Adaptation of New Statistical Ideas for Medicine

我们的获奖工作将继续包括两个主要组成部分：参与特定的生物医学 研究项目，如NHBLI的FEHGAS研究，以及适当的新统计方法的开发 用于分析大型、复杂的数据集。这些努力与专门项目是相辅相成的。 ？建议哪些统计方法是最合适的，并作为新方法的测试案例。 例如，FEHGAS研究试图根据SNP数据预测高血压的发病年龄(和 背景变量，如年龄和性别)。有550,000个SNP可用于预测，其中大多数 这将被证明是无用的，使这个问题成为一个更具挑战性的问题，而不是 表达微阵列的情况。Efron计划扩展经验贝叶斯生活方法，从他最近的 希望克服通常预测能力较弱所造成的困难 单个SNPs的数量。Olshen计划扩展CART(计算机辅助回归树)和Bootstrap 选择有前景的预测性SNPs群体的方法学。 大规模的显著性测试，例如在微阵列癌症研究中选择“重要的”基因， 已经成为智能统计发展的一个领域。然而，适当实施的关键问题 在文献中保持模糊：选择适当的零假设；选择一个 比较集(所有550,000个SNP应该一起检测还是单独检测染色体？)；以及影响 关联性。正如《进展》中所述，我们在回答这些问题方面取得了一些进展 报告。我们的持续努力是方法论实施和理论发展的结合。 相关性对标准统计技术有特别重要的影响。III“是一组微阵列 一项涉及2万个基因的研究有了有效的样本，这是不争的事实。 由于严重的基因相关性，其大小减少到约17个。我们目前正在开发诊断程序 方法在海量数据集中找出相关性困难，并评估它们对假设检验的影响， 估计和预测。一项20,000个基因微阵列研究产生了200,000,000个相关性，这听起来 对于实际的洞察力来说，大得令人难以忍受。但我们在经验Ba5‘s近似上取得了进展 这将相关性、影响汇总在一个数字中，适合进行简单分析。 二十世纪生物统计学的应用在自然界中占压倒性的频率。纯净：频率主义， 然而，对于分析现代生物医学产生的大型、复杂的数据集来说，这是至关重要的 设备，其中必须考虑数千个参数和数百万个数据点的关系 在一起。我们正在继续开发经验贝叶斯方法，允许将贝叶斯思想带到 涉及多重推理的问题，而不需要科学家提供特定的先验分布。 一个长期项目是了解经验贝叶斯信息在医学研究中积累的速度有多快。 错误发现率是对基因(或SNP，带来体素)的贝叶斯后验概率的估计 根据观察到的数据，为‘NULL’。我们需要观察多少个对象和多少个基因 以得到后验概率的精确经验贝叶斯估计？ 在我们自己版本的摩尔定律中，生物医学数据集的大小每增加一个数量级 自20世纪90年代以来的几年。新兴技术(瓷砖阵列、珠子阵列、适体芯片、甲基化阵列、 外显子芯片和各种新的成像设备)承诺进一步增加，对计算 设备和统计方法学。我们的长期目标是提供算法和理论 适当的tp海量数据生物医学要求。

Our MERIT award work will continue to have two main components: involvement in .specific biomedical reseai-ch projects sucli as NHBLI's FEHGAS study, and development of new statistical methods appropriate for the analysis of large, complex data sets. These efforts are complementary, with the speciflc projects ¿suggesting which statistical rnethods are mofit needed, and also serving as test cases for new methodology. The FEHGAS study, for exarhple,- seeks to predict age of onset of hypertiension from SNP data (and background variables such as age and gender). There are 550,000 SNPs available for prediction, most of which will turn out to be useless, making the problem an ijrder of magnitude more challenging, than in expression microarray situations. Efron plans to extend the empirical Bayes liiethodology from his recent paper to this context, hopefully overcoming the difficulties caused by the usually weak predictive power of individual SNPs. Olshen plans to extend CART (Computer Assisted Regre.s.sion Trees) and bootstrap methodology to the selection of groups of promising predictive SNPs. Large-scale significance testing, for instance selecting 'significant' genes in a microarray cancer study, has become an area of iiitense statistical development. Nevertheless, crucial questions of appropriate implomentation remain vague in the literature: the choice of an appropriate null hypothesis; the selection of a comparison set (Should all 550,000 SNPs be tested together or sepai-ately by chromosome?); and the effects of correlation. We have made some headway in answering thescf questions, as described in the Progress Report. Our continuing efforts are a combination of methodological implementation and theoretical development. Correlatiion can have particularly dra.stic effects on staiidard statistical techniques. Iii "Are a .set of microarrays independent of each other?" it is shovyn that a study involving 20,000 genes has its effective sample size reduced to about 17 because of severe gene-wise correlation. We are currently developing diagnostic methods to spot correlation difficulties in massive data sets, and to assess their effects on hypothesis tests, estimates, and predictions. A 20,000 gene microarray study produces 200,000,000 correlations, which sounds oppressively large for practical insight. But we are making progress on an empirical Ba5'es approximation that summarizes correlation, effects in a single number, suitable for simple analysis. Twentieth Centiiry biostatistical applications were overwhelmingly frequentist in nature. Pure: frequentism, though, becomfSi impra<;tical for analyzing the large, complex data sets produced by modem biomedical devices, where the relationships of thousands of parameters and millions of data points have to be considered together. We are continuing to develop empirical Bayes methods that allow Bayesian ideas to be brought to bear on questions of multiple inference, without requiring specific prior distributions from the .scientist. A long-term project is to understand how quickly empirical Bayes information accrues in a medical study. A False Discovery Rate is an estimate of the Bayes posterior probabiUty that a gene (or a SNP, br a voxel) is 'null', given the observed data. How many subjects and how many genes do we need to observe in order to get an acciurate empirical Bayes estiinate of the posterior probability? hi our own version of Moore's law, biomedical data sets have increased an order of magnitude in size every few years since the 1990s. Emerging technologies (tiling arrays, bead arrays, aptamer chips, methylation arrays, exon chips, and a variety of new imaging devices) promise further increases, taxing both computational equipment and statistical inethodology. Our long-term MERIT goal is to provide algorithms and theory appropriate tp massive-data biomedical requirements.