Propensity Score Methods for Analyzing Observational Data Like Randomized Experiments: Challenges and Solutions for Rare Outcomes and Exposures.

TitlePropensity Score Methods for Analyzing Observational Data Like Randomized Experiments: Challenges and Solutions for Rare Outcomes and Exposures.
Publication TypeJournal Article
Year of Publication2015
AuthorsRoss ME, Kreider AR, Huang Y-S, Matone M, Rubin DM, A Localio R
JournalAm J Epidemiol
Volume181
Issue12
Pagination989-95
Date Published2015 Jun 15
ISSN1476-6256
KeywordsAdolescent, Antipsychotic Agents, Causality, Child, Confounding Factors, Epidemiologic, Data Interpretation, Statistical, Diabetes Mellitus, Type 2, Epidemiologic Research Design, Female, Humans, Intention to Treat Analysis, Longitudinal Studies, Male, Matched-Pair Analysis, Models, Statistical, Observational Studies as Topic, Propensity Score, Randomized Controlled Trials as Topic
Abstract

Randomized controlled trials are the "gold standard" for estimating the causal effects of treatments. However, it is often not feasible to conduct such a trial because of ethical concerns or budgetary constraints. We expand upon an approach to the analysis of observational data sets that mimics a sequence of randomized studies by implementing propensity score models within each trial to achieve covariate balance, using weighting and matching. The methods are illustrated using data from a safety study of the relationship between second-generation antipsychotics and type 2 diabetes (outcome) in Medicaid-insured children aged 10-18 years across the United States from 2003 to 2007. Challenges in this data set include a rare outcome, a rare exposure, substantial and important differences between exposure groups, and a very large sample size.

DOI10.1093/aje/kwu469
Alternate JournalAm. J. Epidemiol.
PubMed ID25995287
Grant List5R01HS018550 / HS / AHRQ HHS / United States