DynTxRegime (version 3.01)

rwl:

Description

Estimation of an optimal dynamic treatment regime using residual weighted learning (RWL). The method is limited to single-decision-point scenarios with binary treatment options.

Usage

rwl(..., moPropen, moMain, data, reward, txName, regime,
    lambdas = 2.0, cvFolds = 0L, kernel = "linear",
    kparam = NULL, responseType = "continuous", 
    guess = NULL, verbose = TRUE)

Arguments

…

ignored. Included to require named input.

moPropen

An object of class "modelObj." This object specifies the model of the propensity for treatment regression and the R methods to be used to obtain parameter estimates and predictions. The method specified to obtain predictions must return the prediction on the scale of the probability, i.e., returned values must be in the interval (0,1). See moPropen for further information.

moMain

A single object of class "modelObj." This object specifies the model of the main effects component of the outcome regression and the R methods to be used to obtain parameter estimates and predictions. The method chosen to obtain predictions must return the prediction on the scale of the response variable.

data

An object of class "data.frame." The covariates and treatment histories.

reward

An object of class "vector." A vector of the outcome of interest.

txName

An object of class "character." The column header of the stage treatment variable as given in input data. Treatment must be binary and will be recoded as -1/+1 if not provided as such.

regime

An object of class "formula." The formula defines the decision rule, i.e., the covariates to be included in the kernel.

lambdas

An object of class "numeric." Tuning parameter to avoid overfitting. If more than one is given and cvFolds > 0, cross-validation will be used to select an optimal from among those provided.

cvFolds

An object of class "integer." If cross-validation is to be used to find an optimal lambda and/or kernel parameter, the number of folds to use in the CV.

kernel

An object of class "character." In conjunction with input kparam, this input specifies the kernel function to be used. Must be one of {'linear', 'poly', or 'radial'}. If 'linear,' the linear kernel; kparam is ignored. If 'poly,' the polynomial kernel; kparam must be specified. If 'radial,' the Gaussian radial basis function kernel; kparam must be specified.

kparam

An object of class "numeric." If input kernel = 'linear', this input is ignored. If input kernel = 'poly', this input is the order of the polynomial. If input kernel = 'radial', this input is sigma; i.e., $$K(x,y) = exp(||x-y||^2 / (2*sigma^2)).$$ For kernel = 'radial', a vector of kernel parameters can be provided, and cross-validation will be used to determine the optimal of those provided. Note that input cvFolds must be > 0.

responseType

An object of class "character." One of continuous, binary, count indicating the type of response variable.

guess

An object of class "numeric" or NULL. Starting parameter values for optimization method.

verbose

An object of class "logical." If FALSE, screen prints will be suppressed.

Value

Returns an object of class "RWL" that inherits directly from class "DynTxRegime."

Methods

coef

signature(object = "RWL"): Retrieve parameter estimates for all regression steps.

cvInfo

signature(object = "RWL"): Retrieve cross-validation results.

DTRstep

signature(object = "RWL"): Retrieve description of method used to create object.

estimator

signature(x = "RWL"): Retrieve the estimated value of the estimated optimal regime for the training data set.

fitObject

signature(object = "RWL"): Retrieve value object returned by regression methods.

optimObj

signature(object = "RWL"): Retrieve value object returned by optimization routine.

optTx

signature(x = "RWL", newdata = "missing"): Retrieve the estimated optimal treatment regime for training data set.

optTx

signature(x = "RWL", newdata = "data.frame"): Estimate the optimal treatment regime for newdata.

outcome

signature(x = "RWL"): Retrieve value object returned by outcome regression methods.

plot

signature(x = "RWL"): Generate plots for regression analyses.

print

signature(object = "RWL"): Print main results of analysis.

propen

signature(x = "RWL"): Retrieve value object returned by propensity score regression methods.

regimeCoef

signature(object = "RWL"): Retrieve the estimated decision function parameter estimates.

residuals

signature(object = "RWL"): Retrieve residuals of outcome regression.

show

signature(object = "RWL"): Show main results of analysis.

summary

signature(object = "RWL"): Retrieve summary information from regression analyses.

References

Zhou, X., Mayer-Hamblett, N., Kham, U., and Kosorok, M. R. (2016+). Residual Weighted Learning for Estimating Individualized Treatment Rules. Journal of the American Statistical Association, in press.

Examples

# Load and process data set
  data(bmiData)

  # define response y to be the negative 12 month
  # change in BMI from baseline
  bmiData$y <- -100*(bmiData$month12BMI - bmiData$baselineBMI) /
                     bmiData$baselineBMI


# Constant propensity model
  moPropen <- buildModelObj(model = ~1,
                            solver.method = 'glm',
                            solver.args = list('family'='binomial'),
                            predict.method = 'predict.glm',
                            predict.args = list(type='response'))

# Create modelObj object for main effect component
  moMain <- buildModelObj(model = ~ gender + parentBMI + month4BMI,
                          solver.method = 'lm')
  ## Not run: ------------------------------------
#     rwlRes <- rwl(moPropen = moPropen, moMain = moMain,
#                   data = bmiData, reward = bmiData$y, txName = "A2", 
#                   regime = ~ parentBMI + month4BMI)
# 
# ##Available methods
# 
#   # Coefficients of the propensity score regression
#   coef(rwlRes)
# 
#   # Description of method used to obtain object
#   DTRstep(rwlRes)
# 
#   # Estimated value of the optimal treatment regime for training set
#   estimator(rwlRes)
# 
#   # Value object returned by propensity score regression method
#   fitObject(rwlRes)
# 
#   # Summary of optimization routine
#   optimObj(rwlRes)
# 
#   # Estimated optimal treatment for training data
#   optTx(rwlRes)
# 
#   # Estimated optimal treatment for new data
#   optTx(rwlRes, bmiData)
# 
#   # Value object returned by outcome regression method
#   outcome(rwlRes)
# 
#   # Plots if defined by propensity regression method
#   dev.new()
#   par(mfrow = c(2,4))
#   plot(rwlRes)
# 
#   dev.new()
#   par(mfrow = c(2,4))
#   plot(rwlRes, suppress = TRUE)
# 
#   # Value object returned by propensity score regression method
#   propen(rwlRes)
# 
#   # Parameter estimates for decision function
#   regimeCoef(rwlRes)
# 
#   # Residuals used on method
#   residuals(rwlRes)
# 
#   # Show main results of method
#   show(rwlRes)
# 
#   # Show summary results of method
#   summary(rwlRes)
#  
#   
## ---------------------------------------------