DOI: 10.3886/icpsr39527
title: Innovative Randomized Trial Designs to Generate Stronger Evidence about Subpopulation Benefits and Harms [Methods Study], 2013-2018
title: Archival Version

description: Research studies called clinical trials test treatments to see if they are safe and effective for patients. When designing clinical trials, researchers must plan to include enough patients with different traits for the study to have accurate results. Once the study starts, researchers must follow the plan. Sometimes, early results from a trial show that a group of patients with a certain trait may have more benefits or harms from the treatment than other groups. For example, the treatment may not work for patients with a history of heart disease. In the standard trial design, researchers can't change the plan to stop enrolling these patients once the trial starts.
In this study, the research team compared the standard trial design with more flexible approaches known as adaptive enrichment designs. These designs set up rules that allow researchers to change the study plan. For example, if early results show a treatment doesn't work for patients with heart disease, researchers can stop enrolling these patients in the trial. The team compared the trial designs using data from four completed trials.
To access the methods and software, please visit the AdaptiveDesignStreamlinedOptimizer GitHub.
description: The research aimed to (i) develop new adaptive enrichment designs and prove their key statistical properties; (ii) conduct simulations that mimic features of completed trial data sets in order to evaluate the new trial designs' performance (such as sample size, duration, power, bias); the data sets are from trials involving treatments for HIV, stroke, and heart failure; (iii) develop user-friendly software for optimizing the performance of our new adaptive designs and comparing them to standard designs. The goal was to construct designs that satisfy power and type I error requirements at the minimum cost in terms of expected sample size, ie, average sample size over a set of plausible scenarios.
description: Standard designs for randomized controlled trials (RCTs) typically exhibit reduced statistical power to detect if a treatment differentially benefits or harms a subpopulation of study participants. Adaptive enrichment designs can address this issue by including rules for changing the trial protocol based on interim data, thus preserving a study's ability to detect these effects.
In this study, researchers developed new adaptive enrichment designs to optimize sample size for use in clinical trials examining time-to-event and other delayed outcomes. To reduce error and improve the precision of the analysis results, researchers also explored the addition of new prognostic baseline variables, which are characteristics that correlate with the progression or resolution of a disease.
Researchers evaluated new adaptive enrichment designs in three simulation RCT data sets and assessed the value of prognostic baseline variables in one simulation RCT data set. They compared the performance of these new designs to standard designs considering

Maximum sample size, which is the number of participants in the study if enrollment does not stop
Average sample size, which is the average number of participants enrolled
Precision gain, which is the improvement in estimating the population-level treatment effect due to adjusting for chance imbalances between study arms in baseline variables

Then the researchers developed a free, open source software program to assist researchers in designing clinical trials. Users can input their own study scenarios and performance criteria into the program to evaluate and compare numerous standard and adaptive enrichment design choices. The program works with clinical trials that have continuous, binary, or time-to-event primary outcomes.
description: Datasets: 
DS1: Innovative Randomized Trial Designs to Generate Stronger Evidence about Subpopulation Benefits and Harms [Methods Study], 2013-2018