fitIterTMLE: Iterative TMLE wrapper for 'fitSeqGcomp'

In stremr: Streamlined Estimation of Survival for Static, Dynamic and Stochastic Treatment and Monitoring Regimes

Description

Calls fitSeqGcomp with argument iterTMLE = TRUE.

Usage

fitIterTMLE(...)

Arguments

...	Arguments that will be passed down to the underlying function fitSeqGcomp

Value

data.table with survival by time for sequential GCOMP and iterative TMLE

See Also

fitSeqGcomp

Examples

options(stremr.verbose = TRUE)
require("data.table")
set_all_stremr_options(fit.package = "speedglm", fit.algorithm = "glm")

# ----------------------------------------------------------------------
# Simulated Data
# ----------------------------------------------------------------------
data(OdataNoCENS)
OdataDT <- as.data.table(OdataNoCENS, key=c(ID, t))

# define lagged N, first value is always 1 (always monitored at the first time point):
OdataDT[, ("N.tminus1") := shift(get("N"), n = 1L, type = "lag", fill = 1L), by = ID]
OdataDT[, ("TI.tminus1") := shift(get("TI"), n = 1L, type = "lag", fill = 1L), by = ID]

# ----------------------------------------------------------------------
# Define intervention (always treated):
# ----------------------------------------------------------------------
OdataDT[, ("TI.set1") := 1L]
OdataDT[, ("TI.set0") := 0L]

# ----------------------------------------------------------------------
# Import Data
# ----------------------------------------------------------------------
OData <- importData(OdataDT, ID = "ID", t = "t", covars = c("highA1c", "lastNat1", "N.tminus1"),
                    CENS = "C", TRT = "TI", MONITOR = "N", OUTCOME = "Y.tplus1")

# ----------------------------------------------------------------------
# Model the Propensity Scores
# ----------------------------------------------------------------------
gform_CENS <- "C ~ highA1c + lastNat1"
gform_TRT = "TI ~ CVD + highA1c + N.tminus1"
gform_MONITOR <- "N ~ 1"
stratify_CENS <- list(C=c("t < 16", "t == 16"))

# ----------------------------------------------------------------------
# Fit Propensity Scores
# ----------------------------------------------------------------------
OData <- fitPropensity(OData, gform_CENS = gform_CENS,
                        gform_TRT = gform_TRT,
                        gform_MONITOR = gform_MONITOR,
                        stratify_CENS = stratify_CENS)

# ----------------------------------------------------------------------
# IPW Ajusted KM or Saturated MSM
# ----------------------------------------------------------------------
require("magrittr")
AKME.St.1 <- getIPWeights(OData, intervened_TRT = "TI.set1") %>%
             survNPMSM(OData) %$%
             IPW_estimates
AKME.St.1

# ----------------------------------------------------------------------
# Bounded IPW
# ----------------------------------------------------------------------
IPW.St.1 <- getIPWeights(OData, intervened_TRT = "TI.set1") %>%
             survDirectIPW(OData)
IPW.St.1[]

# ----------------------------------------------------------------------
# IPW-MSM for hazard
# ----------------------------------------------------------------------
wts.DT.1 <- getIPWeights(OData = OData, intervened_TRT = "TI.set1", rule_name = "TI1")
wts.DT.0 <- getIPWeights(OData = OData, intervened_TRT = "TI.set0", rule_name = "TI0")
survMSM_res <- survMSM(list(wts.DT.1, wts.DT.0), OData, t_breaks = c(1:8,12,16)-1,)
survMSM_res$St

# ----------------------------------------------------------------------
# Sequential G-COMP
# ----------------------------------------------------------------------
t.surv <- c(0:15)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
params = list(fit.package = "speedglm", fit.algorithm = "glm")

## Not run: 
gcomp_est <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "TI.set1",
                          Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
gcomp_est[]

## End(Not run)
# ----------------------------------------------------------------------
# TMLE
# ----------------------------------------------------------------------
## Not run: 
tmle_est <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "TI.set1",
                    Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE)
tmle_est[]

