Analysis of Clinical Trials with Missing Data by Modeling Causes of Drop-Outs

Kada Akiko, Matsuyama Yutaka, Sato Tosiya

doi:10.5691/jjb.23.81

In clinical trials, some patients are dropped-out of the trials by the different causes and their outcomes may happen to be missing. In such a case, analyses ignoring the missing mechanisms lead a biased estimator. One of the methods for taking them into account is the IPCW (Inverse Probability of Censoring Weighted) method, which accounts for the observed past histories of time-dependent factors that are predictors of drop-outs and are correlated with the outcomes. In this method, the probability of being censored is usually modeled via a logistic regression without considering the causes of drop-outs. We developed the IPCW method including the causes of drop-outs such as improvement or aggravation. As an example, we analyzed the data from a randomized clinical trial of a drug for osteoporosis. To evaluate the efficacy, the difference of the rate of increasing in the lumber vertebral mineral density at 48 weeks from baseline in the two dose groups were estimated. We compared the results of the proposed IPCW methods with those of the usual IPCW methods, complete-case analysis, and LOCF method. Although the results of the two IPCW estimators did not change much, the missing mechanisms could be modeled reasonably and could be interpreted clinically by the proposed method compared with the usual IPCW method.

Analysis of Clinical Trials with Missing Data by Modeling Causes of Drop-Outs

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