TY  - JOUR
PY  - 2015//
TI  - Propensity score methods for analyzing observational data like randomized experiments: challenges and solutions for rare outcomes and exposures
JO  - American journal of epidemiology
A1  - Ross, Michelle E.
A1  - Kreider, Amanda R.
A1  - Huang, Yuan-Shung
A1  - Matone, Meredith
A1  - Rubin, David M.
A1  - Localio, A. Russell
SP  - 989
EP  - 995
VL  - 181
IS  - 12
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0002-9262
UR  - http://dx.doi.org/10.1093/aje/kwu469
ID  - ref1
ER  -