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Modelling and sample size reestimation for longitudinal count data with incomplete follow up.

Thomas Asendorf1, Robin Henderson2, Heinz Schmidli3

  • 11 Department of Medical Statistics, University Medical Center Göttingen, Germany.

Statistical Methods in Medical Research
|June 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for clinical trials with longitudinal count data, improving sample size estimation and reestimation for enhanced study power and accuracy in multiple sclerosis research.

Keywords:
Adaptive designdiscrete autoregressive processlesion countsnegative binomialsample size reestimationtime dependent

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Longitudinal Data Analysis

Background:

  • Longitudinal count data present unique challenges in clinical trial analysis.
  • Accurate sample size estimation is crucial for trial validity and efficiency.
  • Multiple sclerosis (MS) trials often utilize lesion counts from magnetic resonance imaging as key endpoints.

Purpose of the Study:

  • To develop and evaluate statistical methods for modeling and inference of longitudinal count data in clinical trials.
  • To establish robust procedures for sample size estimation and blinded sample size reestimation.
  • To ensure adequate statistical power and control Type I error rates in trials with count outcomes.

Main Methods:

  • A binomial thinning model is employed to handle correlated count data with marginal Poisson or negative binomial distributions.
  • Methods for sample size planning and blinded sample size reestimation are developed for randomized controlled trials.
  • The proposed approaches are designed to accommodate incomplete observational data.

Main Results:

  • A simulation study demonstrated the effectiveness of the proposed sample size estimation and reestimation methods.
  • The procedures successfully maintained desired study power while controlling Type I error rates.
  • The modeling approach was illustrated using data from a clinical trial in secondary progressive multiple sclerosis patients.

Conclusions:

  • The developed statistical framework provides a reliable method for analyzing longitudinal count data in clinical trials.
  • The sample size estimation and reestimation techniques enhance the efficiency and integrity of clinical trial design.
  • The approach is applicable to various clinical settings, particularly those involving count-based endpoints like MS lesion counts.