Table 1

Purported theoretical advantages of IPD meta-analysis of clinical trials and practical limitations in achieving them

ItemPractical challengesConsequences
Standardisation of data across trials
  • Derive standard participant characteristics

  • Diversity of characteristics and their coding

  • Time and resources required to standardise them

  • Loss of detail in characteristics through categorisation, for example, physical activity coded as any activity versus predominantly inactive

  • Analysis of subgroup effects and treatment–covariate interactions

  • Poor recruitment of participants with a given characteristic in primary trials

  • Limits to clinical usefulness of analysis where covariates are coded in a crude way

  • Derive standard outcome definitions

  • Complexity or lack of consensus concerning outcome definition

  • Time and resources required to standardise them

  • Unaddressed between-study heterogeneity

  • Delay in the development of final dataset

Unreported outcomes
  • Incorporation of unreported outcomes

  • Format of variables in shared datasets may prevent harmonisation

  • For outcomes, data collected as mean change, lack of baseline and final values in dataset make them unusable in IPD meta-analysis

Role of IPD meta-analysis in dynamic research areas
  • Acquisition of IPD from all eligible trials

  • Uncooperative trial investigators

  • Lost or corrupted trial data

  • Logistically unfeasible acquisition of IPD (trials identified late in the project life)

  • Trials in the IPD meta-analysis might be substantially different in comparison with those for which IPD is not available

  • The IPD meta-analysis does not reflect the totality of evidence on the subject and is at risk of being out-of-date

Other advantages listed in the literature*
  • Check the integrity of trial data

  • Matching participants reported in trial publication and with those in shared datasets

  • Delay in development of final dataset

  • Dataset credibility

  • Imputation of missing data

  • Variation in reasons for data missingness across trials

  • Effect sizes using imputed data may be harder to defend

  • Incorporation of unpublished trials

  • Data integrity could not be verified against peer-reviewed publication

  • Potential undetectable data errors can have serious impact on the findings of the analysis and its reliability

  • Analyse all important outcomes

  • Lack of consensus among trialists and clinicians (core outcome set) means that important outcomes are not recorded in each study

  • Evidence pooled for research-driven outcomes rather than for those critical and important to patient care

  • *As described in publications by Riley et al 1 and Tierney et al.5

  • IPD, individual participant data.