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 reasonably 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.
Highlights
The aim of event history analysis is to study whether and when subjects experience some kind of event, such as smoking initiation, graduation from college or first criminal offence
Deletion methods suffered from either loss of power or undercoverage issues resulting from a biased standard error
In nine out of the 81,000 simulated data sets the estimated standard errors for the period deletion strategy were so large that they had a huge impact on the average standard error
Summary
The aim of event history analysis is to study whether and when subjects experience some kind of event, such as smoking initiation, graduation from college or first criminal offence. In retrospective studies subjects are not always able to remember the exact date at which they experienced the event, but they may be able to remember the calendar year or their age at the time of event occurrence In both cases the underlying survival process is continuous, but the time axis is divided in intervals. This implies a loss of information but hardly affects the estimates of model parameters, their standard errors and statistical power [2]. 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. 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
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