Methods for Epidemiology Studies

流行病学研究方法

• 批准号: 8565443
• 负责人: Nilanjan Chatterjee
• 金额: $ 323.27万
• 依托单位: DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS
• 依托单位国家: 美国
• 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/8565443
• 关键词: Accounting; Agreement; Architecture; Biometry; Breast; Breast Cancer Detection; Breast Cancer Education; Bronchi; Case-Control Studies; Categories; Cervical; Cohort Studies; Colon; Complex; Computer software; Computers; Confounding Factors (Epidemiology); Consumption; Data; Data Set; Development; Diagnostic tests; Dimensions; Disease; Dose; Environment; Environmental Exposure; Epidemiologic Methods; Epidemiologic Studies; Evaluation; Future; Gene Frequency; General Population; Genes; Genetic; Genomics; Human Papillomavirus; Incidence; Individual; Investigation; Linear Models; Linear Regressions; Logistic Regressions; Lung; Malignant Neoplasms; Measures; Methodology; Methods; Mission; Modeling; Nature; Odds Ratio; Outcome; Outcome Measure; Pap smear; Pattern; Performance; Pleura; Population; Predisposition; Prevention; Rectum; Reporting; Research; Residual state; Risk; Sample Size; Sampling; Scanning; Screening for cancer; Screening procedure; Specimen; Statistical Methods; Structure; Surveys; Techniques; Test Result; Testing; Time; Trachea; Validation; Variant; Woman; base; cohort; cost; design; diagnostic accuracy; disease diagnosis; disorder risk; epidemiology study; follow-up; gene environment interaction; genetic association; genome wide association study; genome-wide; high risk; improved; member; novel; novel diagnostics; prognostic; repository; response; simulation

Investigations have been conducted for using data from current genome-wide association studies to assess genetic architecture of cancer and likely yield of future genome-wide association studies. One project explored distribution of allele frequencies and effect-size and their interrelationships for common susceptibility SNPs using discoveries from existing genome-wide association. It used novel methods to correct for bias as variants with larger effect-sizes are currently over-represented due to their larger statistical power for discovery. The analysis identified several intriguing patterns that can have implications for design and analysis of future genetic association studies. A second project explored potential utility of future discoveries from larger genome-wide association studies for building risk-prediction models that can be potentially utilized for targeting high-risk groups for cancer screening. It was found that although many discoveries are expected from future genome-wide association studies, risk-prediction models based only on discovered SNPs are unlike to identify a small portion of the population that would give rise to the large majority of the future cases. Several projects involved development of statistical methods for exploring gene-gene and gene-environment interactions using data from genome-wide association studies. A new method was developed for modeling interaction of an environmental exposure with multiple SNPs within a genomic region using a Bayesian latent variable modeling approach. Another method exploited an assumption of gene-environment independence in the underlying population to improve the power for the test for gene-environment interaction on the absolute risk of a disease from case-control studies. Another report investigated power for various alternative methods for conducting genome-wide interaction scans using simulation studies. General statistical methods Several studies have been conducted to evaluate efficient design and analysis strategies for epidemiologic studies that use complex sampling designs. One study focuses on the efficient usage of specimen repositories for the evaluation of new diagnostic tests and for comparing new tests with existing tests. Typically, all pre-existing diagnostic tests will already have been conducted on all specimens. It was proposed that retesting only a judicious subsample of the specimens by the new diagnostic test could minimizes study costs and specimen consumption, yet estimates of agreement or diagnostic accuracy potentially retain adequate statistical efficiency. Another project explore efficient analysis method for case-cohort designs that select a random sample of a cohort to be used as control with cases arising from the follow-up of the cohort. Analyses of case-cohort studies with time-varying exposures that use Cox partial likelihood methods can be computer intensive. A new computationally simple method has been developed using piecewise-exponential approach where Poisson regression model parameters are estimated from a pseudo-likelihood and the corresponding variances are derived by applying the corresponding variances are derived by applying Taylor linearization methods that are used in survey research. Several studies have involved development of regression models in a setting that involve potentially a large number of predictor variables. A Bayesian variable selection method has been developed in a setting where the number of independent variables or predictors in a particular dataset is much larger than the available sample size. While most of the existing methods allow some degree of correlations among predictors but do not consider these correlations for variable selection, the proposed method accounts for correlations among the predictors in variable selection. The method could be applied to continuous, binary, ordinal, and count outcome data. Another method is proposed to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker product structure that accommodates the longitudinal nature of the predictors, the method uses first-moment sufficient dimension reduction techniques to replace the original markers with linear transformations that contain sufficient information for the regression of the predictors on the outcome. These linear combinations can then be combined into a score that has better predictive performance than a score built under a general model that ignores the longitudinal structure of the data. Our methods can be applied to either continuous or categorical outcome measures. Several studies have developed methodologies related to models for predicting absolute risk of diseases and their applications. One study has developed two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF(q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF(p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. New methods of inference are developed to compare the PCFs and PNFs of two risk models that are built based on the same validation data. A second project developed a linear-expit regression model (LEXPIT) to incorporate linear and nonlinear risk effects to estimate absolute risk from studies of a binary outcome. The LEXPIT is a generalization of both the binomial linear and logistic regression models. The coefficients of the LEXPIT linear terms estimate adjusted risk differences, while the exponentiated nonlinear terms estimate residual odds ratios. The LEXPIT could be particularly useful for epidemiological studies of risk association, where adjustment for multiple confounding variables is common. The method was applied to estimate the absolute five-year risk of cervical precancer or cancer associated with different Pap and human papillomavirus test results in 167,171 women undergoing screening at Kaiser Permanente Northern Califronia. The LEXPIT model found an increased risk due to abnormal Pap test in HPV-negative that was not detected with logistic regression. An R package blm was developed to provide free and easy-to-use software for fitting the LEXPIT model.

