Software for Missing Covariate Data in Cancer Trials.

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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.