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 Rozé et al,1 a large observational data set was analyzed to estimate the relationship between early echocardiographic 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.
Haukoos JS, Lewis RJ. The Propensity Score. JAMA. 2015;314(15):1637–1638. doi:10.1001/jama.2015.13480
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