Disease fatality and bias in survival cohorts

Abstract

Objectives: Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study.

Methods: We simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty years, where disease incidence was the outcome of interest. We simulated this 'inception' cohort under different assumptions about the effect of exposure on disease occurrence and fatality after disease occurrence. We then created a corresponding 'survivor' (or 'cross-sectional') cohort, where cohort enrollment took place at a specific date after exposure began in the inception cohort; subjects dying prior to that enrollment date were excluded. The disease of interest caused all deaths in our simulations, but was not always fatal. In the survivor cohort, person-time at risk began before enrollment for all subjects who did not die prior to enrollment. We compared exposure-disease associations in each inception cohort to those in corresponding survivor cohorts to determine how different assumptions impacted bias in the survivor cohorts. All subjects in both inception and survivor cohorts were considered equally susceptible to the effect of exposure in causing disease. We used Cox proportional hazards regression to calculate effect measures.

Results: There was no bias in survivor cohort estimates when case fatality among diseased subjects was independent of exposure. This was true even when the disease was highly fatal and more highly exposed subjects were more likely to develop disease and die. Assuming a positive exposure-response in the inception cohort, survivor cohort rate ratios were biased downwards when case fatality was greater with higher exposure.

Conclusions: Survivor cohort effect estimates for fatal outcomes are not always biased, although precision can decrease.

Keywords: Bias; Fatality; Simulation; Survivor cohort.