Adaptation of New Statistical Ideas for Medicine

我们的MERIT奖工作将继续有两个主要组成部分：参与特定的生物医学研究项目，如NHBLI的FEHGAS研究，以及开发适合分析大型复杂数据集的新统计方法。这些努力是相辅相成的，具体项目表明哪些统计方法是最需要的，也是新方法的测试案例。 例如，FEHGAS研究试图从SNP数据（以及年龄和性别等背景变量）预测高血压发病年龄。有550，000个SNP可用于预测，其中大多数将被证明是无用的，使得问题比表达微阵列情况更具挑战性。Efron计划将他最近的论文中的经验贝叶斯方法扩展到这一背景下，希望克服单个SNP通常较弱的预测能力所造成的困难。Olshen计划将CART（计算机辅助回归树）和引导方法扩展到选择有希望的预测SNP组。 大规模的显著性检验，例如在微阵列癌症研究中选择“显著”基因，已经成为一个密集的统计发展领域。然而，在文献中，适当实施的关键问题仍然模糊不清：选择适当的零假设;选择比较集（所有550，000个SNP应该一起测试还是分别通过染色体测试？）;以及相关性的影响。正如进度报告所述，我们在回答这些问题方面取得了一些进展。我们的持续努力是方法实施和理论发展的结合。 相关性可能对标准统计技术产生特别严重的影响。一组微阵列是否相互独立？他说：“研究显示，一项涉及两万个基因的研究，由于基因间存在严重的相关性，其有效样本量减少到约17个。我们目前正在开发诊断方法，以发现大规模数据集的相关性困难，并评估其对假设检验，估计和预测的影响。一个2万个基因的微阵列研究产生了2亿个相关性，这对于实际的洞察力来说听起来太大了。但我们正在取得进展的经验Ba 5 'es近似，总结相关性，影响在一个单一的数字，适合简单的分析。 20世纪世纪的生物统计学应用在本质上是一种非理性的应用。纯：然而，频率主义对于分析由现代生物医学设备产生的大的、复杂的数据集变得不切实际，其中必须一起考虑数千个参数和数百万个数据点的关系。我们正在继续开发经验贝叶斯方法，使贝叶斯思想能够在多重推理问题上发挥作用，而不需要科学家提供特定的先验分布。 一个长期的项目是了解经验贝叶斯信息在医学研究中的积累速度。错误发现率是在给定观察数据的情况下，基因（或SNP或元音）为“空”的贝叶斯后验概率的估计。为了得到后验概率的准确经验贝叶斯估计，我们需要观察多少受试者和多少基因？ 根据我们自己的摩尔定律，自20世纪90年代以来，生物医学数据集的规模每隔几年就会增加一个数量级。新兴技术（平铺阵列，珠阵列，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，计算设备和统计方法的负担。我们的长期MERIT目标是提供算法和理论适合于数据生物医学的要求。

Our MERIT award work will continue to have two main components: involvement in .specific biomedical research projects such 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 specific projects ¿suggesting which statistical methods are most needed, and also serving as test cases for new methodology. The FEHGAS study, for example,-seeks to predict age of onset of hypertension 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 order of magnitude more challenging, than in expression microarray situations. Efron plans to extend the empirical Bayes methodology 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 Regression 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 intense statistical development. Nevertheless, crucial questions of appropriate implementation 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 separately by chromosome?); and the effects of correlation. We have made some headway in answering these questions, as described in the Progress Report. Our continuing efforts are a combination of methodological implementation and theoretical development. Correlation can have particularly drastic effects on standard statistical techniques. Iii "Are a .set of microarrays independent of each other?" it is shown 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 Century biostatistical applications were overwhelminglyrequentist in nature. Pure: frequentism, though, become impractical 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 probability that a gene (or a SNP, or a vowel) is 'null', given the observed data. How many subjects and how many genes do we need to observe in order to get an accurate empirical Bayes estimate of the posterior probability? In 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 methodology. Our long-term MERIT goal is to provide algorithms and theory appropriate to massive-data biomedical requirements.