Developing methods and software for fitting the Cox proportional hazards model to partly interval-censored data

职务名称： 部分区间删失数据拟合考克斯比例风险模型的方法和软件开发 摘要： 部分区间删失的至事件发生时间数据经常出现在涉及周期性疾病的医学研究中。 检查，如癌症，艾滋病毒，传染病和糖尿病。例如，无进展生存期 (PFS)，在III期癌症临床试验中最常用的主要终点，实际上部分是间隔- 删失，因为患者的死亡时间将是确切的，而疾病进展的时间将是 仅已知落在两个成像评估时间点之间。 由于缺乏成熟的方法和软件，部分区间删失数据一直被视为 用于临床试验和医学研究分析的右删失数据，在一开始就引入了偏倚 并且可能导致无效的统计推断和错误的医学结论。拟议项目包括 四个目的：（1）发展一种贝叶斯半参数方法来拟合考克斯比例风险模型， 最常用的生存回归模型，部分区间删失数据;（2）开发贝叶斯 空间脆弱性与空间相关性的考克斯比例风险模型的半参数拟合方法 部分区间删失数据;（3）提出了一种贝叶斯半参数方法来拟合多重脆弱性 对部分区间删失数据进行脆弱性选择的比例风险模型;（4）然后基于 经过验证的新方法，构建一个R包，是有效的，可靠的，用户友好的医疗 调查人员使用。R是最常用的统计计算和图形软件，对公众免费。 因此，这一工具将有助于寻找有效的新药和识别风险因素，从而导致 改善对许多疾病的患者治疗和疾病预防， 公共卫生

Title: Developing methods and software for fitting the Cox proportional hazards model to partly interval-censored data Abstract: Partly interval-censored time-to-event data arise frequently in medical studies for diseases that involve periodic examinations, such as cancer, HIV, infectious diseases, and diabetes. For example, progression-free survival (PFS), the most commonly used primary endpoint in phase III cancer clinical trials, is actually partly interval- censored as the time of death for a patient will be exactly known while the time of disease progression will be only known to fall between two imaging assessment timepoints. Due to the lack of well-developed methods and software, partly interval-censored data have been treated as right-censored data for analysis in clinical trials and medical studies, which introduces bias at the very beginning and may lead to invalid statistical inferences and erroneous medical conclusions. The proposed project contains four aims: (1) to develop a Bayesian semiparametric method for fitting the Cox proportional hazards model, the most commonly used survival regression model, to partly interval-censored data; (2) to develop a Bayesian semiparametric method for fitting the Cox proportional hazards model with spatial frailty to spatially correlated partly interval-censored data; (3) to develop a Bayesian semiparametric method for fitting a multiple-frailty proportional hazards model with frailty selection to clustered partly interval-censored data; (4) then based on the validated new methods, to construct an R package that is efficient, reliable, and user-friendly for medical investigators to use. R is the most used statistical computing and graphics software and is free to the public. Hence, such a tool will facilitate the search for effective new drugs and identification of risk factors, thereby lead to improvement in patient treatment and disease prevention for many diseases that are significantly affecting public health.