SAMPLING-BASED APPROACHES FOR BIOSTATISTICAL INFERENCE

贝叶斯和经验贝叶斯方法能够将信息 从类似的和独立的实验，产生改进的估计 个人和共享的模型特征。 许多复杂的公共 健康问题为这种综合提供了理想的环境。 为 例如，可以将几个相邻县的疾病发病率数据 部分汇集，以产生一个更好的估计，为每个县，因此， 这种跨学科的力量借用。 该提案的重点是 开发必要的抽样方法工具， 分析公共卫生和生物医学科学数据。 这些方法 使一个更广泛的建模框架，因为它们允许精确的 在分析的各个阶段中变化的传播， 消除了对分布变异性的近似的需要， 形状 在简要回顾了过去的几个应用领域之后， 具体到健康科学的建议进行深入探讨。 一是临床试验数据的中期监测和终期分析 被证明是一个潜在的富有成效的应用领域。 在这里， 基于采样的方法将允许对监测进行真实的时间评估 计划，并评估是否基于近似结果， 正态性假设是充分的。 此外，先验类 导致给定的决定，条件是观察到的数据，直到 决策点，可以被表征。 这些结果应该有助于 减轻所选先验可能导致过早停止的担忧 审判的结果 第二个研究领域是贝叶斯的使用， 环境和职业背景下的经验贝叶斯方法 健康 例如，将关于以下物质的致突变效力的信息结合起来， 对几种化学品应能对每种化学品进行更准确的评估。 同样，计算机将允许复杂的，现实的层次模型 生物测定数据，而不采取简化假设或 近似值 这两个具体的建议问题领域不是 本报告旨在详尽无遗，但只是说明广泛的问题， 这是所有生物医学数据分析的基础， 将生物统计学理论与公共统计学相结合的机会流 健康应用。

Bayes and empirical Bayes methods enable the combining of information from similar and independent experiments, yielding improved estimation of both individual and shared model characteristics. Many complex public health problems offer ideal settings for this type of synthesis. For example, data on disease incidence in several adjacent counties may be partially pooled to yield a better estimate for each county, as a result of this borrowing of strength across studies. This proposal focuses on developing necessary sampling based methodological tools for improved analysis of public health and biomedical science data. These methods enable a much broader modeling framework since they allow exact propagation of variation throughout the various stages of the analysis, eliminating the need for approximations to distributional variability and shape. After a brief review of several past areas of application, two areas specific to the health sciences are proposed for in-depth exploration. First, the interim monitoring and final analysis of clinical trials data is shown to be a potentially fruitful area of application. Here, sampling based methods will allow real time evaluation of monitoring plans, and assessment of whether approximate results based on the assumption of normality are adequate. Further, the class of priors leading to a given decision, conditional on the data observed up to the decision point, may be characterized. Such results should help to alleviate concerns that a chosen prior might lead to premature stopping of the trial. A second area for investigation is the use of Bayes and empirical Bayes methods in the context of environmental and occupational health. For example, combining information on the mutagenic potency of several chemicals should produce a more accurate assessment of each. Again, the computer will permit complex, realistic hierarchical modeling of bioassay data without resort to simplifying assumptions or approximations. These two specific proposed problem areas are not intended to be exhaustive, but rather indicative of the broad issues which underlie all biomedical data analyses, and the inevitable future stream of opportunities for combining biostatistical theory with public health application.