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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.

在全基因组关联研究（GWAS）的设计和分析中，确定控制选择的严格的经典流行病学标准和种群分层（PS）的稳健处理的相关性是一个主要挑战。本文利用欧洲裔美国人癌症易感性遗传标记（CGEMS）项目的两个GWAS的经验数据来评估PS在不同对照选择策略研究中的影响。开发了一种新的排列程序来校正PS，该程序确定了更小的主成分集，并且比目前使用的方法更好地控制了I型误差。已经为病例对照全基因组关联研究（GWASs）开发了程序，选择卡方趋势检验最大（或相应p值最小）的snp。这些分析表明，需要大量样本才能具有较高的检测概率（真正的疾病SNP出现在卡方值顶部的机会）。这些方法已推广到两阶段设计。结果表明，第一阶段必须足够大，以至于在第一阶段结束时，与疾病相关的SNP将有很大的机会具有最高排名的卡方值。否则，无论在第二阶段或后续阶段研究多少病例和对照，发现概率仍然很小。NCI的乳腺癌风险评估工具（BCRAT）基于一份简短的调查问卷，通过增加7个已被证明与乳腺癌风险适度增加相关的snp，已经进行了一项研究，以评估在多大程度上可以提高区分的准确性。这些snp只略微增加了BCRAT的歧视能力。计算表明，需要数百个这样的snp才能达到较高的区分精度。在Hardy-Weinberg-Equilibrium （HWE）和潜在源群体的基因-环境独立性假设下，病例对照研究可以非常有效地分析遗传效应和基因-环境相互作用。然而，当这些假设失败时，这种分析可能会产生误导。经验-贝叶斯型收缩估计方法已经发展成为一种强大的方法来分析遗传病例控制数据。这些方法可以自适应地将分析缩小到HWE和G-E无关的分布，但仅限于数据证明假设的程度。BRCAPRO是一种统计模型，可以根据乳腺癌和卵巢癌的家族史预测女性携带BRCA基因有害突变的几率。家族史定义为每个亲属诊断出每种疾病的年龄或当前年龄或死亡年龄。BRCAPRO已经扩展到考虑亲属之间的医疗干预（如卵巢切除术），也考虑到乳腺癌或卵巢癌以外的brca相关癌症，为通过审查处理多种疾病建立了严格的基础。通常的临床实践是，建议有乳腺癌病史的BRCA+家族的女性，但她们自己检测BRCA-，她们的家族史并不会使她们比一般人群中任何人更容易患乳腺癌。华盛顿德系犹太人研究（WAS）的数据分析表明，即使在BRCA-家族中，每一级亲属（FDR）患乳腺癌的风险仍为1.5倍。对这些结果进行更复杂的分析，以改进剩余风险估计。环境暴露的相对风险模型一个以包年和强度为单位的三参数过量相对风险模型已经被证明可以量化每天吸烟15-20支以上的肺癌和膀胱癌的相关风险趋于稳定。这些分析已经扩展到检查膀胱癌、食道癌、肾癌、喉癌、肝癌、肺癌、口咽癌和胰腺癌的强度模式，使用的数据来自芬兰的一个单一前瞻性队列，即α -生育酚、β -胡萝卜素癌症预防研究。在每天超过10支香烟的情况下，每个癌症部位都发现了“反向暴露率”模式。经过包年调整后，不同癌症部位的强度效应在数量上是均匀的，并且与先前多个研究分析的强度效应是均匀的。强度模式的一致性表明了一种普遍现象，并可能为吸烟相关癌症风险的分子基础提供线索。用泊松回归模型研究了铜冶炼厂工人呼吸系统癌症死亡率与累积吸入砷暴露之间的关系。结果表明，吸入无机砷存在直接的浓度效应，即在较高浓度和较短时间内接触的固定累积暴露的过量相对风险大于较低浓度和较长时间接触的过量相对风险。暴露评估、暴露测量误差和暴露数据缺失对于大多数疾病，单个生物标志物在实际用途上没有足够的敏感性或特异性。已经开发了一种方法，将几个生物标志物组合成一个复合标记评分，而不假设预测因子分布的模型。使用足够的降维技术，原始标记被替换为低维版本，通过标记的线性变换获得，该标记包含足够的信息，用于预测者对结果的回归。该评分的表现是通过接受者-操作者特征曲线（ROC）下的面积来评估的，ROC是一种流行的对二元疾病结果的单一连续诊断标记的区分能力的汇总测量。还推导了用于评估个体生物标志物对诊断评分贡献的渐近卡方检验。对于许多疾病，很难或不可能做出明确的诊断，因为可能不存在完美的“黄金标准”，或者获得这种标准的成本太高。已经提出了使用连续测试结果来估计特定人群中疾病的流行率和估计可能影响流行率的因素的影响的方法。在乌干达开展的一项针对镰状细胞性贫血儿童的人类疱疹病毒8型的研究中，使用了两种酶免疫测定法来评估感染状况，受此启发，我们拟合了一个双组分多变量混合模型。组件密度使用参数密度建模，参数密度包括数据转换和灵活的转换模型。此外，混合比例，即潜在变量对应于真实未知感染状态的概率，通过逻辑回归建模以纳入协变量。在模拟中评估了模型的性能，并给出了将模型的各种参数化应用于乌干达研究的结果。