Statistical methods for longitudinal and survival data

拟议研究的重点是开发新的统计方法，用于分析到事件发生的时间(生存)数据和纵向/分组结果。该研究计划的目标是：在衡量生存结果的整群随机试验中，比较应计因素对样本大小/功效的影响；开发在存在审查的情况下估计生存时间结果的组内相关系数(ICC)的方法；开发标准化事件比(SERS)的区间估计的统计方法；研究无进展和总体生存的回归模型。 在整群随机试验中，个体分组(例如家庭)被随机分为治疗组或干预组，并且必须考虑组内个体之间的相关性。在这种情况下，将开发新的样本量/能量计算方法，以包括患者招募(应计)信息。 ICC被估计为量化集群内观察结果之间的相关性，其中集群包括在完整群体(如家庭)中发现的个体，例如在基因研究中估计遗传力。当感兴趣的结果是生存时间时，删失的观测值遗漏了确切的事件时间。对于聚集的生存时间，ICC的估计因这些丢失的生存时间而变得复杂。将开发新的估计ICC的方法，包括经审查和未经审查的观测值。 标准化发病率和标准化死亡率是标准化事件比率(SERS)的例子。这些比率提供了观察到的和预期的感兴趣事件的比较。从历史上看，SER的可信区间主要是假设观察到的计数服从泊松分布，并假设预期计数是固定的。将开发新的SER间隔估计方法，用于个人可获得随访时间的情况，并有兴趣将各中心与感兴趣的事件进行比较，如比较肾移植中心之间的标准移植比率。 在临床试验中，总生存期(OS)(死亡时间)和无进展生存期(PFS)(疾病进展时间)通常都是有意义的。对于卫生经济分析，个别临床试验结果并不总是可用的，发布的PFS和OS曲线用于重新创建数据，用于成本效益分析。将开发能够在控制协变量的同时调查治疗效果的回归模型。此外，将调查仅公布的PFS和OS汇总曲线而不是个人级别数据的可用性的影响。 申请的资金将主要用于支持学生。最近颁发的认可导师制的奖项表明了将提供的学生监督的质量。

The focus of the proposed research is the development of new statistical methods for the analysis of time to event (survival) data and longitudinal/clustered outcomes. The objectives of the research program are: to compare the effects of accrual on sample size/power in cluster randomized trials that measure survival outcomes; to develop approaches for estimation of the intraclass correlation coefficient (ICC) for survival time outcomes in the presence of censoring; to develop statistical methods for interval estimation for standardized event ratios (SERs) when information on follow up time is available; to investigate regression modelling for progression-free and overall survival. In cluster randomized trials, groups of individuals (e.g. families) are randomized to treatment or intervention groups and the correlation among individuals within groups must be taken into account. New sample size/power calculation approaches in this context will be developed to include patient recruitment (accrual) information. The ICC is estimated to quantify correlation among observations within a cluster where clusters comprise individuals found in intact groups such as families, as in estimating heritability in genetic studies. When outcomes of interest are survival times, censored observations are missing exact event times. For clustered survival times, estimation of ICC is complicated by these missing survival times. New methods for estimating ICC that include both censored and uncensored observations will be developed. Standardized incidence ratios and standardized mortality ratios are examples of standardized event ratios (SERs). These ratios provide comparisons of observed versus expected events of interest. Historically, SER confidence intervals have focused on assuming a Poisson distribution for observed counts and assuming that expected counts are fixed. New methods for SER interval estimation will be developed for the scenario where follow up times are available for individuals and it is of interest to compare centres with respect to the event of interest, as in comparing standardized transplant ratios among kidney transplant centres. Often both overall survival (OS) (time to death) and progression-free survival (PFS) (time to disease progression) are of interest in clinical trials. For health economic analyses, individual clinical trial outcomes are not always available and published PFS and OS curves are used to recreate data for cost benefit analyses. Regression models that can investigate treatment effects while controlling for covariates will be developed. In addition, the impact of availability of only published PFS and OS summary curves rather than individual-level data will be investigated. The requested funding will be used primarily to support students. Recent awards recognizing mentorship speak to the quality of student supervision that will be provided.