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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Synthetic Negative Controls: Using Simulation to Screen Large-scale Propensity Score Analyses
Epidemiology, Volume 33, No. 4, Year 2022
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Description
The propensity score has become a standard tool to control for large numbers of variables in healthcare database studies. However, little has been written on the challenge of comparing large-scale propensity score analyses that use different methods for confounder selection and adjustment. In these settings, balance diagnostics are useful but do not inform researchers on which variables balance should be assessed or quantify the impact of residual covariate imbalance on bias. Here, we propose a framework to supplement balance diagnostics when comparing large-scale propensity score analyses. Instead of focusing on results from any single analysis, we suggest conducting and reporting results for many analytic choices and using both balance diagnostics and synthetically generated control studies to screen analyses that show signals of bias caused by measured confounding. To generate synthetic datasets, the framework does not require simulating the outcome-generating process. In healthcare database studies, outcome events are often rare, making it difficult to identify and model all predictors of the outcome to simulate a confounding structure closely resembling the given study. Therefore, the framework uses a model for treatment assignment to divide the comparator population into pseudo-treatment groups where covariate differences resemble those in the study cohort. The partially simulated datasets have a confounding structure approximating the study population under the null (synthetic negative control studies). The framework is used to screen analyses that likely violate partial exchangeability due to lack of control for measured confounding. We illustrate the framework using simulations and an empirical example. © 2022 Lippincott Williams and Wilkins. All rights reserved.
Authors & Co-Authors
Schneeweiß, Sebastian Gordian Gordian
United States, Boston
Harvard Medical School
Kalilani-Phiri, Linda V.
Belgium, Braine-l'alleud
Ucb S.a.
Statistics
Citations: 1
Authors: 2
Affiliations: 3
Identifiers
Doi:
10.1097/EDE.0000000000001482
ISSN:
10443983
Research Areas
Health System And Policy
Study Design
Cross Sectional Study
Cohort Study