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Statistical Methods for Cancer Biomarkers

癌症生物标志物的统计方法

基本信息

批准号：
10199945
负责人：
Debashis Ghosh
金额：
$ 27.27万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-01-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10199945
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 Prognosis 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 prediction personalized medicine predictive modeling prognostic model prostate cancer risk response risk prediction model statistics ward

项目摘要

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.

项目总结/摘要个体化预后模型在临床生物医学中大量存在。它们被用来预测未来，从个体患者的特点，并将发挥越来越重要的作用，在走向每- 超声波医学它们可用于早期检测和筛查，或癌症诊断后帮助决定治疗，或治疗后监测进展和复发。虽然有些模型如果这些指标已经确立，则很可能通过使用其他变量得到改进。较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其准确性，最大的改进潜力在于通过新的生物标志物。由于癌症是一种异质性疾病，多因素病因，许多临床和分子因素可能有助于预测患者的未来，将被纳入新模式的候选人。我们将在这项研究中解决的挑战是如何去- velop一种新的模型，既包括新的生物标志物，又利用现有生物标志物中隐含的知识。当包含新生物标志物的可用数据集仅具有适度大小时，为了从包含新生物标志物的适度大小的新数据集开发新模型，典型的方法是将分析这些数据，作为一个单独的实体，并建立一个基于该分析的模型。但这该方法不利用来自已建立模型的外部信息。这些外部信息通常可用，但它可能以回归系数、比值比或其他汇总统计的形式出现或者以在线计算器的预测的形式。我们将考虑各种统计方法，以纳入外部信息。我们建议开发的方法是由特定的头颈癌和前列腺癌研究激发的。但对其他癌症和其他疾病具有更广泛的适用性。在头部和颈部研究中，待并入预测模型中的另外的新生物标志物是HPV状态和其它分子生物标志物。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。研究分为三个具体目标。第一个目标考虑的情况下，有一个温和的大小的新数据集，包括新的生物标志物，并且存在现有的预测模型，不包括这个新的生物标志物。外部信息以估计值和回归标准误差的形式出现基于所述预测器的子集，从所建立的预测模型中提取参数。我们提出一个数字不同的频率论和贝叶斯方法，其中低维参数的信息空间是通过不等式约束和拉格朗日乘数，通过先验分布，并通过一个新的转化方法。在连续和非连续的情况下比较了这两种方法的性质，二进制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，并且具体地，使用我们自己的数据中每个个体的预测。我们在这个目标中考虑到有多个已建立的预测模型，结果变量是生存时间我们考虑不同的可能的方法论途径，一个是适应的方法，第一个目标，第二个非常普遍的方法是将现有模型生成的合成数据第三种通用方法使用权重，使新生物标志物在观察中发挥更大的作用现有的模型并没有很好地预测到。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，并且还存在关于每种新生物标志物与结果变量之间未校正关联的知识，可以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解决这个问题