已经进行了研究，用于使用当前全基因组关联研究的数据来评估癌症的遗传结构，并可能对未来基因组关联研究的产量产生产量。一个项目探索了使用现有全基因组关联的发现，探索了等位基因频率和效应大小的分布及其对共同敏感性SNP的相互关系。它使用新颖的方法来纠正偏差，因为由于发现较大的统计能力，目前具有较大效应尺寸的变体被过度代表。该分析确定了几种有趣的模式，这些模式可能对未来遗传关联研究的设计和分析具有影响。第二个项目探索了从较大的全基因组关联研究中的未来发现的潜在效用，用于建立风险预测模型，这些模型可能可用于靶向高危组进行癌症筛查。据研究发现，尽管从未来的全基因组关联研究中获得了许多发现，但仅基于发现的SNP的风险预测模型与确定一小部分人群不同，这将导致大部分未来的案例。几个项目涉及开发使用来自全基因组关联研究的数据探索基因基因和基因环境相互作用的统计方法。开发了一种新的方法，用于使用贝叶斯潜在可变建模方法对环境暴露与多个SNP的相互作用进行建模。另一种方法利用了基本人群中基因环境独立性的假设，以提高基因环境相互作用的能力，以实现病例对照研究的绝对风险。另一份报告调查了使用模拟研究进行各种替代方法进行全基因组相互作用扫描的能力。一般统计方法已经进行了几项研究，以评估使用复杂抽样设计的流行病学研究的有效设计和分析策略。一项研究着重于标本存储库的有效使用，以评估新的诊断测试，并将新测试与现有测试进行比较。通常，所有先前存在的诊断测试都将在所有标本上进行。有人提出，仅通过新的诊断测试重新测试标本的明智子样本可以最大程度地减少研究成本和样本消耗，但估计一致性或诊断准确性的估计可能会保留足够的统计效率。另一个项目探索了案例 - 霍特设计的有效分析方法，该方法选择了一个队列的随机样本，以用作控制，并由该队列的随访引起的病例。对使用Cox部分似然方法的随时间变化的暴露案例研究的分析可能是计算机密集的。已经使用分段 - 指数方法开发了一种新的计算简单方法，其中Poisson回归模型参数是从伪样性估算的，并通过应用在调查研究中使用的泰勒线性化方法得出相应的方差来得出相应的方差。在涉及大量预测变量的环境中，几项研究涉及回归模型的开发。在特定数据集中的自变量或预测变量的数量远大于可用样本量的情况下，已经开发了一种贝叶斯变量选择方法。尽管大多数现有方法都允许预测变量之间的一定程度的相关性，但不考虑这些可变选择的相关性，但该方法解释了可变选择中预测变量之间的相关性。该方法可以应用于连续，二进制，序数和计数结果数据。提出了另一种方法，将随着时间的时间反复测量的几个预测变量（标记）组合为复合标记得分，而无需假设模型，并且仅需要对预测变量分布有轻度的条件。假设预测变量的第一矩和第二矩可以通过Kronecker产品结构分解为一个时间和标记组件，以适应预测变量的纵向性质，则该方法使用了第一张力量的足够尺寸降低技术来替换原始标记，以替换有足够的线性转换的原始标记，这些变换包含足够的预测指标回归结果的信息。然后可以将这些线性组合组合成比在忽略数据纵向结构的一般模型下建立的分数更好的分数。我们的方法可以应用于连续或分类结果指标。几项研究开发了与预测疾病绝对风险及其应用的模型相关的方法。一项研究制定了两个标准，以评估预测疾病发生率筛查和预防风险的模型的实用性，或者在疾病诊断后对管理模型的有用性。第一个标准是遵循PCF（Q）的案件的比例，是将发展疾病的个体的比例，这些疾病被包括在人口中最高风险的个体中的比例Q中。第二个标准是跟进PNF（p）所需的比例，即需要遵循最高风险的普通人群比例，以便将遵循注定要成为案件的人的比例。开发了新的推理方法来比较基于相同验证数据构建的两个风险模型的PCF和PNF。第二个项目开发了线性增长回归模型（LEXPIT），以结合线性和非线性风险效应，以从二元结果研究中估算绝对风险。 Lexpit是二项式线性和逻辑回归模型的概括。 Lexpit线性项的系数估计调整后的风险差异，而指定的非线性项估计了残余优势比。 Lexpit对于风险关联的流行病学研究可能特别有用，在多个混杂变量的调整很常见的情况下。该方法用于估计与不同的子宫颈瘤和人乳头瘤病毒测试相关的宫颈预科或癌症的绝对五年风险，导致167,171名在Kaiser Permanente Northerente Califronia进行筛查的妇女。 Lexpit模型发现，由于HPV阴性异常的PAP检验，风险增加了，而逻辑回归未检测到。开发了R软件包BLM，以提供免费且易于使用的软件，用于拟合Lexpit型号。