Statistical Methods for Cancer Biomarkers

癌症生物标志物的统计方法

• 批准号: 9974486
• 负责人: Debashis Ghosh
• 金额: $ 27.13万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9974486
• 关键词: Address; Aftercare; Bayesian Method; Biological Markers; Cancer Detection; Characteristics; Clinical; Data; Data Set; Dimensions; Disease; Early Diagnosis; Etiology; Eye; Future; Head and Neck Cancer; Head and neck structure; Human Papillomavirus; Individual; Inequality; Knowledge; Lagrangian multiplier; Lead; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Methodology; Methods; Modeling; Molecular; Monitor; Odds Ratio; Outcome; Patients; Play; Problem Solving; Property; Proteins; Recurrence; Research; Risk; Role; Screening for Prostate Cancer; Statistical Methods; Statistical Models; Time; Training; Update; Urine; Weight; base; cancer biomarkers; cancer diagnosis; early screening; genetic predictors; genome wide association study; improved; individual patient; molecular marker; novel; outcome forecast; outcome prediction; personalized medicine; predictive modeling; prostate cancer risk; response; risk prediction model; statistics; ward

Project Summary/Abstract Individualized prognostic models abound in clinical biomedicine. They are used to make predictions of the future, derived from individual patient characteristics, and will play increasingly important roles in the move towards per- sonalized medicine. They can be used in the settings of early detection and screening, or after a cancer diagnosis to help decide on treatment, or after treatment to monitor for progression and recurrence. While some models are well established, they likely have the potential to be improved through the use of additional variables. Larger and better quality training datasets and improved statistical models and methods will improve their accuracy, but the potential for largest improvement is through new biomarkers. Since cancer is a heterogenous disease with multifactorial etiology, many clinical and molecular factors will likely aid in predicting the future for a patient, and would be candidates for inclusion in a new model. The challenge we will address in this research is how to de- velop a new model that both includes the new biomarkers and makes use of the knowledge implicit in the existing models, when the datasets that are available containing the new biomarkers are only of modest size. To develop a new model from a new dataset of modest size that contains the new biomarkers, the typical approach will be to analyze these data, as a separate entity, and build a model based on that analysis. However, this approach does not utilize the external information from an established model. Such external information will often be available, however it may come in the form of regression coefficients, odds ratios or other summary statistics for a subset of the variables, or in the form of a prediction from an online calculator. We will consider a variety of statistical methods for incorporating the external information. The methods we propose to develop are motivated by specific head and neck cancer and prostate cancer stud- ies, but have much broader applicability to other cancers and other diseases. In the head and neck study the additional new biomarkers to be incorporated in to the prediction models are HPV status and other molecular biomarkers. For the prostate cancer risk prediction model the new bimarkers are based on proteins measured from urine. The research is separated into three specific aims. The first aim considers the situation in which there is a modest sized new dataset, that includes a new biomarker, and there is an existing prediction model, that does not include this new biomarker. The external information comes in the form of estimates and standard errors of regression parameters from an established prediction model based on a subset of the predictors. We propose a number of different frequentist and Bayesian methods, in which the information on the lower dimensional parameter space is used via inequality constraints and Lagrange multipliers, through prior distributions and through a novel transformation approach. The properties of the approaches will be compared in the situation of continuous and binary response variables. In the second aim the external information comes in the form of a prediction from one or more calculators, and specifically the predictions for each individual in our own data are used. We include in this aim consideration of the situation where there are multiple established prediction models and where the outcome variable is the survival time. We consider different possible methodological approaches, one is an adaptation of the methods in the first aim, a second very general method is to incorporate synthetic data generated from the existing models and a third general method uses weights that enable the new biomarker to have a stronger role for observations that were were not predicted well by the existing models. In the third aim we consider the situation where there may be a panel of new biomarkers, and there is also knowledge about the unadjusted association between each new biomarker and the outcome variable, as might be available from a genome-wide association study. A novel nonparametric Bayes approach is proposed to solve this problem.

项目概要/摘要 临床生物医学中存在大量个体化预后模型，它们被用来预测未来。 源自个体患者特征，并将在迈向个性化治疗的过程中发挥越来越重要的作用 它们可用于早期检测和筛查，或癌症诊断后。 帮助决定治疗，或在治疗后监测进展和复发。 已经很好建立，它们可能有通过使用更大的变量来改进的潜力。 更好质量的训练数据集和改进的统计模型和方法将提高其准确性，但是 由于癌症是一种异质性疾病，因此最大的改善潜力是通过新的生物标志物。 多因素病因学，许多临床和分子因素可能有助于预测患者的未来，以及 我们将在这项研究中解决的挑战是如何去 开发一个新模型，既包含新的生物标志物，又利用现有生物标志物中隐含的知识 当包含新生物标记的可用数据集大小适中时。 要从包含新生物标志物的适度大小的新数据集开发新模型，典型的方法是 将作为一个单独的实体来分析这些数据，并基于该分析构建模型。 方法通常不利用来自已建立模型的外部信息。 可用，但可能以回归系数、比值比或其他汇总统计数据的形式出现 对于变量的子集，或者以在线计算器的预测形式，我们将考虑多种形式。 纳入外部信息的统计方法。 我们提议开发的方法是受到特定头颈癌和前列腺癌研究的启发 等，但对其他癌症和其他疾病有更广泛的适用性。 纳入预测模型的其他新生物标记物包括 HPV 状态和其他分子标记物。 对于前列腺癌风险预测模型，新的双标记物基于测量的蛋白质。 来自尿液。 该研究分为三个具体目标，第一个目标考虑的是适度的情况。 大小的新数据集，包括新的生物标记，并且有一个现有的预测模型，不包括 这个新的生物标志物以估计值和回归标准误差的形式出现。 我们根据预测变量的子集从已建立的预测模型中得出参数。 不同的频率主义和贝叶斯方法，其中低维参数的信息 空间通过不等式约束和拉格朗日乘子、先验分布和新颖的方法来使用 变换方法的性质将在连续和连续的情况下进行比较。 二元响应变量。 在第二个目标中，外部信息以来自一个或多个计算器的预测的形式出现，并且 具体而言，我们将使用我们自己的数据中对每个人的预测。 存在多个已建立的预测模型并且结果变量是的情况 我们考虑不同的可能的方法，其中之一是对方法的调整。 第一个目标，第二个非常通用的方法是合并从现有模型生成的合成数据 第三种通用方法使用权重，使新的生物标志物在观察中发挥更强的作用 现有模型没有很好地预测到这一点。 在第三个目标中，我们考虑可能存在一组新生物标志物的情况，并且也存在 关于每个新生物标志物与结果变量之间未经调整的关联的知识，可能 提出了一种新的非参数贝叶斯方法来解决。 这个问题。