The software modules listed below were written by members of the Biostatistics Division within the Department of Preventive Medicine of Northwestern University Feinberg School of Medicine. A brief description is provided for each module.
This software is free, however it is provided without any warranty or implied warranty of fitness, correctness, or use for a particular purpose. Use at your own risk.
ADJSURV_CHR - A SAS macro for calculating cumulative hazard ratios and direct-adjusted survival probabilities for K treatment groups based on a stratified Cox regression model. Based on papers by Zhang et al (2007) and Wei and Schaubel (2008), this macro allows one to compare adjusted survival between K treatment groups when the assumption of proportional hazards is violated.
MIDAS - a SAS macro for multiple imputation using distance aided selection of donors. Implements an iterative predictive mean matching hot-deck for imputing missing data
Binary_imp - function determining order of assignment of binary variables from underlying normally distributed data based on quantiles for entries where two or more values had to be imputed.
CombineNestedImputations - An R function for combining inferences based on nested multiple imputations. This R function combines inferences based on nested multiply imputed data sets and calculates rates of missing information.
Mixed_imp - Function for assigning binary values based on quantiles of underlying normally distributed values.
Modified Lurie-Goldberg Code - This code allows for the multiple imputation of multivariate continuous data using a semi-parametric approach, allowing one to relax distributional assumptions of the data, as described in Helenowski and Demirtas (2013), forthcoming in the Journal of Biopharmaceutical Statistics.