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J Clin Epidemiol. 2009 Oct;62(10):1037-44. doi: 10.1016/j.jclinepi.2008.12.008. Epub 2009 Apr 5.

Generalized least squares for assessing trends in cumulative meta-analysis with applications in genetic epidemiology.

Author information

1
Department of Informatics with Applications in Biomedicine, University of Central Greece, Papasiopoulou 2-4, Lamia, GR 35100, Greece. pbagos@ucg.gr

Abstract

OBJECTIVE:

Cumulative meta-analysis allows the evaluation of a study's contribution to the combined effect of the preceding research. It accrues evidence, gradually adding studies one at a time and provides updated estimates along with confidence intervals whenever new evidence emerges. In many research areas, a temporal evolution of the effect size (ES) is present, leading to diminishing effects and would be advantageous to have methods capable of detecting it.

STUDY DESIGN AND SETTING:

We propose a simple regression-based approach for detecting trends in cumulative meta-analysis. We use the combined ES of studies published up to a particular time, as dependent variable and the rank of the published studies as independent variable, in a weighted linear regression to detect a possible trend over time. The correlation between successive ESs used in the regression, is dealt by introducing a first-order autoregressive coefficient using Generalized Least Squares.

RESULTS:

Application in several published meta-analyses of genetic association studies provides encouraging results, outperforming the commonly used method of comparing the results of first vs. subsequent studies.

CONCLUSION:

The particular method is intuitive, easily implemented and allows drawing conclusions based on formal statistical tests. A STATA command is available at http://bioinformatics.biol.uoa.gr/~pbagos/metatrend/.

PMID:
19345563
DOI:
10.1016/j.jclinepi.2008.12.008
[Indexed for MEDLINE]

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