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Austin Biometrics and Biostatistics




It is a common practice to perform a separate analysis of efficacy and safety data from clinical trials to estimate the benefit and risk aspects of a particular treatment regimen. However, by doing so, one is likely to miss the complete picture of the treatment effect given that these data are generated from the same study subjects and therefore most likely will be correlated. Therefore, it is desirable to analyze these data jointly to obtain a more complete profile of the treatment regimen. A substantial number of statistical methodologies have been proposed in the last decade to model the time-to-event data and longitudinal repeated measures jointly. These methods provide better insights to understand the treatment effect in time-to-event data by incorporating the information contained in the longitudinal repeated measures. In this article, we utilize the joint model method to analyze the time-to-event data, such as patient overall survival, and the repeated measures of laboratory test data to better estimate the treatment effect of a regimen. The data from a recent oncology clinical trial is used to illustrate the application of our proposed method.


Copyright resides with the author in this open access article. Article obtained from Austin Biometrics and Biostatistics.