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Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm trials and randomised controlled trials
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Singh, Janharpreet, Gsteiger, Sandro, Wheaton, Lorna, Riley, Richard D., Abrams, Keith R., Gillies, Clare L. and Bujkiewicz, Sylwia (2022) Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm trials and randomised controlled trials. BMC Medical Research Methodology, 22 (1). 186. doi:10.1186/s12874-022-01657-y ISSN 1471-2288.
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WRAP-Bayesian-network meta-analysis-methods-combining-individual-participant-data-aggregate-data-from-single-arm-trials-randomised-controlled-trials-Abrams-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (890Kb) | Preview |
Official URL: https://doi.org/10.1186/s12874-022-01657-y
Abstract
Background: Increasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometimes resulting in disconnected networks of treatments. When combining different sources of data, complex NMA methods are required to address issues associated with participant selection bias, incorporating single-arm trials (SATs), and synthesising a mixture of individual participant data (IPD) and aggregate data (AD). We develop NMA methods which synthesise data from SATs and randomised controlled trials (RCTs), using a mixture of IPD and AD, for a dichotomous outcome. Methods: We propose methods under both contrast-based (CB) and arm-based (AB) parametrisations, and extend the methods to allow for both within- and across-trial adjustments for covariate effects. To illustrate the methods, we use an applied example investigating the effectiveness of biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis (RA). We applied the methods to a dataset obtained from a literature review consisting of 14 RCTs and an artificial dataset consisting of IPD from two SATs and AD from 12 RCTs, where the artificial dataset was created by removing the control arms from the only two trials assessing tocilizumab in the original dataset. Results: Without adjustment for covariates, the CB method with independent baseline response parameters (CBunadjInd) underestimated the effectiveness of tocilizumab when applied to the artificial dataset compared to the original dataset, albeit with significant overlap in posterior distributions for treatment effect parameters. The CB method with exchangeable baseline response parameters produced effectiveness estimates in agreement with CBunadjInd, when the predicted baseline response estimates were similar to the observed baseline response. After adjustment for RA duration, there was a reduction in across-trial heterogeneity in baseline response but little change in treatment effect estimates. Conclusions: Our findings suggest incorporating SATs in NMA may be useful in some situations where a treatment is disconnected from a network of comparator treatments, due to a lack of comparative evidence, to estimate relative treatment effects. The reliability of effect estimates based on data from SATs may depend on adjustment for covariate effects, although further research is required to understand this in more detail.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | R Medicine > R Medicine (General) | |||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||||||||
| SWORD Depositor: | Library Publications Router | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Evidence-based medicine -- Statistical methods, Meta-analysis, Clinical trials, Drugs -- Testing -- Statistical methods, Rheumatoid arthritis, Bayesian statistical decision theory | |||||||||||||||
| Journal or Publication Title: | BMC Medical Research Methodology | |||||||||||||||
| Publisher: | BioMed Central Ltd. | |||||||||||||||
| ISSN: | 1471-2288 | |||||||||||||||
| Official Date: | 11 July 2022 | |||||||||||||||
| Dates: |
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| Volume: | 22 | |||||||||||||||
| Number: | 1 | |||||||||||||||
| Article Number: | 186 | |||||||||||||||
| DOI: | 10.1186/s12874-022-01657-y | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Date of first compliant deposit: | 3 August 2022 | |||||||||||||||
| Date of first compliant Open Access: | 9 August 2022 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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