A unified approach of meta-analysis: application to an antecedent biomarker study in Alzheimer's disease

Abstract

This article provides a unified methodology of meta-analysis that synthesizes medical evidence by using both available individual patient data (IPD) and published summary statistics within the framework of likelihood principle. Most up-to-date scientific evidence on medicine is crucial information not only to consumers but also to decision makers, and can only be obtained when existing evidence from the literature and the most recent IPD are optimally synthesized. We propose a general linear mixed effects model to conduct meta-analyses when IPD are only available for some of the studies and summary statistics have to be used for the rest of the studies. Our approach includes both the traditional meta-analyses in which only summary statistics are available for all studies and the other extreme case in which IPD are available for all studies as special examples. We implement the proposed model with statistical procedures from standard computing packages. We provide measures of heterogeneity based on the proposed model. Finally, we demonstrate the proposed methodology through a real-life example by studying the cerebrospinal fluid biomarkers to identify individuals with a high risk of developing Alzheimer's disease when they are still cognitively normal.

Keywords:

• confidence interval
• general linear mixed effects model
• heterogeneity index
• individual patient data
• maximum likelihood estimate (MLE)
• meta-analyses

Acknowledgements

The authors would like to thank Dr. David Holtzman and Dr. Anne Fagan, Department of Neurology, Washington University in St. Louis, for providing the biomarker data used in the paper. The authors also thank the Clinical and Psychometric and Genetic Cores of the Alzheimer's Disease Research Center at Washington University for subject assessments. Dr. Xiong's work was supported by grant K25 AG025189 from the National Institute on Aging and by the Alan A. and Edith Wolff Charitable Trust. Financial support for this study was also provided in part by National Institute on Aging grants AG003991, AG005681, and AG026276 for Chengjie Xiong, J. Philip Miller, and John C. Morris. SAS code for implementing the proposed methodology can be requested to the corresponding author through email at [email protected].