Methods for Epidemiology Studies

流行病学研究方法

基本信息

批准号：
7733737
负责人：
Nilanjan Chatterjee
金额：
$ 318.2万
依托单位：
DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/7733737
关键词：
Accounting Age American Area Arsenic Ashkenazim Beta Carotene Bilateral oophorectomy Biological Markers Biometry Bladder Breast Breast Cancer Detection Breast Cancer Education Breast Cancer Risk Assessment Tool Breathing Case-Control Studies Categories Cessation of life Chi-Square Tests Child Cigarette Clinical Colon, Rectum Complex Copper Counseling Data Detection Diagnosis Diagnostic Dimensions Disease Disease Outcome Disease regression Environment Environmental Exposure Enzyme Immunoassay Epidemiologic Methods Epidemiologic Studies Equilibrium Esophagus European Family Family history of Finland Foundations General Population Genes Genetic Markers Gold Human Herpesvirus 8 Individual Infection Intervention Kidney Larynx Liver Logistic Regressions Lung Malignant Neoplasms Malignant neoplasm of ovary Malignant neoplasm of pancreas Malignant neoplasm of urinary bladder Measurement Measures Medical Methodology Methods Mission Modeling Molecular Mutation Oropharyngeal Outcome Ovarian Ovariectomy Pattern Performance Pleura Population Predisposition Prevalence Probability Procedures Purpose Questionnaires Rate Receiver Operator Characteristics Recording of previous events Relative (related person)Relative Risks Residual state Risk Risk Estimate Sampling Score Shapes Sickle Cell Anemia Smoke Smoking Source Specificity Staging Standards of Weights and Measures Statistical Models Stratification Techniques Test Result Testing Trachea and Bronchus Uganda Washington Woman alpha Tocopherol base cancer genetics cancer prevention cancer risk cancer site case control cohort day density design epidemiology study follow-up gene environment interaction genetic analysis genetic association genetic epidemiology genome wide association study improved malignant breast neoplasm member mortality novel prospective respiratory response simulation trend

