# Missing or Coarsened Data Perspective - Genetic Algorithm ```r 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$x1)} THE LAST ARGUMENT IS ALWAYS TAKEN TO BE THE DATA.FRAME - `fSet`: A function or a list of functions. This argument allows the user to specify the subset of tx options available to a patient or the subset of patients that will be modeled uniquely. see ?fSet for details - `refit`: No longer used - `iter`: An integer. See ?iter for details - `verbose`: A logical. If FALSE, screen prints are suppressed. ## Returns An object inheriting from class OptimalSeq ## Examples ```r # Load and process data set data(bmiData) # define the negative 12 month change in BMI from baseline y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L] # Define the propensity for treatment model and methods. # Will use constant model for both decision points moPropen <- buildModelObj(model = ~ 1, solver.method = 'glm', solver.args = list('family'='binomial'), predict.method = 'predict.glm', predict.args = list(type='response')) moPropen <- list(moPropen, moPropen) # outcome model second stage moMain2 <- buildModelObj(model = ~parentBMI+month4BMI, solver.method = 'lm') moCont2 <- buildModelObj(model = ~race + parentBMI+month4BMI, solver.method = 'lm') # outcome model first stage moMain1 <- buildModelObj(model = ~parentBMI+baselineBMI, solver.method = 'lm') moCont1 <- buildModelObj(model = ~race + parentBMI+baselineBMI, solver.method = 'lm') moMain <- list(moMain1, moMain2) moCont <- list(moCont1, moCont2) # regime function second stage regime2 <- function(eta1, eta2, data) { tst <- {data$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) ``` ## 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()`

optimalSeq function