Efficient Study Designs and Semiparametric Inference Methods for Developing Genomic Biomarkers in Cancer Clinical Research

Hisashi Noma ()
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Hisashi Noma: The Institute of Statistical Mathematics, Department of Data Science

A chapter in Frontiers of Biostatistical Methods and Applications in Clinical Oncology, 2017, pp 381-400 from Springer

Abstract: Abstract In the development of genomic biomarkers and molecular diagnostics, clinical studies using high-throughput assays such as DNA microarrays generally require enormous costs and efforts. Several efficient study designs for reducing the costs of such expensive measurements have been developed, mainly in the field of epidemiology. Under these efficient designs, expensive measurements are collected only on selected subsamples based on adequate response-selective sampling schemes, and total measurement costs are effectively reduced. In this study, we discuss the application of these effective designs to genomic analyses in cancer clinical studies, and provide relevant statistical methods such as gene selection (e.g., multiple testing based on the false discovery rate). Efficient semiparametric inference methods using auxiliary clinical information are also discussed.

Keywords: Nested case-control study; Case-cohort study; Two-phase designs; Genomic biomarker; Semiparametric inference; Weighted estimating equation; Calibration estimator (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_23

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DOI: 10.1007/978-981-10-0126-0_23

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