Designing and Analyzing Epidemiologic Studies

我们研究了检测基因-环境（G-E）相互作用的仅病例设计的稳健性，发现如果环境暴露和基因在普通人群中相关，估计可能会严重扭曲。我们分析了医院对照所需的条件，以获得基于医院的病例对照研究中G-E相互作用的无偏估计。理想情况下，对照疾病不应受到E或G的影响，并且应该有多个对照组。我们研究了用于估计常染色体显性基因外显率的亲属队列设计的优点和缺点，并开发了对剩余家族相关性可靠的亲属队列数据分析的边缘方法。这些方法和完全最大似然程序是为产生具有和不具有显性突变的受试者累积风险的单调估计而开发的。我们还开发了双变量治愈模型来研究随机选择的家庭成员的成对生存数据。我们开发了一些方法来评估遗传研究中选择的有两个或更多患病成员的家庭的环境因素的风险。这些方法基于随机效应模型，考虑了确定性和遗传相关性，避免了传统分析中忽略这些特征的偏差。完成了三个项目，以提高一致性或不一致性兄弟姐妹对设计的能力，以检测遗传连锁。一项测试中，不和谐对的权重越大。第二个项目研究优化设计。另一种方法是通过一组旨在检测不同可行替代方案的测试，使最小功率最大化。我们研究了以家庭为基础的关联研究的样本量要求，比较受影响的与未受影响的兄弟姐妹。我们还开发了稳健的程序，以确保在基于传递不平衡检验的关联研究的一系列继承模型中具有良好的权力。我们完成了分析汇总DNA样本的统计方法的工作。先前的工作表明，与未合并设计相比，这种方法在估计患病率和识别具有特定罕见等位基因的个体方面是有效的。目前的工作将这些方法扩展到估计两个或多个等位基因的共同患病率。联合患病率可用于估计联合暴露的风险和估计人口不平衡系数。我们开发了统计技术，以区分DNA片段的突变从正常片段使用数据变性高压液相色谱。病例对照和队列研究的设计和分析方法我们描述了从病例队列设计中估计相对风险估计方差的程序，并提出了处理缺失协变量的适应性建议。我们开始了对绝对风险和从这些研究中估计的归因风险的推断方法的相关工作。暴露评估、暴露测量误差和暴露数据缺失我们开发了一种基于样条的方法来估计以前暴露史的不同部分对当前癌症风险的贡献。这项技术被用于扩展双线性加权方法来分析来自科罗拉多铀高原矿工研究的肺癌数据。暴露的超额相对风险在受试者当前年龄前14年达到最大值。对头颈部照射剂量测量误差对甲状腺癌估计风险影响的详细调查考虑了使用虚幻“外部预测数据”产生的berkson型误差、使用回归预测剂量产生的经典误差以及缺失数据。这一分析在一定程度上改变了对年龄在辐照中的作用的估计，但对每单位剂量辐射的过量相对危险度的估计几乎没有变化。我们证明了Hui和Walter在没有金标准的情况下，通过对两个不同患病率人群的重复测量来估计患病率、敏感性和特异性的方法，对于违反共同错误率假设的情况是稳健的。我们使用混合模型来估计没有金标准的情况的普遍程度。例如，这些想法已被应用于估计各种SENV病毒株的流行率。我们回顾了meta分析方法来分析替代标记物的数据，以估计治疗对真正临床终点的影响，并提出了验证替代终点的研究计划。一位研究者开发了一套MATLAB程序，方便使用这种语言进行复杂的统计和流行病学分析。我们计算了用于估计位置和尺度参数的各阶统计量的信息量。我们开发了调整混杂因素标准化死亡率的方法，并在Epicure程序中以计算机代码的形式提供这些方法。

Methods for Genetic Epidemiology We studied the robustness of the case-only design for detecting gene-environment (G-E) interactions and found that estimates can be seriously distorted if the environmental exposure and gene are associated in the general population. We analyzed conditions required for hospital controls to yield unbiased estimates of G-E interactions in hospital-based case-control studies. Ideally, the control diseases should not be influenced either by E or G, and there should be mre than one control group. We studied the strengths and weaknesses of the kin-cohort design for estimating the penetrance of an autosomal dominant gene, and we developed marginal methods of analysis for kin-cohort data that are robust to residual familial correlations. These methods and full maximum likelihood procedures were developed for producing monotone estimates of cumulative risk in subjects with and without a dominant mutation. We also developed bivariate cure models to study survival data from pairs of members of randomly selected families. We developed methods to evaluate risks from environmental factors in families selected for genetic studies to have two or more diseased members. These methods, which are based on random effects models, take ascertainment and genetic correlations into account and avoid biases from conventional analyses that ignore these features. Three projects were completed to increase the power of concordant or discordant sib pair designs for detecting genetic linkage. One test weighs more severely discordant pairs more heavily. A second project studied optimal designs. Another procedure maximizes the minimum power over a set of tests designed to detect different plausible alternatives. We studied sample size requirements for family based association studies comparing affected with unaffected sibs. We also developed robust procedures to assure good power over a range of inheritance models for association studies based on the transmission disequilibrium test. We completed work on statistical methods for analyzing pooled DNA samples. Previous work has shown this approach to be efficient, compared to unpooled designs, for estimating prevalence and identifying individuals with a particular rare allele. The present work extends these methods to the estimation of the joint prevalence of two or more alleles. Joint prevalences have application to estimating risks from joint exposures and to estimating the population disequilibrium coefficient. We developed statistical techniques for discriminating segments of DNA with mutations from normal segments using data from denaturing high pressure liquid chromatography. Methods for Design and Analysis of Case-Control and Cohort Studies We described procedures for estimating variances for relative risk estimates from the case-cohort design and proposed adaptations to handle missing covariates. We began related work on methods of inference for absolute risk and for attributable risk estimated from such studies. Exposure Assessment, Errors in Exposure Measurements, and Missing Exposure Data We developed a method based on splines to estimate the contribution to current cancer risk of various portions of the previous exposure history. This technique was used to extend bilinear weighting methods to analyze lung cancer data from the Colorado Uranium Plateau Miners Study. The excess relative risk from exposure reached a maximum 14 years before the subject's current age. A detailed investigation of the impact of measurement error of the dose of head and neck irradiation on estimated risks of thyroid cancer took into account Berkson-type errors from the use of phantom "external prediction data", classical error from the use of regressions to predict dose, and missing data. This analysis changed estimates of the role of age at irradiation somewhat, but there was little change in the estimate of excess relative risk per unit dose of radiation. Assay Sensitivity and Specificity and Marker Prevalence We showed that a procedure of Hui and Walter to estimate prevalences, sensitivity, and specificity, in the absence of a gold standard, from repeated measurements in two populations with differing prevalences, is robust to violations of the assumption of common error rates. We used mixture models to estimate the prevalence of conditions for which there is no gold standard. These ideas have been applied, for example, to estimated the prevalences of various strains of SENV virus. Other Work We reviewed meta-analytic methods to analyze data on surrogate markers to estimate the effect of treatment on a true clinical endpoint and proposed a research plan to validate surrogate endpoints. One investigator developed a suite of MATLAB programs that facilitate the use of this language for sophisticated statistical and epidemiological analyses. We calculated the information content of various ranges of order statistics for estimating location and scale parameters. We developed methods for adjusting standardized mortality ratios for confounders and made these available as computer code in the program Epicure.