Statistical Methodology for Correlated Data in Health Sciences

拟议研究的目标是开发直接适用于来自临床和卫生服务研究的集群数据的新的统计方法。具体问题包括：1)涉及少量大群组的整群随机试验中风险比的估计；2)利用群组数据推断生存模型的一致性概率；3)设计和分析具有二元结果的两阶段偏好随机化试验；以及4)与群组数据的相关性及其差异的推断程序。 在具有少量大群组的整群随机试验中，由于现有方法侧重于非统计学家难以解释的优势比，因此需要新的方法来估计风险比。将制定估计风险比率的程序，以解决数量较少的大型集群的问题。还将开发估计样本量和选择模型的相关方法。 对随时间发生的结果的预测在人群健康和健康服务研究以及临床实践中都很重要。已有多种技术来量化生存数据预测模型的区分能力，但很少有人开发用于聚集生存数据的预测模型。对于这种情况，将通过扩展在实践中广泛应用的协调概率的概念来开发新的方法。将开发和评估一致性概率及其差异的可信区间程序。 治疗偏好反映了个人对治疗的选择，并影响他们对治疗的坚持和结果。包括患者偏好和自我选择在内的多种影响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案，其余的以通常的方式随机分配到治疗组。统计方法现在已经很好地用于连续结果，而在本设计中对二元结果的关注较少。在这项拟议的研究中，将开发新的可信区间构建和样本量估计方法，并对具有两个结果的两阶段随机试验进行评估。 相关系数被广泛用来衡量两个变量之间的关联性。通常，人们需要评估Sitting中的相关性，其中一个簇中的每个变量都有多个测量可用，而几乎所有现有的方法都假设变量对的数据是独立的。将为集群数据设置中的皮尔逊和斯皮尔曼等常见相关性开发新的置信度区间程序，重点是改善在小数量集群的情况下的性能。

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.