The potential for increased power from combining P-values testing the same hypothesis

Ganju, Jitendra; Julie, Guoguang

doi:10.1177/0962280214538016

Cited by 13 publications

(8 citation statements)

References 27 publications

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“…Some researchers [24][25][26] noted that the combination of two (or more) test statistics can increase the power compared with the use of a single test. Because the Gray test may lose power when non-proportional SDHs are present, we combined the Gray test and Diff test to maintain power under various scenarios.…”

Section: Combined Testmentioning

confidence: 99%

The use of restricted mean time lost under competing risks data

Lyu

Hou

Chen

2020

BMC Med Res Methodol

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Background: Under competing risks, the commonly used sub-distribution hazard ratio (SHR) is not easy to interpret clinically and is valid only under the proportional sub-distribution hazard (SDH) assumption. This paper introduces an alternative statistical measure: the restricted mean time lost (RMTL). Methods: First, the definition and estimation methods of the measures are introduced. Second, based on the differences in RMTLs, a basic difference test (Diff) and a supremum difference test (sDiff) are constructed. Then, the corresponding sample size estimation method is proposed. The statistical properties of the methods and the estimated sample size are evaluated using Monte Carlo simulations, and these methods are also applied to two real examples.

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Section: Combined Testmentioning

confidence: 99%

The use of restricted mean time lost under competing risks data

Lyu

Hou

Chen

2020

BMC Med Res Methodol

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“…The permutation method described above for nonsequential trials has been shown to control the type I error rate and provide robust inference. [6,7]…”

Section: An Overview Of the Procedures For Nonsequential Trialsmentioning

confidence: 99%

“…The proposals have included maximum or weighted or supremum versions of tests in the survival setting, [1][2][3][4] a model selection algorithm evaluated from blinded data, [5] or a combined test procedure. [6,7] The combination method is broadly applicable (eg, it permits combining P values even when testing different hypotheses) and is simple to use. The method involves using a function of P values from the multiple prespecified test statistics for inference while controlling the type I error rate at its designated value.…”

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confidence: 99%

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Robust inference for group sequential trials

Ganju

Lin

Zhou

2017

Pharmaceutical Statistics

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For ethical reasons, group sequential trials were introduced to allow trials to stop early in the event of extreme results. Endpoints in such trials are usually mortality or irreversible morbidity. For a given endpoint, the norm is to use a single test statistic and to use that same statistic for each analysis. This approach is risky because the test statistic has to be specified before the study is unblinded, and there is loss in power if the assumptions that ensure optimality for each analysis are not met. To minimize the risk of moderate to substantial loss in power due to a suboptimal choice of a statistic, a robust method was developed for nonsequential trials. The concept is analogous to diversification of financial investments to minimize risk. The method is based on combining P values from multiple test statistics for formal inference while controlling the type I error rate at its designated value.This article evaluates the performance of 2 P value combining methods for group sequential trials. The emphasis is on time to event trials although results from less complex trials are also included. The gain or loss in power with the combination method relative to a single statistic is asymmetric in its favor. Depending on the power of each individual test, the combination method can give more power than any single test or give power that is closer to the test with the most power. The versatility of the method is that it can combine P values from different test statistics for analysis at different times. The robustness of results suggests that inference from group sequential trials can be strengthened with the use of combined tests.

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“…Ganju et al. recently proposed to analyze clinical trial data by pre-specifying multiple test statistics and using a combined statistic – the minimum p-value – for inference when there is uncertainty about what candidate primary endpoint, hypothesis, or statistical test to use in planning a clinical trial [4] , [5] , [6] , [7] . The critical value for hypothesis testing comes from permutation which consists of re-randomizing the treatment assignments and calculating the combined statistic.…”

Section: Introductionmentioning

confidence: 99%

Robust inference for responder analysis: Innovative clinical trial design using a minimum p-value approach

Lin

2016

Contemporary Clinical Trials Communications

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Responder analysis is in common use in clinical trials, and has been described and endorsed in regulatory guidance documents, especially in trials where “soft” clinical endpoints such as rating scales are used. The procedure is useful, because responder rates can be understood more intuitively than a difference in means of rating scales. However, two major issues arise: 1) such dichotomized outcomes are inefficient in terms of using the information available and can seriously reduce the power of the study; and 2) the results of clinical trials depend considerably on the response cutoff chosen, yet in many disease areas there is no consensus as to what is the most appropriate cutoff. This article addresses these two issues, offering a novel approach for responder analysis that could both improve the power of responder analysis and explore different responder cutoffs if an agreed-upon common cutoff is not present. Specifically, we propose a statistically rigorous clinical trial design that pre-specifies multiple tests of responder rates between treatment groups based on a range of pre-specified responder cutoffs, and uses the minimum of the p-values for formal inference. The critical value for hypothesis testing comes from permutation distributions. Simulation studies are carried out to examine the finite sample performance of the proposed method. We demonstrate that the new method substantially improves the power of responder analysis, and in certain cases, yields power that is approaching the analysis using the original continuous (or ordinal) measure.

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The potential for increased power from combining P-values testing the same hypothesis

Cited by 13 publications

References 27 publications

The use of restricted mean time lost under competing risks data

The use of restricted mean time lost under competing risks data

Robust inference for group sequential trials

Robust inference for responder analysis: Innovative clinical trial design using a minimum p-value approach

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