Statistical Methods In Epidemiology--general

该项目为流行病学开发新的统计方法，具有广泛的应用，并为正在进行的流行病学项目，特别是与生殖研究有关的项目开发所需的方法。 今年的工作涉及三个项目。(1)一个项目涉及使用美国出生的双变量出生体重和妊娠长度数据来估计和校正使用末次月经估计妊娠长度的测量误差。 我们的目标是最终制定出生体重的改进标准和每个完整妊娠周的差异，并纠正种族和母亲年龄类别中的早产率估计值。 我们还希望改进对这些类别中出生体重、妊娠期长短和围产期死亡率风险之间关系的评估。(2)另一个项目涉及基于人类样本的昂贵生物标志物的汇总评估。早期的工作表明，在病例对照设置中，可以将病例组和对照组的样本汇集在一起，并进行基于集合的分析。 通过略微修改的逻辑模型，该分析可以估计个体水平的风险参数，并且与基于个体测定的分析相比几乎没有损失功效。 这意味着，如果暴露是基于使用人体样本的昂贵测定，则可以通过在测定前合并样本来显著提高效率。在与我的新博士后Paramita Saha的合作中，我们现在正在将这些方法扩展到怀孕时间数据，其中一个由受孕时间定义的层内的样本池。 同样，与单个水平测定相比，功率几乎完全不受影响，并且成本大大降低。一般来说，共享的另一个巨大好处是保守地使用不可替代的人类标本。(3)第三个项目开发了一种方法，用于测试条件逻辑回归应用中所需的同质性假设。 例如，一个人可能会进行一个基于亲缘关系的分析，涉及遗传和环境因素。 如果测量的遗传标记本身不是致病性的，而是作为未测量的SNP的连锁替代物，那么在家庭之间可能存在异质性，并且该测试可以检测该异质性。

This project develops new statistical methods for epidemiology with broad applications and also methods as needed for ongoing projects in epidemiology, particularly those related to reproductive studies. The work this year involved three projects. (1) One project concerned using the bivariate birth weight and gestational length data for US births to estimate and correct for measurement errors in use of last menstrual period for estimating gestational length. Our goal was ultimately to develop improved standards for birth weights and variances for each completed week of gestation and also to correct the estimated rates of preterm birth within ethnic and maternal age categories. We also hope to improve the assessment of the relationship between birth weight, gestational length and risk of perinatal mortality within those categories. (2) Another project concerns pooled assessment of expensive-to-assay biomarkers based on human samples. Earlier work had shown that in a case-control setting one can pool together specimens from sets of cases and sets of controls and carry out a set-based analysis. With a slightly modified logistic model that analysis can estimate the individual-level risk parameters and loses almost no power compared to analysis based on individual assays. This means that if an exposure is based on an expensive assay that uses human samples, one can markedly improve efficiency by pooling specimens prior to assay. In work with my new post-doc, Paramita Saha, we are now extending these methods to time-to-pregnancy data, where one pools specimens within strata defined by the time to conception. Again the power suffers almost not at all, compared to individual level assays, and the costs are greatly reduced. Another great benefit to pooling in general is in its conservative use of irreplaceable human specimens. (3) A third project developed a method for testing the homogeneity assumption that is required in conditional logistic regression applications. For example, one might carry out a sibship-based analysis involving a genetic and an environmental factor. If the measured genetic marker is not itself causative, but serves as a linked surrogate for an unmeasured SNP, then there could be heterogeneity among families and this test could detect that heterogeneity.