Self-selection bias in evaluating the effectiveness of mammography screening: A sensitivity analysis on data from the German screening program

Self-selection bias in observational studies assessing the effectiveness of a breast cancer screening program can arise when non-participants of screening have worse general health conditions or lower compliance with treatments than screening participants. Building on recently described methodology for tumour progression modelling and causal survival analysis of screening data, our main contribution is to describe approaches to conduct sensitivity analyses for self-selection effects when studying screening effects on all-cause survival and breast cancer cumulative incidence, among screen-detected cases, in a population where all individuals are offered screening. Sensitivity analyses are inspired by an approach to correcting for self-selection bias that has been used for observational studies of mortality. We report simulation studies that demonstrate the impact that a correction factor has on all-cause survival and on the cumulative incidence of breast cancer death following diagnosis. We also apply our methods to estimate the benefit of early detection in non-overdiagnosed screen-detected cancers using data from the German mammography screening program. For sensitivity parameter values, we leverage on data on other (non-breast cancer) cause survival among participants and non-participants of mammography screening in the same study and estimate how counterfactual all-cause survival is improved, and breast cancer cumulative incidence is reduced for patients with screen-detected cancers, when accounting for self-selection bias.