Due to ethical and logistical concerns it is common for data monitoring committees to periodically monitor accruing clinical trial data to assess the safety, and possibly efficacy, of a new experimental treatment. Whe...Due to ethical and logistical concerns it is common for data monitoring committees to periodically monitor accruing clinical trial data to assess the safety, and possibly efficacy, of a new experimental treatment. When formalized, monitoring is typically implemented using group sequential methods. In some cases regulatory agencies have required that primary trial analyses should be based solely on the judgment of an independent review committee (IRC). The IRC assessments can produce difficulties for trial monitoring given the time lag typically associated with receiving assessments from the IRC. This results in a missing data problem wherein a surrogate measure of response may provide useful information for interim decisions and future monitoring strategies. In this paper, we present statistical tools that are helpful for monitoring a group sequential clinical trial with missing IRC data. We illustrate the proposed methodology in the case of binary endpoints under various missingness mechanisms including missing completely at random assessments and when missingness depends on the IRC’s measurement.展开更多
Clinical trials are usually long term studies and it seems impossible to reach all required subjects at the same time. Performing interim analyses and monitoring results may provide early termination of trial after ob...Clinical trials are usually long term studies and it seems impossible to reach all required subjects at the same time. Performing interim analyses and monitoring results may provide early termination of trial after obtaining significant results. The aim of this study is comparing group sequential tests in respect to advantage of sample size reduction and early termination. In this study, 4 test types used in group sequential designs were compared with fixed sample size design test and each other. Comparisons were done according to two-sided tests for comparing two treatments. In this sense, 1080 models were performed. In models, 2 different Type I errors, 2 different powers, 5 different analysis groups, 6 different effect sizes and 9 different variances selections were considered. All test types increased the maximum sample size in different manner, compared with fixed sample size design. Each test had different critical values to reject H0 hypothesis, at the same type I error rate and number of analyses conditions. Selection of test type used in group sequential designs depends on a few characteristics, as reducing sample size, early termination and detecting minimal effect size. Test performance is highly related with selected Type I error rate, power and number of analyses. In addition to these statistical characteristics, researchers should decide test type with respect to other trial conditions as the issue of trial, reaching subjects easy or not and importance of early termination.展开更多
Efron (1997) considered several approximations of p-values for simultaneous hypothesis testing. An extension of his approaches is considered here to approximate various probabilities of correlated events. Compared wit...Efron (1997) considered several approximations of p-values for simultaneous hypothesis testing. An extension of his approaches is considered here to approximate various probabilities of correlated events. Compared with multiple-integrations, our proposed method, the parallelogram formulas, based on a one-dimensional integral, not only substantially reduces the computational complexity but also maintains good accuracy. Applications of the proposed method to genetic association studies and group sequential analysis are investigated in detail. Numerical results including real data analysis and simulation studies demonstrate that the proposed method performs well.展开更多
文摘Due to ethical and logistical concerns it is common for data monitoring committees to periodically monitor accruing clinical trial data to assess the safety, and possibly efficacy, of a new experimental treatment. When formalized, monitoring is typically implemented using group sequential methods. In some cases regulatory agencies have required that primary trial analyses should be based solely on the judgment of an independent review committee (IRC). The IRC assessments can produce difficulties for trial monitoring given the time lag typically associated with receiving assessments from the IRC. This results in a missing data problem wherein a surrogate measure of response may provide useful information for interim decisions and future monitoring strategies. In this paper, we present statistical tools that are helpful for monitoring a group sequential clinical trial with missing IRC data. We illustrate the proposed methodology in the case of binary endpoints under various missingness mechanisms including missing completely at random assessments and when missingness depends on the IRC’s measurement.
文摘Clinical trials are usually long term studies and it seems impossible to reach all required subjects at the same time. Performing interim analyses and monitoring results may provide early termination of trial after obtaining significant results. The aim of this study is comparing group sequential tests in respect to advantage of sample size reduction and early termination. In this study, 4 test types used in group sequential designs were compared with fixed sample size design test and each other. Comparisons were done according to two-sided tests for comparing two treatments. In this sense, 1080 models were performed. In models, 2 different Type I errors, 2 different powers, 5 different analysis groups, 6 different effect sizes and 9 different variances selections were considered. All test types increased the maximum sample size in different manner, compared with fixed sample size design. Each test had different critical values to reject H0 hypothesis, at the same type I error rate and number of analyses conditions. Selection of test type used in group sequential designs depends on a few characteristics, as reducing sample size, early termination and detecting minimal effect size. Test performance is highly related with selected Type I error rate, power and number of analyses. In addition to these statistical characteristics, researchers should decide test type with respect to other trial conditions as the issue of trial, reaching subjects easy or not and importance of early termination.
基金supported by the Intramural Program of NIHsupported in part by National Natural Science Foundation of China (Grant No.10901155)supportedin part by NIH (Grant No. EY014478).
文摘Efron (1997) considered several approximations of p-values for simultaneous hypothesis testing. An extension of his approaches is considered here to approximate various probabilities of correlated events. Compared with multiple-integrations, our proposed method, the parallelogram formulas, based on a one-dimensional integral, not only substantially reduces the computational complexity but also maintains good accuracy. Applications of the proposed method to genetic association studies and group sequential analysis are investigated in detail. Numerical results including real data analysis and simulation studies demonstrate that the proposed method performs well.
