DOI: 10.3886/icpsr39118
title: Improving Trial Design and Analysis for Treatments for Rare Diseases [Methods Study], 2020
title: Archival Version

description: 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 small n sequential multiple assignment randomized trial (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
Efficiency, or how big the difference is between the results and actual treatment effectiveness
This study contains two supplementary documentation files. There is no data included in this release.
description: The purpose of this study was to develop new methods for designing and analyzing small n sequential multiple assignment randomized trial (snSMART) studies that increase efficiency and have low bias.
description: The research team created test data sets with 45-300 patients that looked like data from an small n sequential multiple assignment randomized trial (snSMART) study. Using this 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).
Users should refer to the Related Publications for additional technical information regarding the tested statistical methods and overall study design.
description: This study included simulated data created with sample sizes between 45 and 300 patients.
description: Datasets: 
DS1: Improving Trial Design and Analysis for Treatments for Rare Diseases [Methods Study], 2020
description: Users should refer to the Related Publications for additional technical information regarding the tested statistical methods and overall study design.