The number of primary studies evaluating prognostic factors and models is rising per day. Alike for therapies and diagnostic tests, critically summarizing and analyzing the evidence form prognostic studies in a systematic review and meta analysis is beneficial for health care professionals seeking the best evidence. Reviews of prognostic studies are much more challenging because of more variation in questions & designs, specific sources of bias & variation, and more complex statistical meta-analytical models. Several advances regarding the design, critical appraisal and statistical analysis in systematic reviews of prognostic studies, have recently been made. In this course we discuss and practice how to define your review questions, how to search the literature, how to critically assess the methodological quality of primary prognostic studies, and which statistical methods to use for meta-analyses of the results of primary prognostic studies. The course consists of plenary presentations, small-group discussions, and computer exercises.
By the end of the course, you'll should be able to:
- Explain the rationale for performing a systematic review of prognostic studies
- List the key steps of a systematic review of prognostic studies
- Formulate a focused review question addressing a prognostic problem
- Systematically search the literature
- Critically appraise the evidence from primary prognostic studies
- Formulate the difficulties of meta-analysis of prognostic research
- Meta-analyses of performance of prognostic models
- Meta-analyses of the added value of specific prognostic factors
Our Professors
Thomas Debray MSc PhD
Thomas Debray is an assistant professor in epidemiology at the Julius Center for Health Sciens and Primary care of the University Medical Center Utrecht. Before his current engagement, he attained a bachelor's degree in applied informatics, a master's degree in artificial intelligence, a master's degree in clinical epidemiology and a PhD in epidemiology (October 2013). A large part of his research activities focuses on statistical methodology for developing and validating prediction models using large and disparate medical data sets.
