SAMPLING-BASED APPROACHES FOR BIOSTATISTICAL INFERENCE

贝叶斯和经验贝叶斯方法使信息能够组合在一起 从类似和独立的实验中，产生了更好的估计 既有个人特征，也有共享模式特征。许多复杂的公众 健康问题为这类合成提供了理想的环境。为 例如，邻近几个县的疾病发病率数据可能是 部分汇集在一起，从而为每个县产生更好的估计 这种跨研究的力量的借用。这项提案的重点是 开发必要的基于抽样的方法工具，以改进 公共卫生和生物医学科学数据分析。这些方法 支持更广泛的建模框架，因为它们允许精确 在分析的不同阶段传播变化， 消除了对分布变异性的近似的需要，并且 形状。 在简要回顾了过去的几个应用领域之后，有两个领域 具体到健康科学方面都提出了深入的探索。 第一，临床试验数据的中期监测和最终分析 被证明是一个潜在的富有成效的应用领域。这里, 基于抽样的方法将允许对监测进行实时评估 计划，以及评估是否基于 假设为正态就足够了。此外，前科的类别 导致给定的决定，条件是观察到的数据 决策点，可以用来刻画。这样的结果应该有助于 缓解对选择的优先顺序可能导致提前停止的担忧 审判的结果。第二个要调查的领域是贝叶斯和 环境和职业背景下的经验贝叶斯方法 健康。例如，将有关诱变效力的信息结合在一起 几种化学物质应该能对每种化学物质做出更准确的评估。 同样，计算机将允许复杂的、逼真的分层建模 生物化验数据而不采用简化假设或 近似值。这两个具体提出的问题领域不是 意在详尽无遗，但更多的是说明广泛的问题 它们构成了所有生物医学数据分析的基础，以及不可避免的未来 将生物统计学理论与公众相结合的机会源源不断 健康应用程序。

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.