Developing and Testing New Methods for Estimating Treatment Effectiveness in Observational Studies Using High-Dimensional Data [Methods Study], 2023 (ICPSR 39090)
Propensity scores (PS) and instrumental variables (IV) are methods used to assess treatment effects in observational studies when randomized controlled trials (RCTs) are not feasible. However, these methods have limitations, especially when using high-dimensional data, or data with numerous variables or many non-linear and interaction terms. Choices on which variables and non-linear and interaction terms to include may lead to model misspecification. The objective of this study was to develop and test a set of PS and IV methods that account for model misspecification when estimating causal effects of treatments using high-dimensional data.
First, the research team created the two new methods for use with high-dimensional data. The team then used a computer program to create test data that look like real patient data. The team applied the new methods to the test data. Next, the research team applied the new methods to real data from previous studies. They applied the PS method to data from Connors et al. (1996) and applied the IV method to data used by Card (1995). Using both test and real data, the research team compared findings from the new methods with those from existing PS and IV methods and checked to see if findings from the new methods were accurate when including different patient traits and health conditions in the analysis.
This collection contains the R software package RCAL 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, created April 24, 2022 (Windows, r-oldrel)
- For R version 4.3, created October 20, 2023 (Windows, r-release)
- For R version 4.4, created March 14, 2024 (Windows, r-devel)
- For R version 4.2, created April 1, 2023 (Mac, arm64, r-oldrel)
- For R version 4.3, created April 6, 2023 (Mac, arm64, r-release)
- For R version 4.3, created April 11, 2023 (Mac, x86_64, r-release)
Learning Within Health Care Delivery Systems: Design, Analysis, and Interpretation of Longitudinal Cluster Randomized Trials [Methods Study], 2023 (ICPSR 39089)
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)
Randomize Everyone: Creating Valid Instrumental Variables for Learning Health Care Systems [Methods Study], New Hampshire, 2016-2022 (ICPSR 39717)
Comparative effectiveness research, or CER, compares two or more treatments. In some CER studies, researchers use patient data from electronic health records, or EHRs, to compare treatments. But patient traits like age may affect doctors' and patients' choice of treatments, which can bias results. Using EHR systems to identify eligible patients and assign them to treatments by chance could improve results of CER studies that use EHR data.
In this study, the research team explored the views of patients, clinic staff, and clinicians, such as doctors or nurses, on doing CER studies in clinics. The team also tested software with a widely used EHR system. The software finds patients who qualify for a study. During a clinic visit, the software prompts doctors to invite patients to take part in the study. If patients agree, the software assigns patients by chance to a treatment.