Statistical Methods for Cancer Biomarkers

癌症生物标志物的统计方法

• 批准号: 9403697
• 负责人: Debashis Ghosh
• 金额: $ 28.39万
• 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/9403697
• 关键词: Address; Aftercare; Bayesian Method; Biological Markers; 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; Statistical Methods; Statistical Models; Time; Training; Update; Urine; Weight; base; cancer biomarkers; cancer diagnosis; genetic predictors; genome wide association study; improved; individual patient; molecular marker; novel; outcome forecast; outcome prediction; personalized medicine; predictive modeling; response; screening; 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.

项目摘要/摘要 个性化预后模型在临床生物医学中比比皆是。它们被用来预测未来， 源于患者的个体特征，并将在向每一位患者转变的过程中发挥越来越重要的作用。 声学医学。它们可用于早期检测和筛查，或在癌症诊断后使用 帮助决定治疗，或在治疗后监测进展和复发。虽然有些型号 都是公认的，它们可能有潜力通过使用额外的变量来改进。更大 更高质量的训练数据集和改进的统计模型和方法将提高它们的准确性，但 最大的改善潜力是通过新的生物标记物。由于癌症是一种异质性疾病， 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 将成为一种新模型的候选者。我们将在这项研究中解决的挑战是如何去掉 开发一个新的模型，既包括新的生物标志物，又利用现有的 模型，当可用的包含新生物标志物的数据集只有中等大小时。 要从包含新生物标志物的中等大小的新数据集开发新模型，典型的方法是 将把这些数据作为一个单独的实体进行分析，并在此分析的基础上建立模型。不过，这个 该方法不利用来自已建立模型的外部信息。这样的外部信息通常会 然而，它可能以回归系数fi因子、优势比或其他汇总统计的形式出现 对于变量的子集，或者以来自在线计算器的预测的形式。我们将考虑各种 纳入外部信息的统计方法。 我们建议开发的方法是受fic头颈癌和前列腺癌研究的启发。 IES，但对其他癌症和其他疾病有更广泛的适用性。在头部和颈部的研究中 要纳入预测模型的其他新生物标志物是HPV状态和其他分子 生物标志物。对于前列腺癌风险预测模型，新的双标记物是基于测量的蛋白质 从尿液中。 这项研究分为三个领域，fic目标。fiFirst目标考虑的情况是有适度的 调整了新数据集的大小，包括新的生物标记物，并且现有的预测模型不包括 这个新的生物标记物。外部信息以估计和回归的标准误差的形式出现 来自基于预测器的子集的已建立的预测模型的参数。我们提出了一个数字 不同的频率和贝叶斯方法，其中关于低维参数的信息 空间是通过不等式约束和拉格朗日乘子，通过先验分布和通过一个新的 转型方法。比较了这两种方法在连续和连续两种情况下的性能 二元响应变量。 在第二个目的中，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 具体来说，我们使用的是我们自己数据中对每个人的预测。fi。我们在这个目标中包含了考虑因素 存在多个已建立的预测模型且结果变量为 生存时间。我们考虑了不同的可能的方法方法，一种是对 fi的第一个目标是，第二个非常普遍的方法是结合从现有模型产生的合成数据 第三种通用方法使用权重，使新的生物标记物在观察中发挥更大的作用 这是现有模型没有很好地预测到的。 在第三个目标中，我们考虑可能有一组新的生物标志物的情况，也有 了解每个新的生物标记物和结果变量之间未经调整的关联 可从全基因组关联研究中获得。提出了一种新的非参数贝叶斯方法来求解 这个问题。