## End(Not run)

# ----------------------------------------------------------------------
# Running IPW-Adjusted KM with optional user-specified weights:
# ----------------------------------------------------------------------
addedWts_DT <- OdataDT[, c("ID", "t"), with = FALSE]
addedWts_DT[, new.wts := sample.int(10, nrow(OdataDT), replace = TRUE)/10]
survNP_res_addedWts <- survNPMSM(wts.DT.1, OData, weights = addedWts_DT)

# ----------------------------------------------------------------------
# Multivariate Propensity Score Regressions
# ----------------------------------------------------------------------
gform_CENS <- "C + TI + N ~ highA1c + lastNat1"
OData <- fitPropensity(OData, gform_CENS = gform_CENS, gform_TRT = gform_TRT,
                        gform_MONITOR = gform_MONITOR)

# ----------------------------------------------------------------------
# Fitting Propensity scores with Random Forests:
# ----------------------------------------------------------------------
## Not run: 
set_all_stremr_options(fit.package = "h2o", fit.algorithm = "randomForest")
require("h2o")
h2o::h2o.init(nthreads = -1)
gform_CENS <- "C ~ highA1c + lastNat1"
OData <- fitPropensity(OData, gform_CENS = gform_CENS,
                        gform_TRT = gform_TRT,
                        gform_MONITOR = gform_MONITOR,
                        stratify_CENS = stratify_CENS)

# For Gradient Boosting machines:
set_all_stremr_options(fit.package = "h2o", fit.algorithm = "gbm")
# Use `H2O-3` distributed implementation of GLM
set_all_stremr_options(fit.package = "h2o", fit.algorithm = "glm")
# Use Deep Neural Nets:
set_all_stremr_options(fit.package = "h2o", fit.algorithm = "deeplearning")

## End(Not run)

# ----------------------------------------------------------------------
# Fitting different models with different algorithms
# Fine tuning modeling with optional tuning parameters.
# ----------------------------------------------------------------------
## Not run: 
params_TRT = list(fit.package = "h2o", fit.algorithm = "gbm", ntrees = 50,
    learn_rate = 0.05, sample_rate = 0.8, col_sample_rate = 0.8,
    balance_classes = TRUE)
params_CENS = list(fit.package = "speedglm", fit.algorithm = "glm")
params_MONITOR = list(fit.package = "speedglm", fit.algorithm = "glm")
OData <- fitPropensity(OData,
            gform_CENS = gform_CENS, stratify_CENS = stratify_CENS, params_CENS = params_CENS,
            gform_TRT = gform_TRT, params_TRT = params_TRT,
            gform_MONITOR = gform_MONITOR, params_MONITOR = params_MONITOR)

## End(Not run)

# ----------------------------------------------------------------------
# Running TMLE based on the previous fit of the propensity scores.
# Also applying Random Forest to estimate the sequential outcome model
# ----------------------------------------------------------------------
## Not run: 
t.surv <- c(0:5)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
params_Q = list(fit.package = "h2o", fit.algorithm = "randomForest",
                ntrees = 100, learn_rate = 0.05, sample_rate = 0.8,
                col_sample_rate = 0.8, balance_classes = TRUE)
tmle_est <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "TI.set1",
            Qforms = Qforms, params_Q = params_Q,
            stratifyQ_by_rule = TRUE)

## End(Not run)

## Not run: 
t.surv <- c(0:5)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
params_Q = list(fit.package = "h2o", fit.algorithm = "randomForest",
                ntrees = 100, learn_rate = 0.05, sample_rate = 0.8,
                col_sample_rate = 0.8, balance_classes = TRUE)
tmle_est <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "TI.set1",
            Qforms = Qforms, params_Q = params_Q,
            stratifyQ_by_rule = FALSE)

## End(Not run)

stremr documentation built on May 30, 2017, 6:35 a.m.