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 multiply imputed missing baseline characteristics for the CPRD population and selected the trial-eligible participants as the target population based on observed and imputed values. Trial participants were weighted to be representative of the CPRD population (n = 383,418) based on individual predicted probability of selection into the trial. Trial participants were also standardized to the CPRD population without missing values (n = 2,677). In JUPITER, rosuvastatin reduced cardiovascular risk with a 3-year risk difference of -2.0% (95% confidence interval (CI): -2.9, -1.1). The rosuvastatin effect was muted in the first 2 years but remained strong at 3 years after standardizing to the imputed CPRD population (3-year risk difference = -2.7%; 95% CI: -5.8, 0.4) and the CPRD population without missing data (3-year risk difference = -1.7%; 95% CI: -3.5, 0.1). The study serves as an illustration of possible approaches to understanding generalizability of trials using real-world databases given limitations due to missing data on inclusion/exclusion criteria.

A standardization approach to compare treatment safety and effectiveness outcomes between clinical trials and real‐world populations in psoriasis

“…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.…”

“…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 .…”

Body mass index, calcium supplementation and risk of colorectal adenomas

“…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 …”

“…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.…”

An outcome model approach to transporting a randomized controlled trial results to a target population

“…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.…”