Related Experiment Video
Updated: Jun 9, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Generalized least squares for assessing trends in cumulative meta-analysis with applications in genetic epidemiology
Pantelis G Bagos1, Georgios K Nikolopoulos
1Department of Informatics with Applications in Biomedicine, University of Central Greece, Papasiopoulou 2-4, Lamia, GR 35100, Greece. pbagos@ucg.gr
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/.
More Related Videos
Related Concept Videos
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Bias in Epidemiological Studies
Statistical Methods for Analyzing Epidemiological Data
Comparing the Survival Analysis of Two or More Groups

