Statistical methods for Biomarker Discovery, Evaluation, and Validation

该项目将解决方法学发展的需要，是重要的生物标志物的发现。 评估和验证。这些包括：（i）功能数据（光谱）中的结构和方差分析 （ii）评估预后生物标志物的预测值;以及 (iii)开发用于生物标志物验证的组序贯研究设计和分析方法。 首先，我们专注于通过小波和/或差分分析分解复杂的函数数据。这 对相干结构和变化的分析允许对导致区分的特征进行精确的关注 疾病类别之间。 第二，为了评估预后生物标志物的预测价值，我们提出了一个简单而临床的方法， 相关测量，存活数据的阳性预测值（PPV）曲线。估计和推断 PPV曲线的程序，PPV曲线用于比较生物标志物的预测值的用途， 选择模型，并结合生物标志物将进行研究。 第三个目标是开发组序贯研究设计和生物标志物验证的分析方法 问题研究重点是开发用于生物标志物验证的组序贯检验和估计方法 研究，但我们也将研究组序贯检验和估计方法的前瞻性 筛选研究。 早期检测研究网络中的生物标志物发现、评估和验证研究 （EDRN）为拟议的研究提供主要的激励设置。

This project will address methodology development needs that are important for biomarker discovery. evaluation, and validation. These include: (i) analysis of structure and variance in functional data (spectra and/or images) for biomarker discovery; (ii) evaluating the predictive values of prognostic biomarkers; and (iii) development of group sequential study designs and analysis methods for biomarker validation. First, we focus on decomposing complex functional data via wavelet and/or differential analysis. This analysis of coherent structure and variation allows for a refined focus on features leading to discrimination between disease classes. Second, for evaluating the predictive values of prognostic biomarkers, we propose a simple yet clinically relevant measure, positive predictive value (PPV) curve for survival data. Estimating and inference procedures of the PPV curves, the use of PPV curves for comparing predictive values of biomarkers, selecting models, and combining biomarkers will be studied. A third aim is to develop group sequential study designs and analysis "methods for biomarker validation studies. Focus is on developing group sequential testing and estimation methods for biomarker validation studies but we will also investigate group sequential testing and estimation methods for prospective screening studies. The biomarker discovery, evaluation, and validation studies in the Early Detection Research Network (EDRN) provide the primary motivating settings for the proposed research.