Software for Missing Covariate Data in Cancer Trials.

用于癌症试验中缺失协变量数据的软件。

• 批准号: 6694649
• 负责人: CYRUS R MEHTA
• 金额: $ 37.5万
• 依托单位: CYTEL, INC
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-13 至 2005-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6694649
• 关键词: clinical research; clinical trials; computer program /software; computer system design /evaluation; data collection methodology /evaluation; data management; human data; mathematical model; statistics /biometry

DESCRIPTION (provided by applicant): This is a Phase II SBIR proposal for completing the development of a comprehensive collection of statistical tools embedded in LogXact, in EGRET, in SAS as PROCs and in SPLUS as functions. This set of tools will compute maximum likelihood estimates for generalized linear models (GLMs) and parametric survival models with missing categorical covariates, where the missing covariates are assumed to be missing at random (MAR). In this Phase II effort, we will expand the current version of tools available in prototype software XMISS to handle: (i) missing categorical covariates for binomial response models with Iogit, probit, or complementary log-log links, (ii) missing categorical covariates for conditional logistic regression for matched case-control data, (iii) missing categorical covariates for Poisson regression models, (iv) missing categorical covariates for normal linear regression models, (v) missing categorical covariates for ordinal response regression models, (vi) missing categorical covariates for exponential, Weibull and log-normal regression models allowing for right censoring in the response variable. In the development of the Phase II software for all of the GLM's and survival models considered above, we will allow a missing covariate to have up to 5 categories, any of which may have missing values. Also, in Phase II development, we will allow up to 50 covariates total, of which 10 binary covariates can be missing. In addition, we will investigate methods for speeding up the EM algorithm as well as develop new algorithms for obtaining good starting values for the EM algorithm. Missing covariate data is very common problem with cancer clinical trials. There exists no commercial software to handle missing covariate data by maximum likelihood method for the range of models listed above.

描述（由申请人提供）： 这是一项II期SBIR建议，用于完成嵌入在logxact，egret，sas中的saS和splus中的logxact中的统计工具的全面集合。这组工具将计算通用线性模型（GLM）和具有缺失分类协变量的参数生存模型的最大似然估计，其中假定缺失的协变量在随机（MAR）中丢失。在这二阶段的工作中，我们将扩展当前版本的原型软件XMISS中可用的工具以处理： （i）缺少具有IOGIT，ProbIT或互补日志链接的二项式响应模型的分类协变量， （ii）缺少用于匹配病例对照数据的条件逻辑回归的分类协变量， （iii）缺少用于泊松回归模型的分类协变量， （iv）缺少正常线性回归模型的分类协变量， （v）缺少用于顺序响应回归模型的分类协变量， （vi）缺失指数，微量和对数正态回归模型的分类协变量，允许对响应变量进行正确的审查。 在上面考虑的所有GLM和生存模型的II期软件的开发中，我们将允许缺失的协变量具有多达5个类别，其中任何一个可能缺少值。同样，在第二阶段开发中，我们将允许多达50个协变量，其中10个二元协变量可能会丢失。此外，我们将研究加快EM算法的方法，并开发新算法以获得EM算法的良好起始值。 缺少协变量数据是癌症临床试验的非常普遍的问题。没有商业软件可以通过上述模型范围的最大似然方法处理缺失的协变量数据。