Learning Within Health Care Delivery Systems: Design, Analysis, and Interpretation of Longitudinal Cluster Randomized Trials [Methods Study], 2023

Cluster randomized trials, or CRTs, are research studies that compare treatments among different groups of patients, or clusters. An example of a cluster is a group of people who receive care at a single clinic. One type of CRT is a stepped-wedge CRT. These CRTs compare patients' health before and after a new treatment. In stepped-wedge CRTs, all groups start with the standard treatment. Then, each group switches to the new treatment at a specific time during the study. By the end of the study, all groups are receiving the new treatment. In stepped-wedge CRTs, group characteristics, such as how clinics follow up with patients, can affect how well a treatment works. It is hard to figure out if changes in a patient's health are due to the treatment or group characteristics. In this study, the research team wanted to improve how to plan and analyze stepped-wedge CRTs for studying the effect of treatments.
The study had two parts. In the first part, the research team looked at ways to measure how well treatments work in stepped-wedge CRTs in ways that account for group characteristics. In the second part, the research team looked at which statistical methods got accurate results when using data from stepped-wedge CRTs. The team first used a computer program to create test data that looked like data from a stepped-wedge CRT. The team created the test data using nine scenarios; each scenario had a different set of conditions. For example, the number of patient groups varied across each scenario. Using the test data, the team compared six statistical methods for analyzing data from stepped-wedge CRTs. The research team also created a statistical program to help plan and analyze stepped-wedge CRTs.
This collection contains the R software package swCRTdesign and accompanying documentation. The package source as a .tar.gz file and six different versions are available in a zipped package. Files have been released as received by ICPSR from the depositor:

For R version 4.2.3, created March, 11, 2024 (Windows)
For R version 4.3.3, created March, 10, 2024 (Windows)
For R version 4.4.0, created March, 11, 2024 (Windows)
For R version 4.2.0, created August, 27, 2023 (macOS)
For R version 4.3.0, created August, 26, 2023 (macOS)
For R version 4.3.0, created August, 27, 2023 (macOS)