Wei L, Phillippo DM, Shah A, Cleland JGF, Lewsey J, McAllister DA. Transportability of two heart failure trials to a disease registry using individual patient data.
J Clin Epidemiol 2023;
162:160-168. [PMID:
37659583 DOI:
10.1016/j.jclinepi.2023.08.019]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 09/04/2023]
Abstract
OBJECTIVES
Randomized controlled trials are the gold-standard for determining therapeutic efficacy, but are often unrepresentative of real-world settings. Statistical transportation methods (hereafter transportation) can partially account for these differences, improving trial applicability without breaking randomization. We transported treatment effects from two heart failure (HF) trials to a HF registry.
STUDY DESIGN AND SETTING
Individual-patient-level data from two trials (Carvedilol or Metoprolol European Trial (COMET), comparing carvedilol and metoprolol, and digitalis investigation group trial (DIG), comparing digoxin and placebo) and a Scottish HF registry were obtained. The primary end point for both trials was all-cause mortality; composite outcomes were all-cause mortality or hospitalization for COMET and HF-related death or hospitalization for DIG. We performed transportation using regression-based and inverse odds of sampling weights (IOSW) approaches.
RESULTS
Registry patients were older, had poorer renal function and received higher-doses of loop-diuretics than trial participants. For each trial, point estimates were similar for the original and IOSW (e.g., DIG composite outcome: OR 0.75 (0.69, 0.82) vs. 0.73 (0.64, 0.83)). Treatment effect estimates were also similar when examining high-risk (0.64 (0.46, 0.89)) and low-risk registry patients (0.73 (0.61, 0.86)). Similar results were obtained using regression-based transportation.
CONCLUSION
Regression-based or IOSW approaches can be used to transport trial effect estimates to patients administrative/registry data, with only moderate reductions in precision.
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