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Related Experiment Video

Updated: May 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Robustness of methods for blinded sample size re-estimation with overdispersed count data.

Simon Schneider¹, Heinz Schmidli, Tim Friede

¹Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Statistics in Medicine

|April 19, 2013

Summary

Calculating sample sizes for clinical trials with overdispersed count data is challenging. This study shows the EM-algorithm procedure is sensitive to stopping criteria, impacting trial planning and power.

Keywords:

EM algorithm adaptive design clinical trials event counts sample size

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Area of Science:

Biostatistics
Clinical Trial Design
Statistical Modeling

Background:

Count data endpoints are common in clinical trials.
Overdispersion (variance > mean) requires specialized statistical models like Poisson mixtures.
Accurate sample size calculation needs prior knowledge of event rates and overdispersion, often lacking in early trial phases.

Purpose of the Study:

To investigate the implementation of an EM-algorithm based sample size re-estimation procedure for overdispersed count data.
To assess the sensitivity of the EM-algorithm procedure to convergence criteria in clinical trial settings.
To compare the EM-algorithm procedure with other methods regarding sample size distribution and statistical power.

Main Methods:

Investigated the EM-algorithm's dependence on convergence criteria for sample size re-estimation.
Compared the EM-algorithm procedure to alternative methods using operating characteristics.
Explored the robustness of these procedures against deviations from model assumptions.

Main Results:

The EM-algorithm based sample size re-estimation procedure is sensitive to the choice of stopping criterion.
Some procedures demonstrate robustness to moderate deviations from model assumptions.
Performance comparison revealed differences in sample size distribution and power across methods.

Conclusions:

Careful selection of convergence criteria is crucial for the reliable implementation of EM-algorithm based sample size re-estimation in clinical trials.
The robustness findings suggest some methods are adaptable to real-world data variations.
These insights aid in optimizing sample size calculations for trials with overdispersed count data.