The calibration of treatment effects from clinical trials to target populations

By C. Frangakis. 2009

54 citations recorded by the source corpus.

Standard practice teaches that randomized clinical trials should be preferred to observational studies if the aim is to obtain unbiased estimates of efficacy. It is often acknowledged that treatment efficacy might differ in the general population than in the clinical trial, but the source of those differences is often poorly understood or not regarded as important. Substantive differences between RCT and observational study results are typically ascribed to confounding of the latter. Recent literature [1,2] suggests that differences between the results seen in an RCT and in an observational study can be larger than expected not because of confounding, but because of differences between their respective patient populations. These efficacy estimates can differ enough to change treatment recommendations. The paper by Weisberg et al. in this issue [3] is a very clear and innovative example of such emerging literature. But there is another important message that one should derive from this work. That is, RCT results are useful only if we can calibrate their results to predict treatment efficacy in the target population of interest. This editorial makes the case for why calibration requires both clinical knowledge from observational studies, and new statistical insights.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial. (2010, background): “Here, we did not attempt to account for postrandomization variables (12), but such steps may be important especially when the intent-to-treat estimate is subject to nontrivial bias due to noncompliance (49, 50).”
A Review of Generalizability and Transportability (2021, background): “When target and study populations differ on posttreatment variables such as adherence, principal stratification can be used to estimate PATEs by classifying subjects into never-taker, always-taker, and complier categories (Frangakis, 2009).”
Best Care at Lower Cost: The Path to Continuously Learning Health Care in America (2013, background): “practice and many patient populations (Frangakis, 2009; Greenhouse et al., 2008; Stewart et al., 2007; Weisberg et al., 2009).”