Two-Part Estimation of Treatment Rules for Semi-Continuous Data
Cross validation for hd2part models
Fitting subgroup identification models for semicontinuous positive out...
Main fitting function for group lasso and cooperative lasso penalized ...
Fitting function for lasso penalized gamma GLMs
Fit a penalized gamma augmentation model via cross fitting
Plot method for hd2part fitted objects
Prediction function for fitted cross validation hd2part objects
Prediction method for two part fitted objects
Generates data from a two part distribution with a point mass at zero ...
Implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.
Useful links