Longitudinal data had intrinsic correlation problems at different time points, and traditional meta-analysis techniques cannot resolve this problem. Regression coefficients based on multi-level models can fully consider the correlations of longitudinal data at various time points. This paper uses SAS software to perform multi-level regression coefficient model meta-analysis and provides programming code which is simple and easy to operate.
Despite the rapid development of meta-analysis technology, there were currently no consolidation technology for longitudinal data. The meta-analysis model based on the generalized linear mixed-effects model can fully encapsulate the correlation between various time points and accurately estimate the final combined effect, which is an ideal model for longitudinal-data meta-analysis. Through example data, this paper used SAS software to realize longitudinal-data meta-analysis and provided programming codes.