Statistical methods for longitudinal and survival data

拟议研究的重点是开发新的统计方法，用于分析时间到事件（生存）数据和纵向/聚类结果。该研究项目的目的是：比较应计制对测量生存结果的集群随机试验中样本量/功率的影响；开发在审查存在的情况下生存时间结果的类内相关系数（ICC）的估计方法；在获得随访时间信息的情况下，制定标准化事件比率（SERs）间隔估计的统计方法；研究无进展生存期和总生存期的回归模型。******在聚类随机试验中，个体组（如家庭）被随机分配到治疗组或干预组，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/功率计算方法，以包括患者招募（应计）信息。****** ICC被估计为量化一个簇内观察结果之间的相关性，其中簇包括在完整群体（如家庭）中发现的个体，如在遗传研究中估计遗传能力。当感兴趣的结果是生存时间时，审查后的观察会丢失确切的事件时间。对于聚类生存时间，这些缺失的生存时间使ICC的估计变得复杂。将开发新的方法来估计包括审查和未审查的观测值的ICC。******标准化发病率比和标准化死亡率是标准化事件比（SERs）的例子。这些比率提供了观察事件与预期事件的比较。从历史上看，SER置信区间集中在假设观察计数的泊松分布和假设预期计数是固定的。将开发SER区间估计的新方法，用于个人可获得随访时间的情况，并且比较中心与感兴趣的事件有关，如比较肾移植中心之间的标准化移植比率。******在临床试验中，总生存期（OS）（到死亡的时间）和无进展生存期（PFS）（到疾病进展的时间）通常都是值得关注的。对于卫生经济分析，个体临床试验结果并不总是可用的，并使用已发表的PFS和OS曲线来重新创建数据以进行成本效益分析。可以在控制协变量的同时调查治疗效果的回归模型将被开发。此外，将调查仅发表的PFS和OS汇总曲线而不是个人水平数据的可用性的影响。******申请的资金将主要用于支持学生。最近授予导师的奖项说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.