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Comparison of one-step and two-step meta-analysis models using individual patient data
Thomas Mathew1, Kenneth Nordström
1Department of Mathematics and Statistics, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. mathew@umbc.edu
Biometrical Journal. Biometrische Zeitschrift
|March 30, 2010
Summary
Combining trial data for meta-analysis requires careful consideration. This study identifies conditions where one-step and two-step individual patient data (IPD) meta-analysis estimators are equivalent, clarifying aggregate data analysis validity.
Area of Science:
- Biostatistics
- Clinical Trials
- Epidemiology
Background:
- Meta-analysis and multicentre trials necessitate combining data from separate studies.
- Existing literature shows confusion regarding the validity of aggregate data analysis versus individual patient data (IPD) meta-analysis.
Purpose of the Study:
- To address statistical uncertainties in combining trial data by comparing one-step and two-step IPD meta-analysis estimators.
- To derive conditions for the equivalence of these two meta-analysis approaches.
Main Methods:
- Utilized linear models for both summary data and IPD, assuming summary data includes the best linear unbiased estimator or maximum likelihood estimator and its covariance matrix.
- Developed a general framework accommodating random effects and covariates, applicable to various linear models.
Main Results:
- Derived a general condition for the coincidence of one-step and two-step IPD meta-analysis estimators.
- Demonstrated that estimators coincide under specific conditions, extending previous findings.
- Highlighted the influence of balance and heterogeneity on estimator equivalence.
- Showed that with covariates, estimator coincidence requires unrealistic simplifying assumptions.
Conclusions:
- The study provides a theoretical condition for the equivalence of one-step and two-step IPD meta-analysis.
- Findings clarify the validity of aggregate data analysis in meta-analysis, particularly when covariates are involved.
- Practical implications for multicentre trial design and meta-analysis are discussed, emphasizing the limitations with complex models.
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