TITLE:
Evaluating Methods for Dealing with Missing Outcomes in Discrete-Time Event History Analysis: A Simulation Study
AUTHORS:
Shahab Jolani, Nils L. M. van de Ven, Maryam Safarkhani, Mirjam Moerbeek
KEYWORDS:
Missing Data, Deletion, Imputation, Retrospective Observations, Survival Analysis
JOURNAL NAME:
Open Journal of Statistics,
Vol.11 No.1,
January
14,
2021
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.