Design and Methodological Improvements for Patient-Centered Small n Sequential Multiple Assignment Randomized Trials (snSMARTs) in the Setting of Rare Diseases [Methods Study], 2016-2020 (ICPSR 39636)

Version Date: Dec 16, 2025

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Principal Investigator(s):
Kelley Kidwell, University of Michigan

https://doi.org/10.3886/ICPSR39636.v1

Version V1

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Summary

A rare disease is one that affects fewer than 200,000 people in the United States. Because few people have these diseases, clinical studies on treatments can be hard to conduct. One way to study rare disease treatments is with an snSMART study.

snSMART studies have two stages. In the first stage, researchers assign patients to a treatment by chance. In the second stage, patients may stay with the same treatment or switch treatments. Patients stay on the same treatment if it's working well. If the treatment isn't working, researchers assign patients by chance to a new treatment.

snSMARTs can help researchers learn more from a smaller number of patients than a standard clinical study. But most current methods for analyzing snSMARTs use data only from the first stage, which can lead to inefficient results.

In this project, the research team developed and tested new methods that use data from both stages to analyze snSMARTs. The team compared results from the new methods to actual treatment effectiveness to see Bias, or whether results are too high or too low effficiency, or how big the difference is between the results and actual treatment effectiveness

To access the software, please visit the snSMART Sample Size App.

Citation

Kidwell, Kelley. Design and Methodological Improvements for Patient-Centered Small n Sequential Multiple Assignment Randomized Trials (snSMARTs) in the Setting of Rare Diseases [Methods Study], 2016-2020. Inter-university Consortium for Political and Social Research [distributor], 2025-12-16. https://doi.org/10.3886/ICPSR39636.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-1507-31108)

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 new methods for designing and analyzing snSMARTs that increase efficiency and have low bias

Study Design

Using data from both stages of a snSMART studying three treatments, researchers developed two models:

  • Bayesian joint stage model (BJSM)
  • Log Poisson joint stage model (LPJSM)

The models estimate the best first stage treatment based on response rates or the percentage of patients who responded to each of three treatments, from both stages. Researchers compared simulation estimates from the new models with two models that relied on data from only the first stage:

  • Bayesian first stage model (BFSM)
  • First stage maximum likelihood estimates (FSMLE)

To estimate response rates for treatment sequences, researchers extended the new models to allow for the second stage treatment effect to depend on the first stage treatment

  • BJSM with multiple linkage parameters (BJSMM)
  • LPJSM with multiple parameters (LPJSMM)
  • To identify the optimal treatment sequence, researchers compared BJSMM and LPJSMM with an existing method, weighted and replicated regression method (WRRM).

Clinicians who treat rare disease, one patient with a rare disease, and one family member gave input on the study.

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Simulated data

Data Source

Simulated data created with sample sizes between 45 and 300 patients

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Original Release Date

2025-12-16

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Notes

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