Combining non-randomized and randomized data in clinical trials using commensurate priors

• Published in: Health services and outcomes research methodology Vol. 16; no. 3; pp. 154 - 171
• Main Authors: Zhao, Hong, Hobbs, Brian P., Ma, Haijun, Jiang, Qi, Carlin, Bradley P.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2016; Springer Nature B.V
• Subjects: Acquired immune deficiency syndrome; AIDS; Bayesian analysis; Bias; Clinical trials; Economics; Evidence-based medicine; Health Administration; HIV; Human immunodeficiency virus; Markov analysis; Medicine; Medicine & Public Health; Methodology of the Social Sciences; Monte Carlo simulation; Observational studies; Parameter estimation; Patients; Public Health; Random variables; Statistical analysis; Statistics; Task forces; Propensity score matching; Observational studies (OSs); Randomized clinical trials (RCTs); Bayesian analysis; Commensurate priors; Markov chain Monte Carlo (MCMC); commensurate priors; propensity score matching; randomized clinical trials (RCTs); observational studies (OSs)
• ISSN: 1387-3741; 1572-9400
• DOI: 10.1007/s10742-016-0155-7

Randomization eliminates selection bias, and attenuates imbalance among study arms with respect to prognostic factors, both known and unknown. Thus, information arising from randomized clinical trials (RCTs) is typically considered the gold standard for comparing therapeutic interventions in confirmatory studies. However, RCTs are limited in contexts wherein patients who are willing to accept a random treatment assignment represent only a subset of the patient population. By contrast, observational studies (OSs) often enroll patient cohorts that better reflect the broader patient population. However, OSs often suffer from selection bias, and may yield invalid treatment comparisons even after adjusting for known confounders. Therefore, combining information acquired from OSs with data from RCTs in research synthesis is often criticized due to the limitations of OSs. In this article, we combine randomized and non-randomized substudy data from FIRST, a recent HIV/AIDS drug trial. We develop hierarchical Bayesian approaches devised to combine data from all sources simultaneously while explicitly accounting for potential discrepancies in the sources’ designs. Specifically, we describe a two-step approach combining propensity score matching and Bayesian hierarchical modeling to integrate information from non-randomized studies with data from RCTs, to an extent that depends on the estimated commensurability of the data sources. We investigate our procedure’s operating characteristics via simulation. Our findings have implications for HIV/AIDS research, as well as elucidate the extent to which well-designed non-randomized studies can complement RCTs.