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      An overview of methods for network meta-analysis using individual participant data: when do benefits arise?

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          Abstract

          Network meta-analysis (NMA) is a common approach to summarizing relative treatment effects from randomized trials with different treatment comparisons. Most NMAs are based on published aggregate data (AD) and have limited possibilities for investigating the extent of network consistency and between-study heterogeneity. Given that individual participant data (IPD) are considered the gold standard in evidence synthesis, we explored statistical methods for IPD-NMA and investigated their potential advantages and limitations, compared with AD-NMA. We discuss several one-stage random-effects NMA models that account for within-trial imbalances, treatment effect modifiers, missing response data and longitudinal responses. We illustrate all models in a case study of 18 antidepressant trials with a continuous endpoint (the Hamilton Depression Score). All trials suffered from drop-out; missingness of longitudinal responses ranged from 21 to 41% after 6 weeks follow-up. Our results indicate that NMA based on IPD may lead to increased precision of estimated treatment effects. Furthermore, it can help to improve network consistency and explain between-study heterogeneity by adjusting for participant-level effect modifiers and adopting more advanced models for dealing with missing response data. We conclude that implementation of IPD-NMA should be considered when trials are affected by substantial drop-out rate, and when treatment effects are potentially influenced by participant-level covariates.

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          Author and article information

          Journal
          Stat Methods Med Res
          Statistical methods in medical research
          SAGE Publications
          1477-0334
          0962-2802
          May 2018
          : 27
          : 5
          Affiliations
          [1 ] 1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.
          [2 ] 2 Cochrane Netherlands, University Medical Center Utrecht, The Netherlands.
          [3 ] 3 Meta-Research Innovation Center at Stanford, Stanford University, USA.
          [4 ] 4 Institute of Social and Preventive Medicine, University of Bern, Switzerland.
          [5 ] 5 Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Greece.
          [6 ] 6 Institute of Primary Health Care, University of Bern, Switzerland.
          Article
          0962280216660741
          10.1177/0962280216660741
          27487843

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