title: Adjusting for misclassification in a stratified biomarker clinical trial.
abstract: Clinical trials utilizing predictive biomarkers have become a research focus in personalized medicine. We investigate the effects of biomarker misclassification on the design and analysis of stratified biomarker clinical trials. For a variety of inference problems including marker-treatment interaction in particular, we show that marker misclassification may have profound adverse effects on the coverage of confidence intervals, power of the tests, and required sample sizes. For each inferential problem, we propose methods to adjust for the classification errors.
authors.affiliation: Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
authors.forename: Chunling
authors.initials: C
authors.lastname: Liu
authors.forename: Aiyi
authors.initials: A
authors.lastname: Liu
authors.forename: Jiang
authors.initials: J
authors.lastname: Hu
authors.forename: Vivian
authors.initials: V
authors.lastname: Yuan
authors.forename: Susan
authors.initials: S
authors.lastname: Halabi
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis* / Q000145:classification*; D056808:Biostatistics; D002292:Carcinoma, Renal Cell / Q000188:drug therapy / Q000276:immunology / Q000556:secondary; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D016001:Confidence Intervals; D006801:Humans; D015850:Interleukin-6 / Q000032:analysis; D007680:Kidney Neoplasms / Q000188:drug therapy / Q000276:immunology; D015233:Models, Statistical; D009369:Neoplasms / Q000628:therapy; D057285:Precision Medicine; D018401:Sample Size
chemical_list: D014408:Biomarkers, Tumor; C508600:IL6 protein, human; D015850:Interleukin-6
keywords: biomarkers; classification error; correction for error; personalized medicine; power and sample size; prevalence; randomized controlled clinical trials; sensitivity and specificity
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