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Related Experiment Video

Updated: May 5, 2026

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Estimation of between-trial variance in sequential meta-analyses: a simulation study.

Putri W Novianti¹, Kit C B Roes¹, Ingeborg van der Tweel¹

¹Biostatistics & Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.

Contemporary Clinical Trials

|December 11, 2013

Summary

Estimators for heterogeneity in sequential meta-analysis (SMA) can be biased. The DerSimonian-Laird (DL) estimator underestimates effects for dichotomous outcomes, while DL2 and Paule-Mandel (PM) show improved performance for SMAs.

Keywords:

Clinical trials Heterogeneity Sequential meta-analysis

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Area of Science:

Biostatistics
Clinical Trial Methodology
Evidence Synthesis

Background:

Estimators for heterogeneity variance in meta-analysis can produce conflicting results.
Accurate heterogeneity estimation is crucial in sequential meta-analysis (SMA) for timely conclusions.

Purpose of the Study:

To evaluate the performance of heterogeneity estimators in sequential meta-analysis (SMA).
To compare bias and variance of different estimators using simulation and real-world data.

Main Methods:

Conducted extensive simulation studies with dichotomous and continuous outcome data.
Applied and evaluated DerSimonian-Laird (DL), two-step DL (DL2), and Paule-Mandel (PM) estimators.
Assessed bias, variance, and sample size requirements for stable estimates.

Main Results:

The DerSimonian-Laird (DL) estimator significantly underestimates heterogeneity for dichotomous outcomes.
The two-step DL (DL2) estimator demonstrates improved accuracy compared to the standard DL estimator.
Paule-Mandel (PM) and DL2 estimators performed well for both dichotomous and continuous data in SMA.

Conclusions:

DL2 and PM estimators are recommended for heterogeneity estimation in sequential meta-analysis.
These recommended estimators provide more reliable results for both dichotomous and continuous outcomes in SMA.
Choosing appropriate estimators enhances the precision and reliability of conclusions drawn from accumulating evidence.