Statistical analysis of outcomes involving missing data due to mortality or dropout

Music City Center 

Multiple approaches are available to handle missing data if missing data are at random. We often observe missing outcome data in clinical studies due to death and dropout. Removing missing data due to mortality or dropout can reduce the sample size and subsequently statistical power. Moreover, the results are only generalizable to subjects representing non-mortality data. Missing data imputation ignoring death or dropout information may produce biased estimates and uninterpretable group estimates. We propose several approaches for analyzing different forms of outcome data after adjusting for the proportion of mortality or dropout rates. We used zero hurdle regression, multinomial logistic regression, ordinal logistic regression, and prognostic score-adjusted models. We applied these methods to determine the effects of statin on different outcomes in COVID-19 patients. Based on descriptive and simulation comparisons, our findings suggest directly analyzing the joint distribution of mortality or dropout and outcome data. However, the specific type of analysis depends on the type of outcome and prevalence of dropout or mortality in the study.

Zero hurdle models

Missing data

Multinomial logistic regression

Finite mixed models

prognostic score-adjusted models

ordinal logistic regression 

Biometrics Section