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Efficient statistical inference for a parallel study with missing data by using an exact method.

Guogen Shan1, Alan Hutson2, Gregory E Wilding3

  • 1a Epidemiology and Biostatistics Program , School of Public Health, UNLV , Las Vegas , NV , USA.

Journal of Biopharmaceutical Statistics
|April 25, 2019
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Summary
This summary is machine-generated.

This study introduces an exact statistical approach for analyzing parallel group clinical trials with missing data. The exact method, particularly using the likelihood ratio statistic, offers improved accuracy and power compared to asymptotic methods for binary outcomes.

Keywords:
Data generating mechanismexact testone-sided testparallel studyunconditional test

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Inference

Background:

  • Missing data is common in parallel group studies comparing new treatments to standard care.
  • Binary outcomes in such studies are often summarized in 2x3 contingency tables.
  • Existing asymptotic methods for hypothesis testing with missing data may lack sufficient type I error rate control in small to medium sample sizes.

Purpose of the Study:

  • To develop and evaluate an exact statistical approach for hypothesis testing in parallel group studies with missing data.
  • To compare the performance of exact methods based on three previously proposed statistics.
  • To identify the most powerful exact approach for testing the equivalence of response rates.

Main Methods:

  • An exact approach based on maximization is proposed for statistical inference.
  • The performance of exact methods using three statistics (including likelihood ratio) is compared via extensive numerical studies.
  • The focus is on one-sided hypothesis testing for binary outcomes in parallel group designs with missing data.

Main Results:

  • Exact approaches guarantee type I error rate control and are computationally feasible.
  • The exact approach utilizing the likelihood ratio statistic demonstrated superior power compared to exact approaches based on the other two statistics.
  • The proposed exact method was illustrated using two real clinical trial datasets.

Conclusions:

  • The exact approach, particularly the likelihood ratio statistic, provides a valid and powerful method for statistical inference in parallel group studies with missing binary data.
  • This exact methodology addresses the limitations of asymptotic approaches in small to medium sample sizes.
  • The proposed exact approach is recommended for analyzing clinical trial data with missing observations.