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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.

遗传流行病学方法确定控制选择的严格经典流行病学标准和群体分层（PS）的稳健处理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。癌症易感性遗传标记 (CGEMS) 项目的两个欧洲裔美国人 GWAS 的经验数据被用来评估 PS 在不同对照选择策略的研究中的影响。开发了一种新颖的置换程序来校正 PS，该程序识别出较小的主成分集，并比当前使用的方法更好地控制了 I 类错误。已经开发了病例对照全基因组关联研究 (GWAS) 的程序，该程序选择卡方趋势检验最大（或其相应的 p 值最小）的 SNP。这些分析表明，需要大样本才能获得高检测概率（真正的疾病 SNP 出现在卡方值前列的机会）。这些方法已扩展到两阶段设计。结果表明，第一阶段必须足够大，以便与疾病相关的 SNP 在第一阶段结束时有很大机会进入排名最高的卡方值。否则，无论第二阶段或后续阶段研究了多少病例和对照，检测概率仍然很小。一项研究旨在评估通过在 NCI 的乳腺癌风险评估工具 (BCRAT) 中添加 7 个 SNP（已被证明与适度增加的乳腺癌风险相关）可在多大程度上提高区分准确性，该工具基于一份简短的调查问卷。这些 SNP 仅略微增加了 BCRAT 的区分能力。计算表明，需要数百个此类 SNP 才能获得高区分准确性。在哈迪-温伯格平衡（HWE）和潜在来源群体基因-环境独立性的假设下，通过病例对照研究可以对遗传效应和基因-环境相互作用进行有效分析。然而，当这些假设失败时，此类分析可能会产生误导。经验贝叶斯型收缩估计方法已被开发为一种稳健的遗传分析方法病例对照数据。这些方法可以自适应地将分析缩小到 HWE 和 G-E 独立性下的分布，但仅限于数据证明假设合理的范围内。 BRCAPRO 是一种统计模型，可根据女性乳腺癌和卵巢癌家族史预测其携带 BRCA 基因有害突变的机会。家族史定义为每种疾病的诊断年龄或每个亲属的当前年龄或死亡年龄。 BRCAPRO 已扩展到解释亲属之间的医疗干预（如卵巢切除术），并通过建立严格的审查处理多种疾病的基础来解释除乳腺癌或卵巢之外的 BRCA 相关癌症。常见的临床做法是建议来自 BRCA+ 家族、有乳腺癌病史但亲自检测 BRCA- 的女性，她们的家族史不会使她们比普通人群中的任何人更容易患乳腺癌。华盛顿阿什肯纳兹研究 (WAS) 的数据分析显示，即使是 BRCA 患者，仍然存在残余乳腺癌风险，每一级亲属 (FDR) 患乳腺癌的风险是其 1.5 倍。对这些结果进行后续更复杂的分析，以完善残余风险估计。环境暴露相对风险模型先前已证明，以包年数和强度为单位的三参数超量相对风险模型可以量化每天吸烟 15-20 支以上的吸烟相关的肺癌和膀胱癌风险。通过使用来自芬兰单个前瞻性队列（α-生育酚、β-胡萝卜素癌症预防研究）的数据，这些分析已扩展到检查膀胱癌、食道癌、肾癌、喉癌、肝癌、肺癌、口咽癌和胰腺癌的强度模式。在每天吸烟超过 10 支的情况下，每个癌症部位都发现了“逆暴露率”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同质的，并且与之前多项研究分析的强度效应是同质的。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。泊松回归模型已用于研究铜冶炼工人呼吸道癌死亡率与累积吸入砷暴露之间的关系形状。结果表明，吸入无机砷存在直接的浓度效应，即以较高浓度和较短持续时间输送时，固定累积暴露的超额相对风险大于以较低浓度和较长持续时间输送时的相对风险。暴露评估、暴露测量错误和暴露数据缺失对于大多数疾病，单一生物标志物不具有足够的敏感性或特异性以用于实际目的。已经开发出一种方法，可以将多个生物标志物组合成一个综合标志物评分，而无需假设预测变量的分布模型。使用足够的降维技术，原始标记被替换为较低维度的版本，该版本是通过标记的线性变换获得的，其中包含用于结果预测变量回归的足够信息。该评分的表现通过接受者-操作者特征曲线 (ROC) 下的面积进行评估，ROC 是单个连续诊断标记物对二元疾病结果的区分能力的流行汇总衡量标准。还导出了用于评估个体生物标志物对诊断评分的贡献的渐近卡方检验。对于许多疾病来说，很难或不可能做出明确的诊断，因为完美的“金标准”可能不存在，或者获得的成本可能太高。已提出使用连续测试结果来估计特定人群中疾病患病率并估计可能影响患病率的因素的影响的方法。受乌干达镰状细胞性贫血儿童人类疱疹病毒 8 型研究的启发，该研究使用 2 种酶免疫测定法评估感染状态，建立了 2 组分多元混合模型。使用参数密度对组件密度进行建模，其中包括数据转换以及灵活的转换模型。此外，混合比例（对应于真实未知感染状态的潜在变量的概率）通过逻辑回归建模以纳入协变量。在模拟中评估模型性能，并将模型的各种参数化应用于乌干达研究得出的结果。