Developing and Testing New Methods for Estimating Treatment Effectiveness in Observational Studies Using High-Dimensional Data [Methods Study], 2023

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)