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Related Experiment Video

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One-stage random effects meta-analysis using linear mixed models for aggregate continuous outcome data.

Katerina Papadimitropoulou1, Theo Stijnen2, Olaf M Dekkers1

  • 1Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands.

Research Synthesis Methods
|December 8, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a method to create pseudo individual participant data (IPD) from aggregate data for continuous outcomes. Analyzing this pseudo IPD using linear mixed models yields results identical to using true IPD, enhancing meta-analysis flexibility.

Keywords:
linear mixed modelsmeta-analysispseudo individual participant datarandom effects modelsimulation study

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Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Most meta-analyses use aggregate data, not individual participant data (IPD).
  • Linear mixed modeling is suitable for continuous outcomes with IPD but IPD are rarely available.

Purpose of the Study:

  • To present a method for generating pseudo IPD from aggregate data for continuous outcomes.
  • To enable the use of flexible mixed modeling techniques typically requiring true IPD.

Main Methods:

  • Generated pseudo IPD from aggregate data (mean, SD, sample size) for studies with normal outcomes.
  • Analyzed pseudo IPD using likelihood-based linear mixed modeling.
  • Explored various modeling options for within-study and between-study effects.

Main Results:

  • Analysis of pseudo IPD yielded identical results to analyzing true IPD.
  • The pseudo IPD approach allowed for flexible modeling of variances and effects.
  • An extensive model with free within-study variances showed better fit in Alzheimer disease datasets.
  • Simulations confirmed adequate coverage probability for the pseudo IPD method.

Conclusions:

  • Generating pseudo IPD from aggregate data is a viable alternative when true IPD are unavailable.
  • This method facilitates advanced mixed modeling in meta-analyses, improving accuracy and flexibility.
  • The approach is particularly beneficial for handling complex variance structures and small sample effects.