Complex self-management interventions in chronic disease unravelled: a review of lessons learned from an individual patient data meta-analysis

J Clin Epidemiol. 2017 Mar:83:48-56. doi: 10.1016/j.jclinepi.2017.01.004. Epub 2017 Jan 23.

Abstract

Objectives: Meta-analyses using individual patient data (IPD) rather than aggregated data are increasingly applied to analyze sources of heterogeneity between trials and have only recently been applied to unravel multicomponent, complex interventions. This study reflects on methodological challenges encountered in two IPD meta-analyses on self-management interventions in patients with heart failure or chronic obstructive pulmonary disease.

Study design and setting: Critical reflection on prior IPD meta-analyses and discussion of literature.

Results: Experience from two IPD meta-analyses illustrates methodological challenges. Despite close collaboration with principal investigators, assessing the effect of characteristics of complex interventions on the outcomes of trials is compromised by lack of sufficient details on intervention characteristics and limited data on fidelity and adherence. Furthermore, trials collected baseline variables in a highly diverse way, limiting the possibilities to study subgroups of patients in a consistent manner. Possible solutions are proposed based on lessons learnt from the methodological challenges.

Conclusion: Future researchers of complex interventions should pay considerable attention to the causal mechanism underlying the intervention and conducting process evaluations. Future researchers on IPD meta-analyses of complex interventions should carefully consider their own causal assumptions and availability of required data in eligible trials before undertaking such resource-intensive IPD meta-analysis.

Keywords: Chronic disease; Complex interventions; Individual patient data meta-analysis; Randomized trials; Self-care; Self-management; Subgroup analysis.

Publication types

  • Review

MeSH terms

  • Chronic Disease / therapy*
  • Data Collection
  • Data Interpretation, Statistical
  • Humans
  • Meta-Analysis as Topic*
  • Multivariate Analysis
  • Self Care* / methods