The Propensity Score
By J. Haukoos, R. Lewis. Journal of the American Medical Association (JAMA), 2015
904 citations recorded by the source corpus.
Two recent studies published in JAMA involved the analysis of observational data to estimate the effect of a treatment on patient outcomes. In the study by Roze et al,1 a large observational data set was analyzed to estimate the relationship between early echocardiography screening for patent ductus arteriosus and mortality among preterm infants. The authors compared mortality rates of 847 infants who were screened for patent ductus arteriosus and 666 who were not. The 2 infant groups were dissimilar; infants who were screened were younger, more likely female, and less likely to have received corticosteroids. The authors used propensity score matching to create 605 matched infant pairs from the original cohort to adjust for these differences. In the study by Huybrechts et al,2 the Medicaid Analytic eXtract data set was analyzed to estimate the association between antidepressant use during pregnancy and persistent pulmonary hypertension of the newborn. The authors included 3 789 330 women, of which 128 950 had used antidepressants. Women who used antidepressants were different from those who had not, with differences in age, race/ethnicity, chronic illnesses, obesity, tobacco use, and health care use. The authors adjusted for these differences using, in part, the technique of propensity score stratification.
Subfields: Economics and Econometrics, Speech and Hearing
View Paper: https://www.semanticscholar.org/paper/ef66a4030083b85b3add912734dcebe4e8e10138
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In Economics and Econometrics
- Ellen Verhofstadt
- M. Maertens
- David M. Blei
- Yixin Wang
- Peter C. Austin
- N. Cuong
- Jo Swinnen
- Awudu Abdulai
In Speech and Hearing
Citations in the corpus
- The Blessings of Multiple Causes (2018, methodology): “Imbens (2000) and Hirano and Imbens (2004) call this assumption weak unconfound-”
- The performance of different propensity score methods for estimating marginal hazard ratios (2007, background): “Rosenbaum has noted that propensity score methods allow one to estimate marginal, rather than conditional, effects [12].”
- The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments (2013, methodology): “First, propensity score methods are intended to provide estimates of marginal treatment effect [22].”
- Heterogeneous impact of cooperative membership on farmers’ welfare in Rwanda (2015, methodology): “Matching is done with replacement to assure that each treatment unit is matched to the control unit with the closest PS, wh ich reduces bias (Dehejia and Wahba, 2002).”