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Estimation After a Group Sequential Trial.

Elasma Milanzi1, Geert Molenberghs2, Ariel Alonso3

  • 1I-BioStat, Universiteit Hasselt, B-3590 Diepenbeek, Belgium.

Statistics in Biosciences
|October 20, 2015
PubMed
Summary
This summary is machine-generated.

The sample average is a justifiable estimator in group sequential trials with multiple possible sample sizes. Simulations can falsely suggest bias, but no corrections are needed for estimators following these trials.

Keywords:
Exponential FamilyFrequentist InferenceGeneralized Sample AverageJoint ModelingLikelihood InferenceMissing at RandomSample Average

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Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trial Design

Background:

  • Group sequential trials allow for early stopping, leading to variable sample sizes.
  • Previous research focused on trials with only two possible sample sizes.
  • The ordinary sample average was previously shown to be a viable, though not optimal, estimator.

Purpose of the Study:

  • To extend the analysis of the sample average estimator to group sequential trials with a finite set of possible sample sizes.
  • To investigate the potential for simulation bias in evaluating estimators for such trials.
  • To provide guidance on estimator choice and statistical inference in group sequential designs.

Main Methods:

  • Utilized joint likelihood estimation to justify the sample average estimator.
  • Analyzed the consistency and asymptotic unbiasedness of the sample average.
  • Investigated the behavior of conditional likelihood estimators and likelihood-based inference.

Main Results:

  • The sample average is a justifiable, consistent, and asymptotically unbiased estimator for trials with multiple sample sizes.
  • Simulations can misleadingly suggest bias in the sample average when conditioned on sample size.
  • Conditional likelihood estimators offer a modification for small-sample bias concerns, and standard likelihood-based inference is applicable.

Conclusions:

  • No corrections are necessary for estimators in group sequential trials, even with multiple stopping options.
  • The sample average is a robust and justifiable choice for estimation.
  • Classical likelihood-based methods are appropriate for standard errors and confidence intervals, simplifying analysis.