{"title":"基于非参数生物标志物的治疗选择与可重复性数据","authors":"Sara Byers, Xiao Song","doi":"10.1002/sim.10218","DOIUrl":null,"url":null,"abstract":"We consider evaluating biomarkers for treatment selection under assay modification. Survival outcome, treatment, and Affymetrix gene expression data were attained from cancer patients. Consider migrating a gene expression biomarker to the Illumina platform. A recent novel approach allows a quick evaluation of the migrated biomarker with only a reproducibility study needed to compare the two platforms, achieved by treating the original biomarker as an error‐contaminated observation of the migrated biomarker. However, its assumptions of a classical measurement error model and a linear predictor for the outcome may not hold. Ignoring such model deviations may lead to sub‐optimal treatment selection or failure to identify effective biomarkers. To overcome such limitations, we adopt a nonparametric logistic regression to model the relationship between the event rate and the biomarker, and the deduced marker‐based treatment selection is optimal. We further assume a nonparametric relationship between the migrated and original biomarkers and show that the error‐contaminated biomarker leads to sub‐optimal treatment selection compared to the error‐free biomarker. We obtain the estimation via B‐spline approximation. The approach is assessed by simulation studies and demonstrated through application to lung cancer data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonparametric Biomarker Based Treatment Selection With Reproducibility Data\",\"authors\":\"Sara Byers, Xiao Song\",\"doi\":\"10.1002/sim.10218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider evaluating biomarkers for treatment selection under assay modification. Survival outcome, treatment, and Affymetrix gene expression data were attained from cancer patients. Consider migrating a gene expression biomarker to the Illumina platform. A recent novel approach allows a quick evaluation of the migrated biomarker with only a reproducibility study needed to compare the two platforms, achieved by treating the original biomarker as an error‐contaminated observation of the migrated biomarker. However, its assumptions of a classical measurement error model and a linear predictor for the outcome may not hold. Ignoring such model deviations may lead to sub‐optimal treatment selection or failure to identify effective biomarkers. To overcome such limitations, we adopt a nonparametric logistic regression to model the relationship between the event rate and the biomarker, and the deduced marker‐based treatment selection is optimal. We further assume a nonparametric relationship between the migrated and original biomarkers and show that the error‐contaminated biomarker leads to sub‐optimal treatment selection compared to the error‐free biomarker. We obtain the estimation via B‐spline approximation. The approach is assessed by simulation studies and demonstrated through application to lung cancer data.\",\"PeriodicalId\":21879,\"journal\":{\"name\":\"Statistics in Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistics in Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/sim.10218\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/sim.10218","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
Nonparametric Biomarker Based Treatment Selection With Reproducibility Data
We consider evaluating biomarkers for treatment selection under assay modification. Survival outcome, treatment, and Affymetrix gene expression data were attained from cancer patients. Consider migrating a gene expression biomarker to the Illumina platform. A recent novel approach allows a quick evaluation of the migrated biomarker with only a reproducibility study needed to compare the two platforms, achieved by treating the original biomarker as an error‐contaminated observation of the migrated biomarker. However, its assumptions of a classical measurement error model and a linear predictor for the outcome may not hold. Ignoring such model deviations may lead to sub‐optimal treatment selection or failure to identify effective biomarkers. To overcome such limitations, we adopt a nonparametric logistic regression to model the relationship between the event rate and the biomarker, and the deduced marker‐based treatment selection is optimal. We further assume a nonparametric relationship between the migrated and original biomarkers and show that the error‐contaminated biomarker leads to sub‐optimal treatment selection compared to the error‐free biomarker. We obtain the estimation via B‐spline approximation. The approach is assessed by simulation studies and demonstrated through application to lung cancer data.
期刊介绍:
The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.