SOFTWARE FOR MISSING DATA IN CANCER CLINICAL TRIALS

缺失协变量数据的问题是癌症临床试验中常见的问题，因为一些预后因素的数值在一些研究对象中经常丢失或未记录。 当面临缺失协变量值时，主要的商业软件包采用完整病例分析，即使是一个协变量值缺失，受试者的整个记录也会被删除。 众所周知，这种方法会产生有偏差、不一致和效率低下的估计数。 一些软件包利用插补技术来填充缺失值，然后将结果数据集视为完整的数据集。 这些方法是特别的，它们的统计特性还没有得到很好的理解，因为它们不对应于最大似然估计（MLE）（见Little和Rubin，1987）。MLE方法被正确地认为是缺失数据问题的黄金标准。 它以统计理论为坚实基础，产生具有已知分布特性的一致估计。 EM算法的出现，与马尔科夫链蒙特卡罗抽样技术相结合，使得对具有分类、连续或混合协变量的回归模型执行基于MLE的推理在计算上变得可行。 目前还没有商业软件可以利用这一方法上的突破。 该SBIR提案的目标是使用软件填补差距，该软件在存在缺失协变量数据的情况下，对广义线性模型、纵向模型和生存模型执行基于MLE的推断。 最终的软件包将处理可识别和不可识别的缺失。 它将作为外部SAS程序、外部S-plus函数、新LogXact for Windows软件包中的一个选项以及新Egret for Windows软件包中的一个选项提供。缺失协变量数据是生物医学数据的常见属性。在当前的商业软件中，即使只在协变量上缺失数据，整个记录也会被丢弃，从而导致回归模型的估计不一致且效率低下。现在有一个商业机会，可以开发一个友好的软件包，其中包括最新的计算和方法方面的进展，以便在存在缺失数据的情况下，通过最大似然法拟合回归模型。

The problem of missing covariate data is a common one in cancer clinical trials, as the numerical values of some prognostic factors are frequently lost or unrecorded on some of the study subjects. When confronted by missing covariate values, the major commercial software packages resort to a complete-case analysis , whereby a subject s entire record is dropped if even a single covariate value is missing. This approach is known to produce biased, inconsistent and inefficient estimates. A few packages utilize imputation techniques to fill in the missing values and then treat the resulting dataset as though it were complete. These approaches are ad hoc, and their statistical properties are not well understood, for they do not correspond to maximum likelihood estimates (MLE) (see Little and Rubin, 1987). The MLE approach is rightly considered the gold standard for the missing data problem. Firmly grounded in statistical theory, it produces consistent estimates with known distributional properties. The advent of the EM algorithm, combined with Markov-chain Monte Carlo sampling techniques, make it computationally feasible to perform MLE based inference on regression models with either categorical, continuous or mixed covariates. There exists no commercial software to take advantage of this methodological breakthrough. The goal of this SBIR proposal is to fill the gap with software that performs MLE based inference for generalized linear models, longitudinal models, and survival models in the presence of missing covariate data. The final software package will handle both ignorable and non-ignorable missingness. It will be available as external SAS procedures, as external S-plus functions, as an option within the new LogXact for Windows package and as an option within the new Egret for Windows package. PROPOSED COMMERCIAL APPLICATION Missing covariate data is a common property of biomedical data. In current commercial software an entire record is dropped even if it is missing data on only on covariate, thus leading to inconsistent and inefficient estimates for the regression model. There is a commercial opportunity to develop a friendly software package that incorporates the latest computational and methodological advances for fitting regression models by maximum likelihood methods in the presence of missing data.