Generalized least squares for
assessing trends in cumulative meta-analysis: applications in genetic
Pantelis G. Bagos and Georgios Κ.
We propose a simple and formal regression-based
approach for detecting trends in cumulative meta-analysis. By using
the logarithm of the combined Odds Ratio (logOR) of studies published
up to a particular time, as dependent variable and the rank of the
published studies as independent variable in a linear regression with
weights proportional to the inverse variance of logORs, we were able
to detect and validate a possible trend over time for the speculated
association. The correlation of successive logORs used in the
regression, is been dealt by introducing a first order autoregressive
(AR1) coefficient using Generalized Least Squares (GLS). Application
of the method in several already published meta-analyses of genetic
association studies provides very encouraging results, outperforming
the commonly used method of comparing the results of first vs.
subsequent studies. The particular method is intuitive, very easily
implemented in nearly every statistical package, and allows drawing
conclusions based on formal statistical tests.
program for fitting the models proposed in this work, is available
(and a help file: metatrend.hlp)
was developed in Stata 8.0
although it is functional with older versions (6.0 and 7.0).
implemented here, is presented in:
Bagos PG, Nikolopoulos GK. Generalized least squares for assessing trends in cumulative meta-analysis with applications in genetic epidemiology. 2009, Journal of Clinical Epidemiology, 62 (10): 1037-1044 [PDF] [Pubmed] [Google Scholar]
The 4 datasets used in the paper for illustrating can
be found here: CTLA4, GNB3, PAI1, ITA2
See also metatrend at Econpapers and Repec
From within Stata issue the command:
net install metatrend