Expansion of Methods for Two-Stage Trial Designs for Testing Treatment, Self-Selection, and Treatment Preference Effects [Methods Study], 2016-2020 (ICPSR 39625)

Version Date: Dec 16, 2025

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Principal Investigator(s):
Denise Esserman, Yale University

https://doi.org/10.3886/ICPSR39625.v1

Version V1

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Summary

A patient's preference for a treatment may affect how well the treatment works. For example, if patients prefer a specific medicine, they may be more likely to take that medicine.

Traditional randomized clinical trials can't tell how much patient preferences affect how well a treatment works. But a two-stage clinical trial might. In a two-stage trial, researchers assign patients by chance to one of two groups. In the first group, researchers assign patients by chance to get a specific treatment, regardless of their preference. In the second group, patients choose their treatment. In a two-stage trial, researchers can compare health outcomes for patients who choose their treatment with patients who don't. But few methods exist for researchers to design and analyze this type of trial.

In this project, the research team developed new statistical methods for two-stage trials. The team wanted to find out how many patients are needed for two-stage trials to provide accurate results. They also wanted to learn how to measure whether patient preference for a specific treatment affects patients' health outcomes.

To access the software, methods and R package, please visit the preference CRAN webpage and preference GitHub.

Citation

Esserman, Denise. Expansion of Methods for Two-Stage Trial Designs for Testing Treatment, Self-Selection, and Treatment Preference Effects [Methods Study], 2016-2020. Inter-university Consortium for Political and Social Research [distributor], 2025-12-16. https://doi.org/10.3886/ICPSR39625.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-1511-32832)

Subject Terms

Distributor(s)

Inter-university Consortium for Political and Social Research
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Time Period(s)

2016 -- 2020
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Study Purpose

To develop statistical methods for estimating sample size and measuring treatment, selection, and preference effects for noncontinuous outcomes in two-stage clinical trials.

Study Design

The research team developed a binomial statistical model and a Poisson statistical model for analyzing different noncontinuous outcomes. In each model, the team developed estimators of the treatment, selection, and preference effects for both small and large sample sizes and derived sample size formula.

To test the performance and properties of the statistical methods, the research team created simulated data by varying the rate of preference for a treatment, rate of treatment response, and sample size. The team also applied the new methods to calculate sample sizes and treatment effects using two two-stage trials with noncontinuous treatment outcomes as test examples.

Statisticians and researchers with expertise in clinical trial design, statistical methods, and statistical programs helped develop and test the methods.

Data Source

Simulated data

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Original Release Date

2025-12-16

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Notes

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