项目摘要

Methods for Genetic Epidemiology Determination of the relevance of both demanding classical epidemiologic criteria for control selection and robust handling of population stratification (PS) represents a major challenge in the design and analysis of genome-wide association studies (GWAS). Empirical data from two GWAS in European Americans of the Cancer Genetic Markers of Susceptibility (CGEMS) project were used to evaluate the impact of PS in studies with different control selection strategies. A novel permutation procedure was developed for the correction of PS that identified a smaller set of principal components and achieved a better control of type I error than currently used methods. Procedures have been developed for case-control genome-wide association studies (GWASs) that select the SNPs whose chi-square trend tests are largest (or whose corresponding p-values are smallest). These analyses showed that large samples are needed to have a high detection probability (the chance a true disease SNP appears in the top ranks of chi-square values). These methods have been extended to the two-stage design. Results suggest that the first stage must be large enough that a disease-associated SNP will have a large chance of having among the highest ranking chi-square values at the end of the first stage. Otherwise, the detection probability will remain small, regardless of how many cases and controls are studied in the second stage or subsequent stages. A study has been conducted to assess how much the discriminatory accuracy could be improved by adding seven SNPs, which have been shown to be associated with modestly increased breast cancer risk, to the NCI"s Breast Cancer Risk Assessment Tool (BCRAT), which is based on a short questionnaire. These SNPs add only slightly to the discriminatory power of BCRAT. Calculations showed that hundreds of such SNPs would be needed to attain high discriminatory accuracy. Very efficient analyses of genetic effects and gene-environment interactions are possible from case-control studies under the assumption of Hardy-Weinberg-Equilibrium (HWE) and gene-environment independence in the underlying source population. Such analyses, however, can be misleading when these assumptions fail. Empirical-Bayes type shrinkage estimation methodologies have been developed as a robust approach to analysis of genetic case-control data. These methods can adaptively shrink the analysis towards distributions under HWE and G-E independence, but only to the extent the data justify the assumption. BRCAPRO is a statistical model that predicts a woman"s chance of carrying a deleterious mutation in the BRCA genes based on her family history of breast and ovarian cancers. Family history is defined as the age of diagnosis of each disease or current age or age of death for each relative. BRCAPRO has been extended to account for medical interventions (like oophorectomy) amongst relatives and also to account for BRCA-related cancers other than breast or ovarian by developing a rigorous foundation for handling multiple diseases with censoring. It is common clinical practice to counsel women from BRCA+ families with a history of breast cancer, but who personally test BRCA-, that their family history does not predispose them to higher risk of breast cancer than anyone in the general population. Data from the Washington Ashkenazi Study (WAS) has been analyzed to show that there remains a residual breast cancer risk, even amongst the BRCA-, of 1.5-fold per 1st-degree relative (FDR) with breast cancer. These results are being followed up with more sophisticated analysis to refine the residual risk estimates. Models for Relative Risks of Environmental Exposures A three-parameter excess relative risk model in pack-years and intensity has been previously shown to quantify the leveling off smoking related risk of lung and bladder cancer above 15-20 cigarettes per day. These analyses have been extended to examine intensity patterns for incident bladder, esophagus, kidney, larynx, liver, lung, oropharynx, and pancreas cancers by using data from a single prospective cohort in Finland, the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study. At more than 10 cigarettes per day, an "inverse exposure rate" pattern has been found for each cancer site. After adjustment for pack-years, intensity effects were quantitatively homogeneous across the diverse cancer sites and homogeneous with intensity effects from the prior analysis of multiple studies. Consistency of intensity patterns suggested a general phenomenon and may provide clues to the molecular basis of smoking-related cancer risk. Poisson regression model has been used to study the shape of the relationship between respiratory cancer mortality and cumulative inhaled arsenic exposure among copper smelter workers. Results suggested a direct concentration effect from inhaled inorganic arsenic, whereby the excess relative risk for a fixed cumulative exposure was greater when delivered at a higher concentration and shorter duration than when delivered at a lower concentration and longer duration. Exposure Assessment, Errors in Exposure Measurements, and Missing Exposure Data For most diseases, single biomarkers do not have adequate sensitivity or specificity for practical purposes. An approach has been developed to combine several biomarkers into a composite marker score without assuming a model for the distribution of the predictors. Using sufficient dimension reduction techniques, the original markers are replaced with a lower-dimensional version, obtained through linear transformations of markers that contain sufficient information for regression of the predictors on the outcome. The performance of this score is assessed by the area under the receiver-operator characteristics curve (ROC), a popular summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes. An asymptotic chi-squared test for assessing individual biomarker contribution to the diagnostic score is also derived. For many diseases, it is difficult or impossible to establish a definitive diagnosis because a perfect "gold standard" may not exist or may be too costly to obtain. Methods have been proposed to use continuous test results to estimate prevalence of disease in a given population and to estimate the effects of factors that may influence prevalence. Motivated by a study of human herpesvirus 8 among children with sickle-cell anemia in Uganda, where 2 enzyme immunoassays were used to assess infection status, a 2-component multivariate mixture models have been fitted. The compoenent densities are modeled using parametric densities that include data transformation as well as flexible transformed models. In addition, the mixing proportion, the probability of a latent variable corresponding to the true unknown infection status, is modeled via a logistic regression to incorporate covariates. The model performance is assessed in simulations and results are presented from applying various parameterizations of the model to the Ugandan study.

