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Primary studies to identify prognostic factors are abundant, but often have conflicting findings and variable quality. This motivates systematic reviews to identify, evaluate, and summarize prognostic factor studies. Broad search strings are required to identify relevant studies, and the CHARMS-PF checklist guides subsequent data extraction. The QUIPS tool examines each study’s risk of bias; unfortunately, many studies will have high risk of bias due to poor design and analysis. Meta-analysis can be used to combine and summarize prognostic effect estimates (such as hazard ratios or odds ratios) across studies, but may not always be sensible. Between-study heterogeneity is expected. Ideally, separate meta-analyses are performed; for example, for each method of prognostic factor measurement, for each cut point (for categorized continuous prognostic factors), and for unadjusted and adjusted prognostic factor estimates. The adjusted prognostic factor estimate is usually more relevant, because prognosis in clinical practice is commonly based on multiple prognostic factors, and so the prognostic information from a particular factor needs to add value over others. Publication bias is also a major threat in reviews of prognosis studies. Availability of individual participant data alleviates many, but not all, of the challenges.

Original publication





Book title

Systematic Reviews in Health Research: Meta-Analysis in Context: Third Edition

Publication Date



324 - 346