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，因为时间的发展， 无症状疾病的发展（例如，肿瘤发生、HIV感染、糖尿病或高血压发作） 不能精确观察到，而是已知位于两次连续临床检查之间的时间间隔内- 有问题。非参数极大似然估计的理论和计算进展 具有区间删失数据的半参数回归模型承诺更有效和可靠的分析 比目前可能的。这项SBIR提案的广泛、长期目标是创建一套 命令，沿着伴随文本，在广泛使用的商业软件包Stata中执行 常见的考克斯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依赖性协变量，并将该方法扩展到多变量 间隔删失事件时间，当几种无症状疾病或特定疾病复发时出现， 感兴趣的是较大的疾病或者当研究对象被成簇地采样时（例如，家庭，垃圾）。最近 该项目的第一阶段已经完成，成功地建立了科学价值和技术可行性， 建议的研究和开发工作，通过产生一个原型命令的非参数最大像- 协变量可能与时间相关的单变量考克斯比例风险模型的lihood估计 区间删失数据（即，一个单一的事件时间无关的科目），并通过证明的正确性， 新命令的估计结果与已发表的论文和研究代码的结果进行比较。II期 该项目将建立在第一阶段的成功努力，以开发一套可靠，强大，用户友好，速度快， 用于区间删失数据的半参数回归分析的内存效率命令。具体而言， 第一阶段代码将大幅扩展，以纳入分层因子和似然比统计（如 第I阶段中实施的Wald统计的替代方法），用于单变量区间删失数据，以拟合边缘 多疾病和聚类数据的考克斯比例风险模型和比例率/均值模型 为复发事件，并提供单变量和多变量模型的模型检查程序。的 结果的正确性将在五项临床和流行病学研究中得到艾德，加速代码将 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 研究人员新到外地。该SBIR项目生成的软件程序将成为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区间删失数据 以统计有效和无偏见的方式。因此，这一新的工具将有助于寻求有效的内部- 许多常见疾病的预防和预防策略（例如，癌症、艾滋病毒/艾滋病、糖尿病、高血压）， 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.