Personalized treatment decisions, which hold great potential for improving health outcomes for patients of varying characteristics, involve the assessment and estimation of individualized or conditional treatment effects.
However, using data to make these decisions in a reliable, precise, and generalizable way is challenging when sample sizes are low and/or are not representative of the target population. Leveraging the information provided by multiple studies with unconfounded treatment assignment allows for combining datasets to improve the power to estimate heterogeneous treatment effects.
In this website we provide a repository of resources, including relevant literature and newly developed tools, designed to examine treatment effect heterogeneity when data from multiple sources is available, leveraging on recent machine learning methods.
Articles we have published, developing and demonstrating novel methods to assess treatment effect heterogeneity when combining data from multiple sources, include:
- Lupton Branter et al. (2023). Comparing Machine Learning Methods for Estimating Heterogeneous Treatment Effects by Combining Data from Multiple Randomized Controlled Trials.
- Lupton Branter, C., Chang, T-Y., Hong, H., Di Stefano, L., Nguyen, T.Q., and Stuart,E.A. (in press). Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity. Forthcoming in Statistical Science.
- [Extensive HTE Literature review] (Lupton et al., Forthcoming)
Software tools we have developed include:
CATEDisplays: a new R package designed to seamlessly 1) estimate conditional average treatment effect (CATE) coming from one or more sites and/or trials, 2) test for treatment effect heterogeneity, 3) visualize results. This package is mainly designed for researchers evaluating evidence coming from multiple trials and interested in potentially differential treatments effects across different subpopulations, and includes a sample tutorial and full documentation.
CATE MultRCT: a public repository including R code for performing machine learning methods that estimate the conditional average treatment effect (CATE) by combining data from multiple RCTs.