Quantile-Optimal Treatment Regimes with Censored Data
Generate biquadratic kernel weights for a univariate variable
Estimate the marginal mean response of a linear static treatment regim...
Estimate the marginal quantile response of a linear static treatment r...
Estimate the marginal quantile response of a specific dynamic TR
A low-level function for the generic optimization step in estimating M...
A low-level function for the generic optimization step in estimating Q...
A low-level function for the generic optimization step in estimating d...
Estimate the mean-optimal treatment regime for data with independently...
Estimate Quantile-optimal Treatment Regime for covariates-dependent ra...
Estimate the Quantile-opt Treatment Regime under the assumption that t...
Function to estimate the two-stage quantile-optimal dynamic treatment ...
Function to estimate the quantile-optimal treatment regime: the indepe...
Kernel-based Local Kaplan-Meier Estimator
Function to generate simulation data from a sequentially randomized ex...
Kernel-based Local Kaplan-Meier Estimator for the Conditional Probabil...
Provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.