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Simple randomization
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Body:In certain scenarios, in vitro dissolution tests can replace in vivo bioequivalence studies. This is particularly true when a drug product, though available in varying strengths, maintains proportional similarity in its active and inactive ingredients. In such cases, the need for in vivo bioequivalence studies for lower strength variants may be waived, provided dissolution tests and in vivo studies on the highest strength yield satisfactory results.Bioequivalence can be indicated through...
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Efficient baseline utilization for incomplete block crossover clinical trials.

Thomas O Jemielita1, Mary E Putt2, Devan V Mehrotra1

  • 11 Merck & Co., Inc., North Wales, PA, USA.

Statistical Methods in Medical Research
|November 29, 2017
PubMed
Summary
This summary is machine-generated.

Analyzing clinical trial data requires careful baseline utilization. Mixed effects models using change-from-baseline can underperform, impacting type I error rates and power in crossover trials.

Keywords:
Baseline adjustmentcovariance structurecovariatecrossover trialincomplete block designlongitudinal analysis

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmacometrics

Background:

  • Incomplete block crossover trials are common in drug development.
  • Standard analysis uses mixed effects models with unstructured covariance.
  • Treatment effect estimates are complex functions of contrasts.

Purpose of the Study:

  • To evaluate the impact of baseline utilization on statistical analysis in crossover trials.
  • To compare different analytical approaches regarding type I error rate and power.
  • To provide guidance on optimal analysis strategies for clinical drug development.

Main Methods:

  • Focus on three-treatment, two-period crossover designs.
  • Compare mixed effects models using change-from-baseline versus outcome as dependent variable with covariates.
  • Introduce a simpler fixed effects analysis of covariance for small samples.

Main Results:

  • Mixed effects analysis using change-from-baseline underperforms other methods.
  • Type I error rate and power are non-trivially influenced by baseline utilization.
  • Fixed effects ANCOVA is suitable for small samples, avoiding inflated type I error rates.

Conclusions:

  • Careful consideration of baseline data in mixed effects models is crucial for accurate clinical trial analysis.
  • Alternative analytical approaches, including ANCOVA, may offer superior performance.
  • Optimal analysis strategies depend on specific trial characteristics and sample size.