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.
- Confidence interval
- General linear mixed effects model
- Heterogeneity index
- Individual patient data
- Maximum likelihood estimate (MLE)