Designing and Analyzing Epidemiologic Studies

遗传流行病学方法我们研究了仅病例设计检测基因-环境(G-E)交互作用的稳健性，发现如果环境暴露与普通人群中的基因相关，估计可能会严重失真。我们分析了在以医院为基础的病例对照研究中，医院对照产生G-E相互作用的无偏估计所需的条件。理想情况下，控制疾病不应受E或G的影响，并且应有一个以上的对照组。我们研究了用于估计常染色体显性基因外显性的亲缘队列设计的优势和劣势，并开发了对剩余家族相关性稳健的亲缘队列数据的边缘分析方法。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的累积风险的单调估计而开发的。我们还开发了双变量曲线模型来研究随机选择的家庭成员对的生存数据。我们开发了评估环境因素的风险的方法，这些风险来自于被选中进行基因研究的有两个或更多患病成员的家庭。这些方法以随机效应模型为基础，考虑了确定性和遗传相关性，避免了忽略这些特征的传统分析的偏差。完成了三个项目，以增加一致或不一致同胞对设计的能力，以检测遗传连锁。有一项测试将更严重的不和谐配对的权重更大。第二个项目研究了最优设计。另一种程序在一系列旨在检测不同合理替代方案的测试中最大化最小功率。我们研究了基于家庭的关联研究的样本量要求，比较了受影响的同胞和未受影响的同胞。我们还开发了健壮的程序，以确保在基于传递不平衡检验的关联研究的一系列遗传模型上具有良好的能力。我们完成了分析混合DNA样本的统计方法的工作。以前的工作已经表明，与未汇集的设计相比，这种方法在估计患病率和识别具有特定稀有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更多等位基因的联合流行率。联合流行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，利用变性高压液相色谱的数据来区分有突变的DNA片段和正常片段。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的方差的程序，并提出了处理缺失协变量的调整建议。我们开始了关于绝对风险和归因风险的推论方法的相关工作，这些研究估计了这些风险。暴露评估、暴露测量误差和丢失暴露数据我们开发了一种基于样条法的方法来估计先前暴露历史的不同部分对当前癌症风险的贡献。这项技术被用来扩展双线性加权方法，以分析来自科罗拉多州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之前14年达到最大值。对头部和颈部照射剂量的测量误差对甲状腺癌风险估计的影响进行了详细的调查，其中考虑了使用幻象“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，以及丢失的数据。这一分析在一定程度上改变了对年龄在辐射中的作用的估计，但对单位剂量辐射的超额相对风险的估计几乎没有改变。检测敏感度、特异度和标志物流行率我们表明，在没有金标准的情况下，通过对两个流行率不同的人群进行重复测量来估计患病率、敏感度和特异度的程序，对于违反共同错误率的假设是稳健的。我们使用混合模型来估计没有黄金标准的疾病的流行率。例如，这些想法已被应用于估计各种SENV病毒株的流行程度。其他工作我们回顾了分析替代标记物数据的荟萃分析方法，以评估治疗对真实临床终点的影响，并提出了验证替代终点的研究计划。一位研究人员开发了一套matlab程序，便于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不同顺序统计量范围的信息量。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.