Software for Cox Regression Analysis of Interval-Censored Data

项目摘要 区间删失数据在临床和流行病学研究中经常出现，因为发展的时间- 出现无症状疾病(如肿瘤发生、艾滋病毒感染、糖尿病或高血压) 不能准确观察，而是已知位于两次连续临床检查之间的时间间隔内- 定势。非参数极大似然估计的最新理论和计算进展 具有区间截尾数据的半参数回归模型提供了更有效和可靠的fi分析 比目前可能的情况更多。这项SBIR提案的广泛、长期目标是创建一套 广泛使用的商业软件包Stata中的命令和伴随文本，用于执行 熟悉的Cox比例风险模型的前沿非参数极大似然估计 区间删失事件时间的时间相关协变量及将该方法推广到多变量 区间删失事件时间，当几种无症状疾病或某一粒子的复发- 当研究对象被成群抽样(例如，家庭、猫砂)时，人们感兴趣的是大病。最近的 该项目第一阶段的完成，成功地确立了fic的科学价值和技术可行性。 建议的研究和开发工作，通过为非参数最大值生成原型命令，如- 具有潜在时间依赖协变量的单变量Cox比例风险模型的Lihood估计 区间删失数据(即，不相关对象的单个事件时间)，并通过证明 根据发表的论文和研究代码对新命令的结果进行估计。第二阶段 该项目将在第一阶段工作的成功基础上，开发一套可靠、健壮、用户友好、速度和 Memory-effi有效的命令，用于区间删失数据的半参数回归分析。SPECIfiCALLY，即 第一阶段代码将大幅扩展，以纳入Stratifi阳离子因子和似然比统计量(AS 对于单变量区间删失数据的第一阶段中实施的Wald统计的替代方案，到fit边际 多个疾病的COX比例风险模型和聚类率/均值比例模型 对于反复发生的事件，并为单变量和多变量模型提供模型检验程序。这个 结果的正确性将在fiVE临床和流行病学研究中得到验证，加速代码将是 转换为具有图形用户界面和全面文档的商业级程序。 最后，将编写一个配套文本来记录软件本身，并作为以下内容的实质性参考 fi领域的新手研究人员。该SBIR项目生产的软件程序将成为STATA包的一部分- 年龄。这个强大而方便的软件将使生物医学研究人员能够分析间隔审查的数据 以一种统计上正确和公正的方式。因此，这一新工具将有助于寻找有效的相互关系 许多常见疾病(如癌症、艾滋病毒/艾滋病、糖尿病、高血压)的预防和预防战略， 从而改善公共卫生。

Project Summary Interval-censored data arise frequently in clinical and epidemiological studies, because the time to the devel- opment of an asymptomatic disease (e.g., tumor occurrence, HIV infection, onset of diabetes or hypertension) cannot be observed exactly but rather is known to lie in a time interval between two consecutive clinical exam- inations. Recent theoretical and computational advances in nonparametric maximum likelihood estimation of semiparametric regression models with interval-censored data promise far more efficient and reliable analysis than what is currently possible. The broad, long-term objective of this SBIR proposal is to create a suite of commands, along with a companion text, in the widely used commercial software package Stata for performing cutting-edge nonparametric maximum likelihood estimation of the familiar Cox proportional hazards model with time-dependent covariates for interval-censored event times and for extending this methodology to multivariate interval-censored event times, which arise when several asymptomatic diseases or recurrences of a particu- lar disease are of interest or when study subjects are sampled in clusters (e.g., families, litters). The recently completed Phase I of this project has successfully established the scientific merit and technical feasibility of the proposed research and development effort by producing a prototype command for nonparametric maximum like- lihood estimation of the Cox proportional hazards model with potentially time-dependent covariates for univariate interval-censored data (i.e., a single event time for unrelated subjects) and by certifying the correctness of the estimation results from the new command against results from published papers and research code. The Phase II project will build on the success of the Phase I effort to develop a suite of reliable, robust, user-friendly, speed- and memory- efficient commands for semiparametric regression analysis of interval-censored data. Specifically, the Phase I code will be expanded substantially to incorporate stratification factors and likelihood ratio statistics (as an alternative to the Wald statistics implemented in Phase I) for univariate interval-censored data, to fit marginal Cox proportional hazards models for multiple diseases and clustered data and proportional rates/means models for recurrent events, and to provide model-checking procedures for both univariate and multivariate models. The correctness of the results will be certified in five clinical and epidemiological studies, and the sped-up code will be converted into a commercial-grade program with a graphical user interface and comprehensive documentation. Finally, a companion text will be written to document the software itself and serve as a substantive reference for researchers new to the field. The software program produced by this SBIR project will be a part of the Stata pack- age. This powerful and convenient software will enable biomedical investigators to analyze interval-censored data in a statistically efficient and unbiased manner. As such, this new tool will facilitate the search for effective inter- vention and prevention strategies for many common diseases (e.g., cancer, HIV/AIDS, diabetes, hypertension), thereby leading to improvements in public health.