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Landan Zhang; Sylwia Bujkiewicz; Dan Jackson – Research Synthesis Methods, 2024
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…
Descriptors: Simulation, Patients, Outcomes of Treatment, Comparative Analysis
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Cheng, David; Tchetgen, Eric Tchetgen; Signorovitch, James – Research Synthesis Methods, 2023
Matching-adjusted indirect comparison (MAIC) enables indirect comparisons of interventions across separate studies when individual patient-level data (IPD) are available for only one study. Due to its similarity with propensity score weighting, it has been speculated that MAIC can be combined with outcome regression models in the spirit of…
Descriptors: Comparative Analysis, Robustness (Statistics), Intervention, Patients
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Shijie Ren; Sa Ren; Nicky J. Welton; Mark Strong – Research Synthesis Methods, 2024
Population-adjusted indirect comparisons, developed in the 2010s, enable comparisons between two treatments in different studies by balancing patient characteristics in the case where individual patient-level data (IPD) are available for only one study. Health technology assessment (HTA) bodies increasingly rely on these methods to inform funding…
Descriptors: Medical Research, Outcomes of Treatment, Standards, Safety
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Remiro-Azócar, Antonio; Heath, Anna; Baio, Gianluca – Research Synthesis Methods, 2022
Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate…
Descriptors: Patients, Medical Research, Comparative Analysis, Outcomes of Treatment
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Proctor, Tanja; Zimmermann, Samuel; Seide, Svenja; Kieser, Meinhard – Research Synthesis Methods, 2022
During drug development, a biomarker is sometimes identified as separating a patient population into those with more and those with less benefit from evaluated treatments. Consequently, later studies might be targeted, while earlier ones are performed in mixed patient populations. This poses a challenge in evidence synthesis, especially if only…
Descriptors: Comparative Analysis, Meta Analysis, Patients, Medical Research
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Phillippo, David M.; Dias, Sofia; Ades, A. E.; Welton, Nicky J. – Research Synthesis Methods, 2020
Indirect comparisons are used to obtain estimates of relative effectiveness between two treatments that have not been compared in the same randomized controlled trial, but have instead been compared against a common comparator in separate trials. Standard indirect comparisons use only aggregate data, under the assumption that there are no…
Descriptors: Comparative Analysis, Outcomes of Treatment, Patients, Randomized Controlled Trials
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Leahy, Joy; Walsh, Cathal – Research Synthesis Methods, 2019
If IPD is available for some or all trials in a network meta-analysis (NMA), then incorporating this IPD into an NMA is routinely considered to be preferable. However, the situation often arises where a researcher has IPD for trials concerning a particular treatment (eg, from a sponsor) but none for other trials. Therefore, one can reweight the…
Descriptors: Comparative Analysis, Meta Analysis, Bayesian Statistics, Network Analysis
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Brunton, Ginny; Webbe, James; Oliver, Sandy; Gale, Chris – Research Synthesis Methods, 2020
Trials evaluating the same interventions rarely measure or report identical outcomes. This limits the possibility of aggregating effect sizes across studies to generate high-quality evidence through systematic reviews and meta-analyses. To address this problem, core outcome sets (COS) establish agreed sets of outcomes to be used in all future…
Descriptors: Intervention, Outcome Measures, Effect Size, Qualitative Research
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Kontopantelis, Evangelos – Research Synthesis Methods, 2018
Background: Individual patient data (IPD) meta-analysis allows for the exploration of heterogeneity and can identify subgroups that most benefit from an intervention (or exposure), much more successfully than meta-analysis of aggregate data. One-stage or two-stage IPD meta-analysis is possible, with the former using mixed-effects regression models…
Descriptors: Patients, Medical Research, Meta Analysis, Intervention
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Freeman, S. C.; Fisher, D.; Tierney, J. F.; Carpenter, J. R. – Research Synthesis Methods, 2018
Background: Stratified medicine seeks to identify patients most likely to respond to treatment. Individual participant data (IPD) network meta-analysis (NMA) models have greater power than individual trials to identify treatment-covariate interactions (TCIs). Treatment-covariate interactions contain "within" and "across" trial…
Descriptors: Medical Research, Patients, Outcomes of Treatment, Meta Analysis
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Donegan, Sarah; Welton, Nicky J.; Tudur Smith, Catrin; D'Alessandro, Umberto; Dias, Sofia – Research Synthesis Methods, 2017
Background: Many reviews aim to compare numerous treatments and report results stratified by subgroups (eg, by disease severity). In such cases, a network meta-analysis model including treatment by covariate interactions can estimate the relative effects of all treatment pairings for each subgroup of patients. Two key assumptions underlie such…
Descriptors: Network Analysis, Meta Analysis, Outcomes of Treatment, Comparative Analysis
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Leahy, Joy; O'Leary, Aisling; Afdhal, Nezam; Gray, Emma; Milligan, Scott; Wehmeyer, Malte H.; Walsh, Cathal – Research Synthesis Methods, 2018
The use of individual patient data (IPD) in network meta-analysis (NMA) is becoming increasingly popular. However, as most studies do not report IPD, most NMAs are performed using aggregate data for at least some, if not all, of the studies. We investigate the benefits of including varying proportions of IPD studies in an NMA. Several models have…
Descriptors: Patients, Medical Research, Meta Analysis, Network Analysis