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

Debray, Thomas Pa; Schuit, Ewoud; Efthimiou, Orestis; Reitsma, Johannes B; Ioannidis, John Pa; Salanti, Georgia; Moons, Karel Gm (2018). An overview of methods for network meta-analysis using individual participant data: when do benefits arise? Statistical Methods in Medical Research, 27(5), pp. 1351-1364. SAGE Publications (UK and US) 10.1177/0962280216660741

[img] Text
Debray StatMethodsMedRes 2018.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (206kB)

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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)
04 Faculty of Medicine > Medical Education > Institute of General Practice and Primary Care (BIHAM)

UniBE Contributor:

Salanti, Georgia

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

0962-2802

Publisher:

SAGE Publications (UK and US)

Language:

English

Submitter:

Doris Kopp Heim

Date Deposited:

18 Aug 2016 09:38

Last Modified:

05 Dec 2022 14:57

Publisher DOI:

10.1177/0962280216660741

PubMed ID:

27487843

Uncontrolled Keywords:

Meta-analysis; individual participant data; missing data; mixed treatment comparison; network meta-analysis; repeated measurements

BORIS DOI:

10.7892/boris.86089

URI:

https://boris.unibe.ch/id/eprint/86089

Actions (login required)

Edit item Edit item
Provide Feedback