Statistical Methodology for Correlated Data in Health Sciences

拟议研究的目标是开发新的统计方法，直接适用于临床和卫生服务研究产生的聚类数据。所涉及的具体问题如下：1）包含少量大群的随机分组试验中风险比的估计; 2）生存模型与群数据的一致性概率的推断; 3）具有二元结果的2阶段偏好随机分组试验的设计和分析; 4）群数据相关性及其差异的推断程序。现有的方法主要集中在优势比上，非统计学家难以解释，因此需要新的方法来估计具有少量大群的整群随机试验的风险比。将制定估计风险比率的程序，以解决大集群数量少的问题。还将制定样本量估计和模型选择的相关方法。随着时间的推移发生的结果的预测是重要的人口健康和卫生服务的研究以及临床实践。有各种各样的技术来量化生存数据的预测模型的区分能力，但很少有集群生存数据的开发。通过扩展在实践中广泛应用的协调概率的概念，将为这种情况开发新的方法。将制定和评价一致性概率及其差异的置信区间程序。治疗偏好反映了个人对治疗的选择，并影响他们对治疗的依从性和结果。包括患者偏好和自我选择在内的多种效应可以使用2阶段随机设计进行估计，该设计允许随机选择的参与者子集选择自己的治疗，其余参与者以通常方式随机分配至治疗组。统计方法现在已经很好地发展为连续的结果，在这种设计中较少关注二元结果。在拟议的研究中，将开发置信区间构建和样本量估计的新程序，并对具有二元结局的2阶段性能随机试验进行评价。相关系数被广泛用于衡量两个变量之间的关联。通常情况下，人们需要评估的相关性，在坐在多个测量可在一个集群内的每个变量，而几乎所有现有的方法假设数据对变量的独立性。新的置信区间的程序，如皮尔逊和斯皮尔曼的聚类数据设置中的常见相关性，将重点提高性能的情况下，少量的集群。

The goal of the proposed research is to develop novel statistical methodology that is directly applicable to clustered data arising from clinical and health services research. Specific questions addressed are: 1) estimation of risk ratios in cluster randomization trials involving a small number of large clusters; 2) inference for the concordance probability of survival models with clustered data; 3) design and analysis of 2-stage preference randomization trials with binary outcomes; and 4) inference procedures for correlations and their differences with clustered data. New methods are needed for estimating risk ratios in cluster randomization trials with a small number of large clusters, because existing methods focus on odds ratio which are uneasy to interpret for non-statisticians. Procedure for the estimation of risk ratios will be developed to address the issue of small number of large clusters. Associated methods for sample size estimation and model selection will also be developed. Prediction of outcomes that occur over time is important in population health and health services research as well as clinical practice. There exists a variety of techniques to quantify the discrimination ability of prediction models for survival data, but few have been developed for clustered survival data. New methods will be developed for such cases by extending the concept of concordance probability that is widely applied in practice. Confidence interval procedures for concordance probabilities and their differences will be developed and evaluated. Treatment preferences reflect individuals' choice of treatment and influence their adherence to treatment and outcomes. Multiple effects including patient preference and self-selection can be estimated using a 2-stage randomized design that allows a randomly selected subset of participants to choose their own treatment, with the remainder randomized to treatment groups in the usual way. Statistical methods are now well-developed for continuous outcomes with less attention paid to binary outcomes in this design. In the proposed research, novel procedures for confidence interval construction and sample size estimation will be developed and evaluated for the 2-stage performance randomized trials with binary outcome. Correlation coefficients are widely used to measure the association between two variables. Oftentimes one needs to assess correlations in sittings where multiple measurements are available on each of the variables within a cluster, whereas virtually all existing methods assume independence of data for pairs of variables. New confidence interval procedures for common correlations such as Pearson's and Spearman's in clustered data settings will be developed with a focus on improving the performance in the case of small number of clusters.