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Zhang L, Bujkiewicz S, Jackson D. Four alternative methodologies for simulated treatment comparison: How could the use of simulation be re-invigorated? Res Synth Methods 2024; 15:227-241. [PMID: 38104969 DOI: 10.1002/jrsm.1681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/23/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023]
Abstract
Simulated treatment comparison (STC) is an established method for performing population adjustment for the indirect comparison of two treatments, where individual patient data (IPD) are available for one trial but only aggregate level information is available for the other. The most commonly used method is what we call 'standard STC'. Here we fit an outcome model using data from the trial with IPD, and then substitute mean covariate values from the trial where only aggregate level data are available, to predict what the first of these trial's outcomes would have been if its population had been the same as the second. However, this type of STC methodology does not involve simulation and can result in bias when the link function used in the outcome model is non-linear. An alternative approach is to use the fitted outcome model to simulate patient profiles in the trial for which IPD are available, but in the other trial's population. This stochastic alternative presents additional challenges. We examine the history of STC and propose two new simulation-based methods that resolve many of the difficulties associated with the current stochastic approach. A virtue of the simulation-based STC methods is that the marginal estimands are then clearly targeted. We illustrate all methods using a numerical example and explore their use in a simulation study.
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Affiliation(s)
- Landan Zhang
- Statistical Innovation, AstraZeneca, Cambridge, UK
| | - Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Dan Jackson
- Statistical Innovation, AstraZeneca, Cambridge, UK
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Bujkiewicz S, Singh J, Wheaton L, Jenkins D, Martina R, Hyrich KL, Abrams KR. Bridging disconnected networks of first and second lines of biologic therapies in rheumatoid arthritis with registry data: bayesian evidence synthesis with target trial emulation. J Clin Epidemiol 2022; 150:171-178. [PMID: 35850425 DOI: 10.1016/j.jclinepi.2022.06.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 05/27/2022] [Accepted: 06/20/2022] [Indexed: 10/17/2022]
Abstract
OBJECTIVES We aim to use real-world data in evidence synthesis to optimize an evidence base for the effectiveness of biologic therapies in rheumatoid arthritis to allow for evidence on first-line therapies to inform second-line effectiveness estimates. STUDY DESIGN AND SETTING We use data from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis to supplement randomized controlled trials evidence obtained from the literature, by emulating target trials of treatment sequences to estimate treatment effects in each line of therapy. Treatment effects estimates from the target trials inform a bivariate network meta-analysis (NMA) of first-line and second-line treatments. RESULTS Summary data were obtained from 21 trials of biologic therapies including two for second-line treatment and results from six emulated target trials of both treatment lines. Bivariate NMA resulted in a decrease in uncertainty around the effectiveness estimates of the second-line therapies, when compared to the results of univariate NMA, and allowed for predictions of treatment effects not evaluated in second-line randomized controlled trials. CONCLUSION Bivariate NMA provides effectiveness estimates for all treatments in first and second line, including predicted effects in second line where these estimates did not exist in the data. This novel methodology may have further applications; for example, for bridging networks of trials in children and adults.
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Affiliation(s)
- Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK.
| | - Janharpreet Singh
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | - Lorna Wheaton
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | - David Jenkins
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK; Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Reynaldo Martina
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK
| | - Kimme L Hyrich
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK; Versus Arthritis Centre for Epidemiology, Centre for Musculoskeletal Research, The University of Manchester, Manchester, M13 9PL, UK
| | - Keith R Abrams
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK; Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK; Centre for Health Economics, University of York, York, YO10 5DD, UK
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