The effects of overweight or obesity on survival after cancer diagnosis are difficult to discern based on observational data because these associations reflect the net impact of both causal and spurious phenomena. We describe two sources of bias that might lead to underestimation of the effect of increased body weight on survival after cancer diagnosis: collider stratification bias and heterogeneity in disease bias. Given the mixed evidence on weight status, weight change, and postdiagnosis survival for cancer patients, systematic evaluation of alternative explanations is critical. The plausible magnitudes of these sources of bias can be quantified on the basis of expert knowledge about particular cancer types using simulation tools. We illustrate each type of bias, describe the assumptions researchers need make to evaluate the plausible magnitude of the bias, and provide a simple example of each bias using the setting of renal cancer. Findings from simulations, tailored to specific types of cancer, could help distinguish real from spurious effects of body weight on patient survival. Using these results can improve guidance for patients and providers about the relative importance of weight management after a diagnosis. Cancer Epidemiol Biomarkers Prev; 26(1); 17–20. ©2017 AACR.
Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).
- Received July 29, 2016.
- Revision received October 24, 2016.
- Accepted November 10, 2016.
- ©2017 American Association for Cancer Research.