遗传流行病学测定的方法确定两种要求的经典流行病学标准对控制选择和鲁棒人口分层（PS）的相关性是全基因组关联研究（GWAS）的设计和分析中的主要挑战。来自欧洲裔美国人的两个GWA的经验数据使用癌症易感性（CGEM）项目的遗传标记项目来评估PS在具有不同控制选择策略的研究中的影响。开发了一种新颖的置换程序，用于校正PS，该PS确定了一组较小的主组件，并且比当前使用的方法可以更好地控制I型误差。已经开发了针对全基因组关联研究（GWASS）的程序，该研究选择了其卡方趋势测试最大的SNP（或其相应的p值最小）。这些分析表明，需要大型样本具有很高的检测概率（卡方值的最高级别出现了真正的疾病SNP的机会）。这些方法已扩展到两阶段的设计。结果表明，第一阶段必须足够大，以使与疾病相关的SNP在第一阶段结束时具有很大的排名卡方值。否则，无论在第二阶段或随后的阶段研究多少病例和对照，检测概率将保持较小。 A study has been conducted to assess how much the discriminatory accuracy could be improved by adding seven SNPs, which have been shown to be associated with modestly increased breast cancer risk, to the NCI"s Breast Cancer Risk Assessment Tool (BCRAT), which is based on a short questionnaire. These SNPs add only slightly to the discriminatory power of BCRAT. Calculations showed that hundreds of such SNPs would be needed to attain high discriminatory accuracy. Very在遗传效应和基因 - 环境相互作用的有效分析中可以从hardy-Weinberg平衡（HWE）和基因 - 环境独立性的假设中进行的案例对照研究，而这些分析在这些分析中可能会误导这些假设，而这些分析可能会误导这些方法。病例对照数据。 Brcapro是一个统计模型，可以预测女性根据她的乳腺癌和卵巢癌的家族史在BRCA基因中携带有害突变的机会。家族史定义为每个疾病的诊断年龄，每种疾病的诊断年龄，每种相对年龄的诊断年龄。卵巢通过为审查多种疾病而建立严格的基础。每1度亲戚（FDR）与乳腺癌。这些结果得到了更复杂的分析，以完善剩余风险估计。以前已证明，环境暴露相对风险的模型在包装年度和强度的三参数过量的相对风险模型中，可以量化每天15-20次香烟以上的肺和膀胱癌与肺部和膀胱癌相关的风险。这些分析已扩展以检查入射膀胱，食道，肾脏，喉，肝脏，肺，口咽和胰腺癌的强度模式，并使用芬兰单个前瞻性队列，α-托泊酚，β-可托烯预防研究的数据。每天超过10支香烟，每个癌症部位都发现了“反向暴露率”模式。调整包装年后，在各种癌症部位进行了定量均匀的强度效应，并从先前对多项研究的分析中具有强度效应。强度模式的一致性表明一种普遍现象，可能为吸烟相关癌症风险的分子基础提供线索。泊松回归模型已用于研究呼吸癌死亡率和累积吸入铜冶炼厂工人之间关系的形状。结果表明，吸入无机砷的直接浓度效应，在固定累积暴露的相对风险中，以较高的浓度和较短的持续时间交付时，固定累积暴露的过量风险比以较低的浓度和更长的持续时间交付时更大。暴露评估，暴露测量错误以及大多数疾病的暴露数据缺失，单个生物标志物对于实际目的没有足够的敏感性或特异性。已经开发出一种方法将多个生物标记物结合到复合标记得分中，而不假设预测因子的分布模型。使用足够的尺寸缩小技术，原始标记被较低维的版本替换，该标记是通过标记的线性转换获得的，这些标记包含足够的信息，以回归预测因子对结果的回归。该分数的性能由接收器操作器特征曲线（ROC）下的区域评估，这是对二元疾病结果的单个连续诊断标记物的歧视能力的流行摘要度量。还得出了用于评估单个生物标志物对诊断评分的单个生物标志物贡献的渐近卡方检验。对于许多疾病而言，很难或不可能建立确定的诊断，因为完美的“黄金标准”可能不存在或可能太成本太高了。已经提出了使用连续测试结果来估计给定人群中疾病患病率的方法，并估计可能影响流行率的因素的影响。在乌干达患有镰状细胞贫血儿童中人类疱疹病毒8的研究中，使用了2种酶免疫测定来评估感染状况，已安装了2组分量的多元混合物模型。组合密度是使用包括数据转换以及灵活转换模型的参数密度建模的。另外，混合比例，与真实未知感染状态相对应的潜在变量的概率是通过逻辑回归建模以结合协变量的。在模拟中评估了模型性能，并通过将模型的各种参数化对乌干达研究提出了结果。