B-SPLINE STATISTICAL TECHNOLOGIES AND HAZARD REGRESSION

事件历史分析方法广泛应用于医学研究： 例如在临床试验中，在药理学研究中， 电离辐射的影响。在乳腺癌和结肠直肠癌筛查中， 癌症和性传播疾病的研究。近来 一直对生物统计学文献相当感兴趣， 在估计对数风险函数中使用样条。我们建议 将这一领域的最新进展和正在进行的进展纳入HARE， 利用自适应多项式样条技术进行事件分析， 历史数据。 当观察到的故障时间由未知的 机制，利益往往在于估计危险，生存，或 密度函数在HARE中，这些函数通过以下方式进行估计： 多项式样条及其所选张量积， 有协变量的多变量生存模型。使用MARS类方法 以自适应地选择样条基函数。HARE还将计算 标准测试统计，提供易于使用的图形用户界面 具有广泛的指导能力， 显示计算的估计和诊断。 拟议的商业应用： 事件历史分析，特别是生存分析，是最重要的分析之一。 大量使用统计方法。HARE模块增加了一个令人兴奋的新功能， 事件历史分析。MathSoft打算瞄准 生物医学和可靠性工程市场。这一切成功都 市场代表着数以万计的潜在用户。很难 设想统计软件竞技场的商业情况， 更有可能成功。

Event history analysis methods are widely used in medical research: for example in clinical trials, in pharmacological studies, in studying the effects of ionizing radiation., in screening for breast and colorectal cancer, and in the study of sexually transmitted diseases. Recently there has been considerable interest in the biostatistics literature regarding the use of splines in estimating the log-hazard function. We propose to incorporate recent and ongoing advances in this area into HARE, a product utilizing adaptive polynomial spline technology for the analysis of event history data. When the observed failure times are generated by an unknown mechanism, interest often lies in estimating a hazard, survival, or density function. In HARE these functions are estimated by means of polynomial splines and their selected tensor products in univariate and multivariate survival models with covariates. MARS-like methods are used to adaptively select the spline basis functions. HARE will also compute standard test statistics, provide an easy-to-use graphical user interface with extensive guidance capabilities, and comprehensively graphically display the computed estimates and diagnostics. PROPOSED COMMERCIAL APPLICATIONS: Event history analysis, especially survival analysis, is one of the most heavily used statistical methods. The HARE module adds an exciting new dimension to event history analysis. MathSoft intends to target the biomedical and reliability engineering markets with HARE. Each of these markets represents tens of thousands of potential users. It is difficult to envision a commercial situation in the statistical software arena that is more likely to succeed.