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Joint synthesis of conditionally related multiple outcomes makes better use of data than separate meta‐analyses
Author(s) -
Anwer Sumayya,
Ades A.E.,
Dias Sofia
Publication year - 2020
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1380
Subject(s) - meta analysis , colonisation , conditional independence , outcome (game theory) , covariate , confidence interval , relative risk , random effects model , treatment effect , econometrics , medicine , statistics , computer science , intensive care medicine , mathematics , biology , mathematical economics , colonization , microbiology and biotechnology , traditional medicine
Background When there are structural relationships between outcomes reported in different trials, separate analyses of each outcome do not provide a single coherent analysis, which is required for decision‐making. For example, trials of intrapartum anti‐bacterial prophylaxis (IAP) to prevent early onset group B streptococcal (EOGBS) disease can report three treatment effects: the effect on bacterial colonisation of the newborn, the effect on EOGBS, and the effect on EOGBS conditional on newborn colonisation. These outcomes are conditionally related, or nested, in a multi‐state model. This paper shows how to exploit these structural relationships, providing a single coherent synthesis of all the available data, while checking to ensure that different sources of evidence are consistent. Results Overall, the use of IAP reduces the risk of EOGBS (RR: 0.03; 95% Credible Interval (CrI): 0.002–0.13). Most of the treatment effect is due to the prevention of colonisation in newborns of colonised mothers (RR: 0.08, 95% CrI: 0.04–0.14). Node‐splitting demonstrated that the treatment effect calculated using only direct evidence was consistent with that predicted from the remaining evidence ( p = 0.15). The findings accorded with previously published separate meta‐analyses of the different outcomes, once these are re‐analysed correctly accounting for zero cells. Conclusion Multiple outcomes should be synthesised together where possible, taking account of their structural relationships. This generates an internally coherent analysis, suitable for decision making, in which estimates of each of the treatment effects are based on all available evidence (direct and indirect). Separate meta‐analyses of each outcome have none of these properties.