Statistical methods for Biomarker Discovery, Evaluation, and Validation

该项目将解决对生物标记物发现非常重要的方法学开发需求。 评估和验证。这些包括：(1)功能数据(光谱)的结构和方差分析 和/或图像)用于发现生物标记物；(Ii)评估预后生物标记物的预测价值；以及 (3)开发用于生物标记物验证的分组序贯研究设计和分析方法。 首先，我们专注于通过小波和/或差分分析对复杂函数数据进行分解。这 对相干结构和变异的分析允许对导致区分的特征进行精细的关注 在疾病课之间。 第二，为了评估预后生物标记物的预测价值，我们提出了一种简单但临床上的方法。 相关测量，生存数据的阳性预测值(PPV)曲线。估计和推论 PPV曲线的程序，使用PPV曲线比较生物标志物的预测值， 将对模型的选择、生物标志物的组合进行研究。 第三个目标是开发用于生物标记物验证的分组序贯研究设计和分析方法 学习。重点是开发用于生物标志物验证的成组序贯测试和估计方法 研究，但我们也将调查分组序贯测试和估计方法的预期 筛查研究。 早期检测研究网络中生物标记物的发现、评估和验证研究 (EDRN)为拟议的研究提供主要的激励环境。

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.