Methods for Epidemiology Studies

一项研究评估了当使用混杂因素的汇总分数（例如倾向分数或疾病风险分数）而不是混杂因素本身来分析观察性数据时，暴露效应估计的渐近偏倚取决于协变量。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，这些模型根据总结得分进行了调整（并进行了匹配），并得出了渐近偏倚。一项研究评估了线性混合模型（lmm）的综合拟合优度检验，通过计算从协变量空间分区内的单元格中计算出的模型的观测值和期望值之间的差异的二次形式。结果表明，在一定的温和条件下，检验统计量具有渐近卡方分布，并导出了检验统计量在局部替代条件下幂的解析表达式。开发了一种新的方法来确定具有共同危险因素的疾病亚型。该方法在国际淋巴瘤流行病学联盟中应用，以显示b细胞淋巴瘤和t细胞淋巴瘤的风险概况存在很大差异。研究人员还开发了一种新的方法，用于估计可归因于介导风险因素的遗传原因的疾病遗传性比例，并使用该方法表明，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，用于根据筛查数据估计风险，将基线时存在的疾病风险与突发疾病的发病风险分开。在这种情况下，标准Kaplan-Meier估计是有偏差的。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），即诊断测试为患者揭示的额外疾病的平均数量。使用MRS发现，对于很少呈阳性的测试，大的风险差异并不意味着良好的风险分层，如果疾病太罕见，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，使用肿瘤材料来检测病例感染的分子研究的关联措施可能会高估或低估感染与随后的癌症风险之间的关系。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，提出了一种统计程序，以提高病例对照研究的逻辑回归模型的效率。开发了一种利用大数据源的外部摘要级信息进行模型校准的约束最大似然分析方法。开展了大量的统计遗传学和基因组学研究，开发了一种强有力的统计程序，以确定对所有疾病亚型具有统一影响或在不同亚型之间具有异质影响的遗传风险因素。提出了一种遗传关联测试，可以解释在替代模型下由于基因-环境相互作用而导致的遗传效应的异质性。一项研究开发了各种测试基因-环境相互作用的方法的扩展，以解释输入的基因型数据。一项研究开发了利用GWAS的摘要水平结果估计效应大小分布的方法。将该方法应用于几种疾病的大GWAS结果表明，涉及数千到数千个易感snp的复杂性状具有高度多基因结构。一些研究正在研究如何提高多基因风险预测模型的性能，这些模型结合了来自大型GWAS的汇总水平结果和效应大小分布的各种类型的先验信息。建立了一种基于似然的检测方法和一种有效的i型错误评估方法，用于癌症驱动基因检测的互斥性分析。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，从而鉴定出一些新的驱动基因。

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.