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Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm
Janharpreet Singh1, Sandro Gsteiger2, Lorna Wheaton3
1Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK. js929@leicester.ac.uk.
BMC Medical Research Methodology
|July 11, 2022
Summary
Incorporating single-arm trials (SATs) into network meta-analysis (NMA) can estimate treatment effects for disconnected networks. Adjusting for covariates improves the reliability of these estimates, though further research is needed.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Health Economics
Background:
- Network meta-analysis (NMA) increasingly requires non-randomized evidence to estimate relative treatment effects, especially for disconnected networks.
- Complex NMA methods are needed to address biases and synthesize diverse data sources like individual participant data (IPD) and aggregate data (AD).
- This study focuses on NMA methods that incorporate single-arm trials (SATs) and randomized controlled trials (RCTs) for dichotomous outcomes.
Purpose of the Study:
- To develop and present novel NMA methods for synthesizing data from SATs and RCTs, using a mixture of IPD and AD.
- To extend these methods to include covariate adjustments within and across trials.
- To illustrate the application of these methods using a case study on biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis.
Main Methods:
- Proposed contrast-based (CB) and arm-based (AB) parametrizations for NMA.
- Incorporated methods for within- and across-trial covariate adjustments.
- Applied methods to a rheumatoid arthritis dataset comprising 14 RCTs and an artificial dataset with IPD from two SATs and AD from 12 RCTs.
Main Results:
- The unadjusted CB method underestimated tocilizumab's effectiveness in the artificial dataset compared to the original, with overlapping posterior distributions.
- Exchangeable baseline response parameters yielded similar estimates to independent parameters when predicted and observed baseline responses aligned.
- Covariate adjustment for rheumatoid arthritis duration reduced heterogeneity but minimally impacted treatment effect estimates.
Conclusions:
- Incorporating SATs in NMA can be valuable for estimating relative treatment effects in disconnected networks lacking comparative evidence.
- The reliability of effect estimates derived from SATs may be enhanced by covariate adjustment, warranting further investigation.
Keywords:
Arm-based methodsBayesian hierarchical methodsEvidence synthesisIndividual participant dataNetwork meta-analysisObservational evidenceRheumatoid arthritisSingle-arm trialsMore Related Videos
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