Meta-Analysis of Prognostic Studies Evaluating Time-Dependent Diagnostic and Predictive Capacities of Biomarkers

Satoshi Hattori () and Xiao-Hua Zhou
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Satoshi Hattori: Osaka University, Department of Integrated Medicine, Biomedical Statistics
Xiao-Hua Zhou: VA Puget Sound Health Care System, HSR&D Center of Excellence

A chapter in Frontiers of Biostatistical Methods and Applications in Clinical Oncology, 2017, pp 257-273 from Springer

Abstract: Abstract Prognostic biomarker studies, which examine the associationAssociation between biomarkers and patients’ prognoses, have played important roles in clinical decision making. Since prognostic studies are often conducted with small sample sizes in a limited number of centers, meta-analysis is expected to be a powerful tool to obtain sound evidence on prognostic biomarkers. However, the application of meta-analysis of prognostic studies has been limited partly due to the lack of sound statistical methods. In this chapter, we introduce some recently developed methods useful for the evaluation of diagnostic or predictive capacities of biomarkers for binary or time-to-event outcomes. In addition, we newly present a novel method to estimate the time-dependent positive and negative predictive value curves based on Meta analysis meta-analysis.

Keywords: Cutoff value; Diagnostic studies; Prognostic studies; Meta-analysis; Time-dependent predictive value curve; Time-dependent receiver operating characteristics (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-10-0126-0_16

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DOI: 10.1007/978-981-10-0126-0_16

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