DOI: 10.3886/icpsr39521
title: Causal Inference for Effectiveness Research in Using Secondary Data [Methods Study], 2013-2018
title: Archival Version

description: Comparative effectiveness research compares two or more treatments to see which one works better for which patients. Electronic healthcare data are useful for this type of research. These data come from medical records and insurance claims. The data include information about how well patients respond to treatments. But many things--not just treatments--affect whether a patient's health improves.
 How well a patient responds to a treatment may depend on the patient's age or what medicines the patient takes. It could also depend on what other health problems a patient has and how severe those problems are. Or a doctor may suggest one treatment instead of another because of a patient's personal situation and health. Researchers need ways to determine whether changes in a patient's health result from a certain treatment or something else.
 Different statistical methods help researchers account for the various things that can affect treatment results. But researchers don't know which methods work best. This study compared several methods. The team looked at how well the methods worked to predict patients' responses to treatment, taking into account their personal situations and health. The team then created a computer program to help researchers use the methods.
 To access the methods and software, please visit the
Hdps GitHub and TargetedLearning GitHub.
description: Analyzing data from electronic healthcare databases, such as electronic health records, to gain generalizable knowledge of the effectiveness of medical interventions in routine care can improve patient care and outcomes, particularly for populations that are often excluded from randomized trials. However, researchers underuse these data for generating evidence on treatment effects, in part because of concerns about bias. Bias may arise in the data because clinicians selectively base their prescribing decisions on such factors as disease severity and patient prognosis. Current approaches to minimize such bias rely on the investigator to specify all potential confounding factors. New analytic approaches propose using algorithms to maximize control of confounding factors. However, researchers do not know how well these algorithms perform when applied to electronic healthcare data, particularly for special populations and small samples. Further, researchers have lacked readily available software to facilitate use of the algorithms.
This study evaluated the performance of several algorithms for variable selection, propensity score (PS) estimation, and causal inference. Researchers performed simulations using the plasmode framework, which combines simulated and empirical data to more accurately reflect complex relations that typically exist among baseline covariates. The research team then used three healthcare data sets in conjunction with plasmode simulations to evaluate the ability of each algorithm to effectively control for confounding. The team considered different scenarios by varying outcome incidence, treatment prevalence, sample size, and treatment effect. To help researchers use the algorithms, the team developed software and accompanying guidance.
During the study, the research team met with patient representatives, who identified key problems they encounter in healthcare delivery and provided input on what potential research questions are of greatest interest.
description: Datasets: 
DS1: Causal Inference for Effectiveness Research in Using Secondary Data [Methods Study], 2013-2018