesity paradox has been observed, where body mass index (BMI) is negatively associated with diagnosis (hazard ratio [HR] ~0.9 per 5-kg/m
methodsWe simulated binary disease diagnosis and outcome data, including the typically unmeasured/unknown background variable (U) that could introduce collider bias. We calculated U-unadjusted (biased) and U-adjusted (true) marginal odds ratios (OR) from a case-only analysis, and determined the bias percentage using
resultsAcross a broad range of plausible parameter values for the PCa context, collider bias did not distort the OR of BMI on PCa death by more than 4%, equivalent to a ± 0.04 distortion in the OR estimate for continuous BMI. In comparison, classical confounding showed a higher potential for distorting BMI and PCa death associations than collider bias.
conclusionsCollider bias alone is unlikely to explain the inverse obesity paradox in (localized) PCa, reinforcing some mechanistic evidence that the observed positive relationship between BMI and PCa death is real, and not a statistical artifact. This finding emphasizes the importance of exploring alternative mechanisms beyond collider bias to better understand the underlying factors driving this paradox.
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