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Stepwise progressive parametric multiple testing procedure with correlated normal test statistics.

Xuan Deng1, Mark Chang1

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Journal of Biopharmaceutical Statistics
|March 20, 2020
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Summary
This summary is machine-generated.

This study introduces a new stepwise progressive parametric multiple (SPPM) testing procedure to address multiple testing issues in clinical trials. SPPM offers improved statistical power compared to existing methods in specific scenarios.

Keywords:
Group sequential designmultiple primary endpointsmultivariate Normal distributionstopping boundaries

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Inference

Background:

  • Multiple testing is a common challenge in clinical trials, potentially inflating the type I error rate.
  • Existing statistical procedures may not always provide optimal power when dealing with multiplicity.
  • Controlling the family-wise error rate is crucial for the validity of clinical trial results.

Purpose of the Study:

  • To propose a novel stepwise progressive parametric multiple (SPPM) testing procedure.
  • To evaluate the performance of the SPPM procedure against existing multiple testing methods.
  • To demonstrate the advantages of the SPPM procedure in terms of statistical power.

Main Methods:

  • The SPPM procedure utilizes products of combinations of local p-values.
  • Critical values are determined through progressive numerical integrations.
  • The closure principle is employed to maintain the overall error rate control.

Main Results:

  • The SPPM procedure was compared to several other multiple testing procedures.
  • SPPM demonstrated superior statistical power in specific multiple testing situations.
  • The proposed method effectively controls the type I error rate while enhancing power.

Conclusions:

  • The stepwise progressive parametric multiple (SPPM) testing procedure is an effective approach for managing multiplicity in clinical trials.
  • SPPM offers a valuable alternative for researchers seeking to maximize statistical power in complex testing scenarios.
  • Further research may explore the application of SPPM in diverse clinical trial designs.