A Quality-Effects Model for Meta-Analysis

  title={A Quality-Effects Model for Meta-Analysis},
  author={Suhail A. R. Doi and Lukman Thalib},
We introduce a quality-effects approach that combines evidence from a series of trials comparing 2 interventions. This approach incorporates the heterogeneity of effects in the analysis of the overall interventional efficacy. However, unlike the random-effects model based on observed between-trial heterogeneity, we suggest adjustment based on measured methodological heterogeneity between studies. We propose a simple noniterative procedure for computing the combined effect size under this model… 

Fixed or random effects meta-analysis? Common methodological issues in systematic reviews of effectiveness

Some of the common methodological issues that arise when conducting systematic reviews and meta-analyses of effectiveness data are discussed, including issues related to study designs, meta-analysis, and the use and interpretation of effect sizes.

Methods for the bias adjustment of meta-analyses of published observational studies.

The summary quality score used for bias adjustment within the context of an appropriate model may be most expedient, and implications for the bias adjustment of meta-analyses of observational studies are discussed.

Meta-analysis in evidence-based healthcare: a paradigm shift away from random effects is overdue

It is argued for an urgent return to the earlier framework with updates that address problems with the random-effects approach and demonstrated that there exist better estimators under the fixed-effect model framework that can achieve optimal error estimation.

Meta-analysis of heterogeneous clinical trials: an empirical example.

Selecting the best meta-analytic estimator for evidence-based practice: a simulation study.

A simulation study was conducted to compare estimator performance and demonstrates that the IVhet and quality effects estimators, though biased, have the lowest mean squared error.

Meta-analysis I

This chapter provides an in-depth discussion of the various statistical methods currently available, with a focus on bias adjustment in meta-analysis.

Is It Time for the Cochrane Collaboration to Reconsider Its Meta-Analysis Methodology?

  • A. Onitilo
  • Economics
    Clinical Medicine & Research
  • 2014
A quality effect model is proposed that achieves variance reduction through additional information garnered from the conduct and design of the list of studies and the case that bias reduction is not possible is made and it is questioned if the time has come for organizations such as Cochrane to seriously consider updating their methodologies.



Modelling study quality in meta-analysis.

A probability model is presented for the effect of quality on a summary effect measure, which motivates criteria used to assess quality, and can be used to incorporate quality into the calculation of asummary effect.

Meta-analysis in clinical trials.

A comparison of statistical methods for meta-analysis.

It is shown that the commonly used DerSimonian and Laird method does not adequately reflect the error associated with parameter estimation, especially when the number of studies is small, and three methods currently used for estimation within the framework of a random effects model are considered.

Meta-analysis of best-evidence synthesis?

  • H. Eysenck
  • Psychology
    Journal of evaluation in clinical practice
  • 1995
This article examines the usefulness of meta-analysis, and articulates many of the criticisms that have been made of its workings, and suggests that best-evidence synthesis, or theory-directed analysis, might be a safer option.

Detecting and describing heterogeneity in meta-analysis.

It is concluded that the test of heterogeneity should not be the sole determinant of model choice in meta-analysis, and inspection of relevant normal plots, as well as clinical insight, may be more relevant to both the investigation and modelling of heterogeneity.

Systematic Reviews: Meta-analysis and its problems

Meta-analysis may not be the one best method for studying the diversity of fields for which it has been used, and several problems arise in this work.

Investigating causes of heterogeneity in systematic reviews

Investigation of artefactual and true causes of heterogeneity form essential steps in moving from a combined effect estimate to application to particular populations and individuals.