| optimalSeq {DynTxRegime} | R Documentation |
Missing or Coarsened Data Perspective - Genetic Algorithm
Description
Missing or Coarsened Data Perspective - Genetic Algorithm
Usage
optimalSeq(
...,
moPropen,
moMain,
moCont,
data,
response,
txName,
regimes,
fSet = NULL,
refit = FALSE,
iter = 0L,
verbose = TRUE
)
Arguments
... |
Additional arguments required by rgenoud. At a minimum this should include Domains, pop.size and starting.values. See ?rgenoud for more information. |
moPropen |
An object of class modelObj, a list of objects of class modelObj, or a list of object of class modelObjSubset, which define the models and R methods to be used to obtain parameter estimates and predictions for the propensity for treatment. See ?moPropen for details. |
moMain |
An object of class modelObj, a list of objects of class modelObj, or a list of object of class modelObjSubset, which define the models and R methods to be used to obtain parameter estimates and predictions for the main effects component of the outcome regression. See ?modelObj and/or ?modelObjSubset for details. NULL is an acceptable input if IPWE is desired or there is no main effects component of the outcome regression model. |
moCont |
An object of class modelObj, a list of objects of class modelObj, or a list of object of class modelObjSubset, which define the models and R methods to be used to obtain parameter estimates and predictions for the contrasts component of the outcome regression. See ?modelObj and/or ?modelObjSubset for details. NULL is an acceptable input if IPWE is desired or there is no contrast component of the outcome regression model. |
data |
A data frame of the covariates and tx history |
response |
The response vector |
txName |
A vector of characters. The column headers of data that correspond to the tx covariate for each decision point. The ordering should be sequential, i.e., the 1st element gives column name for the 1st decision point tx, the 2nd gives column name for the 2nd decision point tx, etc. |
regimes |
A function or a list of functions. For each decision point, a function defining the tx rule. For example, if the tx rule is : I(eta_1 < x1), regimes is defined as regimes <- function(a,data) {as.numeric(a < data$x1)} THE LAST ARGUMENT IS ALWAYS TAKEN TO BE THE DATA.FRAME |
fSet |
A function or a list of functions. This argument allows the user to specify the subset of tx options available to a patient or the subset of patients that will be modeled uniquely. see ?fSet for details |
refit |
No longer used |
iter |
An integer. See ?iter for details |
verbose |
A logical. If FALSE, screen prints are suppressed. |
Value
An object inheriting from class OptimalSeq
References
Baqun Zhang, Anastasios A. Tsiatis, Eric B. Laber & Marie Davidian, "A Robust Method for Estimating Optimal Treatment Regimes", Biometrics, 68, 1010-1018.
Baqun Zhang, Anastasios A. Tsiatis, Eric B. Laber & Marie Davidian, "Robust estimation of optimal treatment regimes for sequential treatment decisions", Biometrika (2013) pp.1-14.
See Also
Other statistical methods:
bowl(),
earl(),
iqLearn,
optimalClass(),
owl(),
qLearn(),
rwl()
Other single decision point methods:
earl(),
optimalClass(),
owl(),
qLearn(),
rwl()
Other multiple decision point methods:
bowl(),
iqLearn,
optimalClass(),
qLearn()
Examples
# Load and process data set
data(bmiData)
# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]
