Statistical Methods for Meta-Analyses

Catalog description

This graduate-level Biostatistics course will introduce the fundamentals of statistical methods for meta-analyses. It will cover key principles of meta-analysis and the statistical rationales behind the analytic models, including univariate meta-analysis, multivariate meta-analysis, meta-analysis of diagnostic test accuracy, network meta-analysis, and multivariate network meta-analysis. Beyond these commonly used models, the course will cover statistical methods and software that investigate and correct for biases in systematic reviews such as publication bias, outcome reporting bias. Advanced statistical inferential tools such as publication bias, outcome reporting bias. Advanced statistical inferential tools such as composite likelihood, pseudolikelihood, integrated likelihood methods, EM algorithms will be introduced. In addition, the course will also cover some practical steps in systematic review including search strategies, data abstraction methods; quality assesment; and writing a meta-analysis report. The course is composed of a series of weekly lectures and small group discussions. Students will be expected to attend weekly lectures, participate in class discussions, review assigned readings, complete homework assignments, and conduct a real-world meta-analysis with a clinically meaningful problem. Fundamentals of Biostatistics background or permission of instructor required to enroll.