Joint incorporation of randomised and observational evidence in estimating treatment effects. (January 2019)

• Record Type: Journal Article
• Authors: Ferguson, John; Alvarez-Iglesias, Alberto; Newell, John; Hinde, John; O' Donnell, Martin
• Is Part Of: Statistical methods in medical research. Volume 28:Number 1(2019)
• Page Start: 235
• Page End: 247
• Publication Date: 2019-01
• Subjects: Observational study -- randomised trial -- meta-analysis -- root mean square error -- parametric bootstrap; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217720854
• Languages: English
• ISSNs: 0962-2802

Abstract

In evidence-based medicine, randomised trials are regarded as a gold standard in estimating relative treatment effects. Nevertheless, a potential gain in precision is forfeited by ignoring observational evidence. We describe a simple estimator that combines treatment estimates from randomised and observational data and investigate its properties by simulation. We show that a substantial gain in estimation accuracy, compared with the estimator based solely on the randomised trial, is possible when the observational evidence has low bias and standard error. In the contrasting scenario where the observational evidence is inaccurate, the estimator automatically discounts its contribution to the estimated treatment effect. Meta-analysis extensions, combining estimators from multiple observational studies and randomised trials, are also explored.