# Define the propensity for treatment model and methods.
# Will use constant model for both decision points
moPropen <- buildModelObj(model = ~ 1,
solver.method = 'glm',
solver.args = list('family'='binomial'),
predict.method = 'predict.glm',
predict.args = list(type='response'))
moPropen <- list(moPropen, moPropen)
# outcome model second stage
moMain2 <- buildModelObj(model = ~parentBMI+month4BMI,
solver.method = 'lm')
moCont2 <- buildModelObj(model = ~race + parentBMI+month4BMI,
solver.method = 'lm')
# outcome model first stage
moMain1 <- buildModelObj(model = ~parentBMI+baselineBMI,
solver.method = 'lm')
moCont1 <- buildModelObj(model = ~race + parentBMI+baselineBMI,
solver.method = 'lm')
moMain <- list(moMain1, moMain2)
moCont <- list(moCont1, moCont2)
# regime function second stage
regime2 <- function(eta1, eta2, data) {
tst <- {data$parentBMI > eta1} & {data$month4BMI > eta2}
rec <- rep('MR', nrow(x = data))
rec[!tst] <- 'CD'
return( rec )
}
# regime function first stage
regime1 <- function(eta1, eta2, data) {
tst <- {data$parentBMI > eta1} & {data$baselineBMI > eta2}
rec <- rep('MR', nrow(x = data))
rec[!tst] <- 'CD'
return( rec )
}
regimes <- list(regime1, regime2)
#### Analysis using AIPW
## Not run:
fit_AIPW <- optimalSeq(moPropen = moPropen,
moMain = moMain, moCont = moCont,
regimes = regimes,
data = bmiData, response = y12, txName = c('A1','A2'),
Domains = cbind(rep(0,4),rep(100,4)),
pop.size = 100, starting.values = rep(25,4))
##Available methods
# Coefficients of the regression objects
coef(object = fit_AIPW)
# Description of method used to obtain object
DTRstep(object = fit_AIPW)
# Estimated value of the optimal treatment regime for training set
estimator(x = fit_AIPW)
# Value object returned by regression methods
fitObject(object = fit_AIPW)
# Retrieve the results of genetic algorithm
genetic(object = fit_AIPW)
# Estimated optimal treatment and decision functions for training data
optTx(x = fit_AIPW)
# Estimated optimal treatment and decision functions for new data
optTx(x = fit_AIPW, newdata = bmiData)
# Value object returned by outcome regression method
outcome(object = fit_AIPW)
# Plots if defined by regression methods
dev.new()
par(mfrow = c(2,4))
plot(x = fit_AIPW)
plot(x = fit_AIPW, suppress = TRUE)
# Retrieve the value object returned by propensity regression method
propen(object = fit_AIPW)
# Show main results of method
show(object = fit_AIPW)
# Show summary results of method
summary(object = fit_AIPW)
## End(Not run)
#### Single Decision Point Analysis using IPW
# Define the propensity for treatment model and methods.
moPropen <- buildModelObj(model = ~ 1,
solver.method = 'glm',
solver.args = list('family'='binomial'),
predict.method = 'predict.glm',
predict.args = list(type='response'))
# regime function second stage
regime <- function(eta1, eta2, data) {
tst <- {data$parentBMI > eta1} & {data$month4BMI > eta2}
rec <- rep('MR', nrow(x = data))
rec[!tst] <- 'CD'
return( rec )
}
## Not run:
fit_IPW <- optimalSeq(moPropen = moPropen,
regimes = regime,
data = bmiData, response = y12, txName = 'A2',
Domains = cbind(rep(0,2),rep(100,2)),
pop.size = 100, starting.values = rep(25,2))
##Available methods
# Coefficients of the regression objects
coef(object = fit_IPW)
# Description of method used to obtain object
DTRstep(object = fit_IPW)
# Estimated value of the optimal treatment regime for training set
estimator(x = fit_IPW)
# Value object returned by regression method
fitObject(object = fit_IPW)
# Retrieve the results of genetic algorithm
genetic(object = fit_IPW)
# Estimated optimal treatment and decision functions for training data
optTx(x = fit_IPW)
# Estimated optimal treatment and decision functions for new data
optTx(x = fit_IPW, newdata = bmiData)
# Value object returned by outcome regression method
outcome(object = fit_IPW)
# Plots if defined by outcome regression method
dev.new()
par(mfrow = c(2,4))
plot(x = fit_IPW)
plot(x = fit_IPW, suppress = TRUE)
# Retrieve the value object returned by propensity regression method
propen(object = fit_IPW)
# Show main results of method
show(object = fit_IPW)
# Show summary results of method
summary(object = fit_IPW)
## End(Not run)