2017
DOI: 10.1093/aje/kwx287
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Generalizing Randomized Clinical Trial Results: Implementation and Challenges Related to Missing Data in the Target Population

Abstract: Statins are indicated in patients with elevated levels of high-sensitivity C-reactive protein and normal low-density lipoprotein cholesterol based on results of the multicountry trial, Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) (2003-2008), but the benefit in real-world populations remains unknown. We sought to generalize JUPITER results to trial-eligible population using data from the UK Clinical Practice Research Datalink (CPRD), 2001-2014. We … Show more

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Cited by 27 publications
(28 citation statements)
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“…Model-based standardization methods have increasingly been used to estimate the gap between RCTs and real-world populations. 8,9 In contrast to common methods of standardization, such as age-or sex-standardized rates, model-based standardization allows the standardization of a population to many different covariates. 8 A weighting approach, based on predicting an individual's probability of being a trial participant as a function of his or her baseline characteristics, can achieve this standardization and therefore has the potential to inform this gap.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Model-based standardization methods have increasingly been used to estimate the gap between RCTs and real-world populations. 8,9 In contrast to common methods of standardization, such as age-or sex-standardized rates, model-based standardization allows the standardization of a population to many different covariates. 8 A weighting approach, based on predicting an individual's probability of being a trial participant as a function of his or her baseline characteristics, can achieve this standardization and therefore has the potential to inform this gap.…”
Section: Discussionmentioning
confidence: 99%
“…Model‐based standardization methods have increasingly been used to estimate the gap between RCTs and real‐world populations . In contrast to common methods of standardization, such as age‐ or sex‐standardized rates, model‐based standardization allows the standardization of a population to many different covariates .…”
mentioning
confidence: 99%
“…Briefly, inverse odds of sampling weights use a form of model‐based standardization to transport the effects of an intervention in a given study population (in this case, the effects of calcium supplementation on adenoma risk in one of the two trials) to another target population (here, the other trial). These methods have been applied to a variety of randomized clinical trials when seeking to generalize or transport treatment effects to other target populations of interest …”
Section: Methodsmentioning
confidence: 99%
“…These methods have been applied to a variety of randomized clinical trials when seeking to generalize or transport treatment effects to other target populations of interest. [12][13][14] All analyses were conducted according to the intent-totreat principle regardless of participant adherence. Two-sided P values of <0.05 were considered to indicate statistical significance.…”
Section: Statistical Analysesmentioning
confidence: 99%
“…For these reasons, there has been increasing interest within the causal inference literature in developing methods to translate RCT results to target populations, estimating what is referred to as the target average treatment effect (TATE) (4)(5)(6)(7)(8)(9)(10)(11). The majority of this work has aimed to account for the selection process into the trial by developing weights using a model of the probability of a person being as part of the RCT versus the target population.…”
Section: Introductionmentioning
confidence: 99%