Selection Bias in Cancer Survivorship Studies and its Remediation

Oregon Convention Center 

With improvements in screening and treatment, the number of cancer survivors is growing, increasing the need for research into long-term health and well-being after diagnosis. Longitudinally collected data from the Women's Health Initiative (WHI), and associated Life and Longevity After Cancer (LILAC) Study, enable critical evaluation of time-varying health disparities between survivors and cancer-free individuals. However, traditional assessment of observed data may distort the effect of cancer and its treatment on trajectories of aging. Bias can result when there is differential loss to follow-up and death between survivors and cancer-free individuals, and when other characteristics are predictors of our outcome and censoring. Using simulations informed by real data, we will illustrate underlying causal structures that can introduce selection bias into studies of cancer survivorship and demonstrate methods to remediate this bias. These considerations will guide discussion of LILAC data analysis, and general recommendations for minimizing bias in future studies of aging trajectories among cancer survivors.