@article{ref1,
title="Propensity score methods for analyzing observational data like randomized experiments: challenges and solutions for rare outcomes and exposures",
journal="American journal of epidemiology",
year="2015",
author="Ross, Michelle E. and Kreider, Amanda R. and Huang, Yuan-Shung and Matone, Meredith and Rubin, David M. and Localio, A. Russell",
volume="181",
number="12",
pages="989-995",
abstract="Randomized controlled trials are the &quot;gold standard&quot; 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.<p /> <p>Language: en</p>",
language="en",
issn="0002-9262",
doi="10.1093/aje/kwu469",
url="http://dx.doi.org/10.1093/aje/kwu469"
}