We provide tools to estimate the individualized intervalvalued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal intervalvalued dose, by using the jump Qlearning (JQL) method. The jump Qlearning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Qfunction, a residual jump Qlearning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Waldtype confidence interval of value function constructed through the Bootstrap.
Package details 


Author  Hengrui Cai <hcai5@ncsu.edu>, Chengchun shi <cshi4@ncsu.edu>, Rui Song <rsong@ncsu.edu>, Wenbin Lu <wlu4@ncsu.edu> 
Maintainer  Hengrui Cai <hcai5@ncsu.edu> 
License  LGPL3 
Version  3.6.9 
Package repository  View on CRAN 
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