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Rare Outcomes, Common Treatments: Analytic Strategies Using Propensity Scores

Leonard E. Braitman, PhD; and Paul R. Rosenbaum, PhD
[+] Article, Author, and Disclosure Information

Grant Support: Dr. Rosenbaum is supported by grant SES-0004205 from the Methodology, Measurement and Statistics Program and the Statistics and Probability Program of the U.S. National Science Foundation.

Requests for Single Reprints: Leonard E. Braitman, PhD, Office for Research and Technology Development, Albert Einstein Medical Center, 5501 Old York Road, Philadelphia, PA 19141; e-mail, lbraitman@att.net.

Current Author Address: Dr. Braitman: Office for Research and Technology Development, Albert Einstein Medical Center, 5501 Old York Road, Philadelphia, PA 19141.

Dr. Rosenbaum: Department of Statistics, University of Pennsylvania, 400 Jon M. Huntsman Hall, 3730 Locust Walk, Philadelphia, PA 19104.

Ann Intern Med. 2002;137(8):693-695. doi:10.7326/0003-4819-137-8-200210150-00015
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When treated patients are compared to controls, differing outcomes may reflect either effects caused by the treatment or differences in prognosis before treatment. Random assignment of patients to treatment or control, as in a randomized, controlled clinical trial (1), ensures that the groups were comparable before treatment and the prognosis in treated and control groups was nearly the same, so that differing outcomes indicate treatment effects. Somewhat more precisely, random assignment ensures that the only differences in prognosis between groups are due to chance, the flip of a coin in assigning treatments. In an ideal randomized trial, if a common statistical test rejects the hypothesis that the difference in outcomes is due to chance, a treatment effect is demonstrated. Notice that randomization does nothing to make patients have individually similar prognoses; rather, it ensures that assignment to treatment or control is unrelated to prognosis.

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