Comparison of Methods to Generalize Randomized Clinical Trial Results Without Individual-Level Data for the Target Population. Issue 2 (11th October 2018)

• Record Type: Journal Article
• Authors: Hong, Jin-Liern; Webster-Clark, Michael; Jonsson Funk, Michele; Stürmer, Til; Dempster, Sara E; Cole, Stephen R; Herr, Iksha; LoCasale, Robert
• Is Part Of: American journal of epidemiology. Volume 188:Issue 2(2019)
• Page Start: 426
• Page End: 437
• Publication Date: 2018-10-11
• Subjects: cardiovascular diseases -- external validity -- generalizability -- JUPITER trial -- randomized clinical trial -- statins; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwy233
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Our study explored the application of methods to generalize randomized controlled trial results to a target population without individual-level data. We compared 4 methods using aggregate data for the target population to generalize results from the international trial, Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER), to a target population of trial-eligible patients in the UK Clinical Practice Research Datalink (CPRD). The gold-standard method used individual data from both the trial and CPRD to predict probabilities of being sampled in the trial and to reweight trial participants to reflect CPRD patient characteristics. Methods 1 and 2 used weighting methods based on simulated individual data or the method of moments, respectively. Method 3 weighted the trial's subgroup-specific treatment effects to match the distribution of an effect modifier in CPRD. Method 4 calculated the expected absolute benefits in CPRD assuming homogeneous relative treatment effect. Methods based on aggregate data for the target population generally yielded results between the trial and gold-standard estimates. Methods 1 and 2 yielded estimates closest to the gold-standard estimates when continuous effect modifiers were represented as categorical variables. Although individual data or data on joint distributions remains the best approach to generalize trial results, these methods using aggregate data might be useful tools for timely assessment of randomized trial generalizability.