Sequential methods for comparing years of life saved in the two-sample censored data problem.
This research develops nonparametric strategies for sequentially monitoring clinical trial data where detecting years of life saved is of interest. The recommended test statistic looks at integrated differences in survival estimates during the time frame of interest. In many practical situations, the test statistic presented has an independent increments covariance structure. Hence, with little additional work, we may apply these testing procedures using available methodology. In the case where an independent increments covariance structure is present, we suggest how clinical trial data might be monitored using these statistics in an information-based design. The resulting study design maintains the desired stochastic operating characteristics regardless of the shapes of the survival curves being compared. This offers an advantage over the popular log-rank-based design strategy since more restrictive assumptions relating to the behavior of the hazards are required to guarantee the planned power of the test. Recommendations for how to sequentially monitor clinical trial progress in the nonindependent increments case are also provided along with an example.
Murray
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Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor 48109-2029, USA. skmurray@umich.edu
Tsiatis
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D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D013269:Stochastic Processes; D016019:Survival Analysis