Evaluating Methods for Dealing with Missing Outcomes in Discrete-Time Event History Analysis: A Simulation Study

Abstract

Background: In discrete-time event history analysis, subjects are measured once each time period until they experience the event, prematurely drop out, or when the study concludes. This implies measuring event status of a subject in each time period determines whether (s)he should be measured in subsequent time periods. For that reason, intermittent missing event status causes a problem because, unlike other repeated measurement designs, it does not make sense to simply ignore the corresponding missing event status from the analysis (as long as the dropout is ignorable). Method: We used Monte Carlo simulation to evaluate and compare various alternatives, including event occurrence recall, event (non-)occurrence, case deletion, period deletion, and single and multiple imputation methods, to deal with missing event status. Moreover, we showed the methods’ performance in the analysis of an empirical example on relapse to drug use. Result: The strategies assuming event (non-)occurrence and the recall strategy had the worst performance because of a substantial parameter bias and a sharp decrease in coverage rate. Deletion methods suffered from either loss of power or undercoverage issues resulting from a biased standard error. Single imputation recovered the bias issue but showed an undercoverage estimate. Multiple imputations performed reasonabl y with a negligible standard error bias leading to a gradual decrease in power. Conclusion: On the basis of the simulation results and real example, we provide practical guidance to researches in terms of the best ways to deal with missing event history data .