In this paper, we investigate meta-analysis of the overall treatment effect in the setting of a multi-center clinical trial study in which patient level data are available. We estimate the overall treatment effect using two methods: meta-analysis, which uses the summary statistics from each center and a unified combined analysis of patient level data. In the meta-analysis we use a random effects meta-analysis model and in both analyses we use a parametric proportional hazards model.

In a randomized clinical trial study, subjects are recruited at multiple centers to accrue large enough samples within an acceptable period of time and to enhance the generalizability of study results. Heterogeneity between trials may arise from the center effects or treatment effect itself. To take into account the heterogeneities, random effects models are used. We performed a data analysis based on a multi-center clinical trial study in small-cell lung cancer conducted by the Eastern Cooperative Oncology Group and then parallel data analysis within a simulation study.

In the simulation study we vary the magnitude of the center and the treatment-by-center heterogeneity in the data generation and estimated the over all treatment effect using the two methods. We compared the two methods in terms of bias, mean square error and percentage of significant treatment effect. The simulation study shows that meta-analysis treatment effects estimate are slightly biased when covariates are included in the analysis.