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1.
Biometrics ; 75(1): 133-143, 2019 03.
Artículo en Inglés | MEDLINE | ID: mdl-30004574

RESUMEN

Lan and DeMets (1983) proposed the alpha spending function for group sequential trials to permit the use of unspecified frequencies and timings of interim analyses in the trial design. Regarding a trial with censored time to endpoint, Lan and DeMets (1989) later defined information time at an interim analysis in a maximum duration trial. To compare two survival curves utilizing such a design, information times for group sequential logrank and Wilcoxon-type statistics have been developed by assuming that the survival time follows an exponential distribution or a Weibull distribution without considering the censoring distribution. To better address the practical concerns inherent in clinical trials with survival endpoints, we present a new approach to adequately design a group sequential trial using the Harrington-Fleming (1982) test based on our proposed information fractions by assuming the censoring distribution depends on the patient's accrual time according to various entry distributions and by extending the underlying survival distribution to the generalized gamma distribution. We also determine associated sample sizes, expected number of events and expected stopping time. Two phase III trials of non-small-cell lung cancer originally designed using fixed-sample tests are utilized to illustrate the potential advantages of using a group sequential design with the proposed approach. This enhanced method facilitates the design and analysis of group sequential clinical trials studying survival endpoints by increasing implemental flexibility.


Asunto(s)
Ensayos Clínicos como Asunto , Proyectos de Investigación , Análisis de Supervivencia , Biometría , Carcinoma de Pulmón de Células no Pequeñas/genética , Carcinoma de Pulmón de Células no Pequeñas/mortalidad , Carcinoma de Pulmón de Células no Pequeñas/terapia , Humanos , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/terapia , Tamaño de la Muestra , Distribuciones Estadísticas , Factores de Tiempo
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