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。具体解决的问题有：1)涉及少量大集群的集群随机化试验的风险比估计；2)聚类数据下生存模型的一致性概率推断；3)设计和分析具有二元结果的两阶段偏好随机化试验；4)聚类数据相关性及其差异的推理过程。由于现有的方法侧重于比值比，对于非统计人员来说难以解释，因此需要新的方法来估计具有少量大聚类的聚类随机化试验的风险比。将制定评估风险比率的程序，以解决大集群数量少的问题。还将开发样本量估计和模型选择的相关方法。预测随时间推移而发生的结果在人口健康和卫生服务研究以及临床实践中很重要。目前存在多种量化生存数据预测模型判别能力的技术，但很少有针对聚类生存数据的技术。通过扩展在实践中广泛应用的一致性概率的概念，将开发新的方法来解决这种情况。将开发和评估一致性概率及其差异的置信区间程序。治疗偏好反映了个体对治疗的选择，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重效应可以使用两阶段随机设计来估计，该设计允许随机选择的参与者子集选择他们自己的治疗方法，其余的以常规方式随机分配到治疗组。统计方法现在已经发展得很好，用于连续结果，在本设计中对二元结果的关注较少。在本研究中，将开发新的置信区间构建和样本量估计程序，并对具有二元结果的两阶段随机试验进行评估。相关系数被广泛用于度量两个变量之间的关联。通常情况下，人们需要在集群中对每个变量都有多个测量值的情况下评估相关性，而实际上所有现有的方法都假设变量对的数据是独立的。新的置信区间程序为常见的相关性，如聚类数据设置中的Pearson和Spearman，将重点放在提高在少量聚类情况下的性能。

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.