Synthesising quantitative evidence in systematic reviews of complex health interventions - PubMed (original) (raw)

Synthesising quantitative evidence in systematic reviews of complex health interventions

Julian P T Higgins et al. BMJ Glob Health. 2019.

Abstract

Public health and health service interventions are typically complex: they are multifaceted, with impacts at multiple levels and on multiple stakeholders. Systematic reviews evaluating the effects of complex health interventions can be challenging to conduct. This paper is part of a special series of papers considering these challenges particularly in the context of WHO guideline development. We outline established and innovative methods for synthesising quantitative evidence within a systematic review of a complex intervention, including considerations of the complexity of the system into which the intervention is introduced. We describe methods in three broad areas: non-quantitative approaches, including tabulation, narrative and graphical approaches; standard meta-analysis methods, including meta-regression to investigate study-level moderators of effect; and advanced synthesis methods, in which models allow exploration of intervention components, investigation of both moderators and mediators, examination of mechanisms, and exploration of complexities of the system. We offer guidance on the choice of approach that might be taken by people collating evidence in support of guideline development, and emphasise that the appropriate methods will depend on the purpose of the synthesis, the similarity of the studies included in the review, the level of detail available from the studies, the nature of the results reported in the studies, the expertise of the synthesis team and the resources available.

Keywords: complex interventions; guideline development; meta-analysis; systematic reviews.

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Conflict of interest statement

Competing interests: JMG reports personal fees from the Campbell Collaboration. EAR reports being a Methods Editor with Cochrane Public Health.

Figures

Figure 1

Figure 1

Example graphical displays of data from a review of interventions to promote breast feeding, for the outcome of continued breast feeding up to 23 months. Panel A: Forest plot for relative risk (RR) estimates from each study. Panel B: Albatross plot of p value against sample size (effect contours drawn for risk ratios assuming a baseline risk of 0.15; sample sizes and baseline risks extracted from the original papers by the current authors); Panel C: Harvest plot (heights reflect design: randomised trials (tall), quasi-experimental studies (medium), observational studies (short); bar shading reflects follow-up: longest follow-up (black) to shortest follow-up (light grey) or no information (white)). Panel D: Bubble plot (bubble sizes and colours reflect design: randomised trials (large, green), quasi-experimental studies (medium, red), observational studies (small, blue); precision defined as inverse of the SE of each effect estimate (derived from the CIs); categories are: “Potential Harm”: RR <0.8; “No Effect”: RRs between 0.8 and 1.25; “Potential Benefit”: RR >1.25 and CI includes RR=1; “Benefit”: RR >1.25 and CI excludes RR=1).

Figure 2

Figure 2

Intervention components in the studies integrated by Welton et al (a sample of 18 from 56 active treatment arms). EDU, educational component; BEH, behavioural component; COG, cognitive component; REL, relaxation component; SUP, psychosocial support component.

Figure 3

Figure 3

Theoretical diabetes care model (adapted from Brown _et al_68).

Figure 4

Figure 4

Simplified version of the conceptual model used by Briggs et al (_a_dapted from Briggs _et al_90).

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