Methods for Epidemiology Studies

一项研究评估了当使用混杂因素的汇总评分（例如倾向评分或疾病风险评分）而不是混杂因素本身来分析观察数据时，以协变量为条件的暴露效应估计值的渐近偏倚。该研究评估了队列数据、病例对照和匹配病例对照研究的回归模型，这些研究针对汇总评分进行了调整（并匹配），并得出了渐近偏倚。 一项研究评价了线性混合模型（LVMH）的综合拟合优度检验 通过计算从协变量空间的分区的单元内的模型计算的观测值和期望值之间的差的二次形式。 证明了在一定的条件下，检验统计量服从渐近卡方分布，并给出了检验统计量在局部替代下的幂的解析表达式。开发了一种新方法来确定具有共同风险因素的疾病亚型。国际淋巴瘤流行病学联合会应用了这种方法，以显示B细胞和T细胞淋巴瘤的风险特征存在很大差异。还开发了一种新方法来估计可归因于介导风险因素的遗传原因的疾病遗传性的比例，并使用该方法显示约24%的肺癌和7%的膀胱癌遗传性可归因于吸烟的遗传决定因素。 一项研究开发了一种混合模型，用于从筛查数据中估计风险，将基线时存在的疾病风险与突发疾病的风险分开。 在这种情况下，标准Kaplan-Meier估计值存在偏倚。 另一项研究开发了一种新的风险分层框架，称为平均风险分层（MRS），这是诊断测试揭示患者额外疾病的平均数量。 使用MRS，表明大的风险差异并不意味着很少阳性的测试的良好风险分层，如果疾病太罕见，则大的约登指数（或AUC）并不意味着良好的风险分层，一份报告显示，使用肿瘤材料进行分子研究的相关性测量，在病例中检测感染可能会过度或低估感染与随后癌症风险之间的关系。本文提出了一种统计方法，通过一系列无偏估计方程，利用协变量特异性疾病患病率的辅助信息，提高病例对照研究中logistic回归模型的效率。本文提出了一种基于大数据源的约束最大似然分析模型校正方法，并进行了大量的统计遗传学和基因组学研究，建立了一个稳健的统计程序，用于识别对所有疾病亚型具有一致效应或对不同亚型具有异质效应的遗传风险因素。提出了一种遗传关联的检验方法，该方法可以解释在替代模型下由于基因-环境相互作用而引起的遗传效应的异质性。一项研究扩展了各种方法，用于测试基因-环境相互作用，以解释插补的基因型数据。一项研究开发了使用GWAS的汇总水平结果估计效应量分布的方法。该方法应用于几种疾病的大型GWAS结果表明，复杂性状的高度多基因结构涉及数千至数千个易感性SNP。 一些研究正在进行中，以调查如何提高多基因风险预测模型的性能，该模型结合了来自大型GWAS的总结水平结果和各种类型的效应大小分布的先验信息。本文提出了一种基于似然性检验和I类错误评估的有效方法，用于癌症驱动基因检测中的互斥性分析。这些方法被开发并应用于癌症基因组图谱（TCGA）项目的数据分析，从而鉴定出许多新的驱动基因。

A study assessed the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders (e.g. the propensity score or disease risk score) , instead of the confounders themselves, are used to analyze observational data. The study evaluated regression models for cohort data, case-control and matched case-control studies that are adjusted for (and matched on) summary scores and derive the asymptotic bias. A study evaluated omnibus goodness of fit test for linear mixed models (LMMs) by computing a quadratic form of the differences between the observed and expected values computed from the model within cells of a partition of the covariate space. It showed that under some mild conditions, the test statistic has an asymptotic chi-square distribution and derived analytic expressions for the power of the test statistic under a local alternative. A new method was developed for determining subtypes of disease that share common risk factors. This methodology was applied in International Lymphoma Epidemiology Consortium to show strong differences in the risk profiles for B-Cell and T-Cell lymphomas. A new method was also developed for estimating the proportion of disease heritability that can be attributed genetic causes of mediating risk factors, and used this methodology to show that approximately 24% of lung cancer and 7% of bladder cancer heritability can be attributed to the genetic determinants of smoking. A study developed a mixture model for estimating risk from screening data, separating risk of disease present at baseline from risk of onset of incident disease. Standard Kaplan-Meier estimates are biased in this situation. Another study developed a new framework for risk stratification called mean risk stratification (MRS), which is the average amount of extra disease that a diagnostic test reveals for a patient. Using MRS, it was shown that a big risk difference does not imply good risk stratification for tests that are rarely positive, that a large Youden's index (or AUC) does not imply good risk stratification if disease is too rare, and that the expected benefit of a diagnostic test is a function of the test solely through MRS. A report showed that measures of association for molecular studies that use material from tumors to detect infection in cases can over- or underestimate the relationship between infections and subsequent cancer risk. A statistical procedure has been proposed to improves the efficiency of the logistic regression model for a case-control study by utilizing auxiliary information on covariate-specific disease prevalence via a series of unbiased estimating equations. A method was developed for constrained maximum likelihood analysis for model calibration using external summary-level information from big-data sources.A number of studies statistical genetic and genomics were conducted A robust statistical procedure has been developed to identify genetic risk factors that have either a uniform effect for all disease subtypes or heterogeneous effects across different subtypes. A test for genetic association was proposed that can account for heterogeneity in genetic effects due to gene-environment interactions under alternative models. A study developed extensions of various methods for testing gene-environment interactions to account for imputed genotype data. A study developed method for estimation of effect-size distribution using summary-level results from GWAS. Application of the method to results from large GWAS of several diseases indicate highly polygenic architecture of complex traits involving thousands to tends of thousands of susceptibility SNPs. Several studies are ongoing to investigate how to improve performance of polygenic risk prediction models incorporating summary-level results from large GWAS and various types of prior information on effect-size-distribution. A likelihood-based test and a valid method for type-I error evaluation were developed for mutual exclusivity analysis in detection of cancer driver gene. The methods were developed and applied for analysis of data from the The Cancer Genome Atlas (TCGA) project leading to identification a number of novel driver genes.