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Frontiers of Biostatistical Methods and Applications in Clinical Oncology pp 257–273Cite as

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

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Abstract

Prognostic biomarker studies, which examine the association 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.

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Author information

Authors and Affiliations

  1. Department of Integrated Medicine, Biomedical Statistics, Osaka University, 2-2, Yamada-Oka, Suita, Osaka, 565-0871, Japan

    Satoshi Hattori

  2. HSR&D Center of Excellence, VA Puget Sound Health Care System, Seattle, USA

    Xiao-Hua Zhou

  3. Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA

    Xiao-Hua Zhou

Authors
  1. Satoshi Hattori
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  2. Xiao-Hua Zhou
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Correspondence to Satoshi Hattori .

Editor information

Editors and Affiliations

  1. Graduate School of Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan

    Shigeyuki Matsui

  2. Cancer Research and Biostatistics, Seattle, Washington, USA

    John Crowley

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Hattori, S., Zhou, XH. (2017). Meta-Analysis of Prognostic Studies Evaluating Time-Dependent Diagnostic and Predictive Capacities of Biomarkers. In: Matsui, S., Crowley, J. (eds) Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0126-0_16

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