Abstract:
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Very few methods are currently available for group sequential analysis of recurrent events data subject to a terminal event in the clinical trial setting. This research helps fill this gap by developing methods for sequentially monitoring the nonparametric, two-sample Tayob and Murray statistic (2014). Advantages of the Tayob and Murray statistic include high power to detect treatment differences when there is correlation between recurrent event times or between recurrent and terminal events in an individual. This statistic does not suffer bias from dependent censoring, regardless of the correlation between event times of interest. Nor does the statistic require proportionality between groups of the cumulative mean number of events over time. This manuscript briefly reviews the Tayob and Murray statistic, develops and describes how to use methods for its group sequential analysis, and through simulation compares its operating characteristics with those of Cook and Lawless (1996), which is currently in use, as well as a time-to-first event analysis using the logrank test. We further illustrate our method using the Azithromycin in COPD Trial.
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