Cohort studies of treatments developed from healthcare claims often have hundreds of thousands of patients and up to several thousand measured covariates. Therefore, new causal inference methods that combine ideas from machine learning and causal inference may improve analysis of these studies by taking advantage of the wealth of information measured in claims. In order to evaluate the performance of these methods as applied to claims-based studies, we use a combination of real data examples and plasmode simulation, implemented in R package 'plasmode', which creates realistic simulated datasets based on a real cohort study. In this talk, I will give an overview of our progress so far and what is left to be done.
Jessica Myers Franklin
Harvard University
Evaluating the performance of advanced causal inference methods applied to healthcare claims data
Cohort studies of treatments developed from healthcare claims often have hundreds of thousands of patients and up to several thousand measured covariates. Therefore, new causal inference methods that combine ideas from machine learning and causal inference may improve analysis of these studies by taking advantage of the wealth of information measured in claims. In order to evaluate the performance of these methods as applied to claims-based studies, we use a combination of real data examples and plasmode simulation, implemented in R package 'plasmode', which creates realistic simulated datasets based on a real cohort study. In this talk, I will give an overview of our progress so far and what is left to be done.