Statistical Methods for Correlated Data in Health Research

我的研究计划的总体目标是开发能够对统计实践产生真实的影响的统计方法。未来五年的具体目标如下：1）开发具有二元结果的复杂观察者间和可靠性研究的方法。 在我的研究的这一部分中，我将开发同时评估观察者间一致性和观察者内可靠性的方法。渐近结果的有限样本性能将与自助法进行比较。这些结果将用于分析几项正在进行的大型研究，我是其中的合作研究者。2)制定社区干预试验的方法，包括事件发生时间结局。这些试验的一个明显特征是，它们通常涉及少量的大集群。然而，可以安全地应用这些试验的方法还没有得到很好的开发。我将对渐近方法提出小样本调整。我还将开发计算密集型方法，如bootstrap。3)二元结果的整群随机试验组内相关系数的置信区间程序的发展。研究者经常使用以前文献中的点估计，而没有充分认识到这些估计固有的精度不足。一种适用于少数大类试验的置信区间方法具有较高的实用价值。我将开发新的程序的基础上，我以前的结果开发了大量的小集群。还将开发用于聚类随机化试验产生的二元结局的新依赖参数的置信区间程序。4)具有二元结果的整群随机试验因果中介分析方法的发展。虽然中介分析是一个自然的工具，说明如何干预工程，几乎没有研究工作已经在集群随机试验的背景下进行。我将使用反事实框架开发相关的统计方法。

The general goal of my research program is to develop statistical methods that can have a real impact on statistical practice. Specific objectives for the next five years are as follows: 1) Development of methods for complex inter-observer and reliability studies with binary outcomes. In this part of my research, I will develop methods for concurrent assessment of both inter-observer agreement and intra-observer reliability. The finite sample performance of asymptotic results will be compared to the bootstrap. These results will be used to analyze several ongoing large studies for which I am a co-investigator. 2) Development of methods for community intervention trials with time-to-event outcomes. A defining feature of these trials is that they often involve a small number of large clusters. However, methods that can be safely applied such trials are not well developed. I will propose small sample adjustments to asymptotic methods. I will also develop computationally intensive approaches such as the bootstrap. 3) Development of confidence interval procedures for the intraclass correlation coefficients in cluster randomization trials with binary outcomes. Investigators frequently use point estimates from previous literature without fully appreciating the inherent lack of precision associated with these estimates. A confidence interval procedure that is applicable for trials with small number of large clusters would have high practical value. I will develop new procedures based on my previous results developed for large number of small clusters. Confidence interval procedure for new dependence parameters for binary outcomes arising from cluster randomization trials will also be developed. 4) Development of methods for causal mediation analysis of cluster randomization trials with binary outcomes. Although mediation analysis is a natural tool for illuminating how an intervention works, virtually no research work has been done in the context of cluster randomization trials. I will develop relevant statistical methods using the counterfactual framework.