Nothing
## --------------------------------------------------------------------------------------------------------
## Install data.table (most recent version)
## --------------------------------------------------------------------------------------------------------
# devtools::install_github('Rdatatable/data.table')
## --------------------------------------------------------------------------------------------------------
## For installing most recent vs of h2o see: https://s3.amazonaws.com/h2o-release/h2o/master/latest.html
## --------------------------------------------------------------------------------------------------------
# if ("package:h2o" %in% search()) detach("package:h2o", unload=TRUE)
# if ("h2o" %in% rownames(installed.packages())) remove.packages("h2o")
## Next, download H2O package dependencies:
# pkgs <- c("methods","statmod","stats","graphics","RCurl","jsonlite","tools","utils")
# new.pkgs <- setdiff(pkgs, rownames(installed.packages()))
# if (length(new.pkgs)) install.packages(new.pkgs)
## Download and install the H2O package for R:
# install.packages("h2o", type="source", repos=(c("https://s3.amazonaws.com/h2o-release/h2o/master/3687/R")))
## --------------------------------------------------------------------------------------------------------
## Install stremr
## --------------------------------------------------------------------------------------------------------
# devtools::install_github('osofr/stremr', build_vignettes = FALSE)
# ---------------------------------------------------------------------------
# Test speedglm
# ---------------------------------------------------------------------------
test.speedglm.allestimators10Kdata <- function() {
options(stremr.verbose = FALSE)
# options(stremr.verbose = TRUE)
options(width = 100)
`%+%` <- function(a, b) paste0(a, b)
require("data.table")
# obsDTg05_500K <- data.table::fread(input = "./obsDTg05_500K.csv", header = TRUE, na.strings = "NA_h2o")
data(OdatDT_10K)
Odat_DT <- OdatDT_10K
setkeyv(Odat_DT, cols = c("ID", "t"))
head(Odat_DT)
nrow(Odat_DT)
# ---------------------------------------------------------------------------
# Define some summaries (lags C[t-1], A[t-1], N[t-1])
# ---------------------------------------------------------------------------
ID <- "ID"; t <- "t"; TRT <- "TI"; I <- "highA1c"; outcome <- "Y.tplus1";
lagnodes <- c("C", "TI", "N")
newVarnames <- lagnodes %+% ".tminus1"
Odat_DT[, (newVarnames) := shift(.SD, n=1L, fill=0L, type="lag"), by=ID, .SDcols=(lagnodes)]
# indicator that the person has never been on treatment up to current t
Odat_DT[, ("barTIm1eq0") := as.integer(c(0, cumsum(get(TRT))[-.N]) %in% 0), by = eval(ID)]
Odat_DT[, ("lastNat1.factor") := as.factor(lastNat1)]
# ----------------------------------------------------------------
# IMPORT DATA
# ----------------------------------------------------------------
# options(stremr.verbose = TRUE)
# set_all_stremr_options(fit.package = "glm", fit.algorithm = "glm")
set_all_stremr_options(fit.package = "speedglm", fit.algorithm = "glm")
OData <- importData(Odat_DT, ID = "ID", t = "t", covars = c("highA1c", "lastNat1", "lastNat1.factor"), CENS = "C", TRT = "TI", MONITOR = "N", OUTCOME = outcome)
# To inspect the input data.table:
# OData$dat.sVar
# ------------------------------------------------------------------
# Fit propensity scores for Treatment, Censoring & Monitoring
# ------------------------------------------------------------------
gform_TRT <- "TI ~ CVD + highA1c + N.tminus1"
stratify_TRT <- list(
TI=c("t == 0L", # MODEL TI AT t=0
"(t > 0L) & (N.tminus1 == 1L) & (barTIm1eq0 == 1L)", # MODEL TRT INITATION WHEN MONITORED
"(t > 0L) & (N.tminus1 == 0L) & (barTIm1eq0 == 1L)", # MODEL TRT INITATION WHEN NOT MONITORED
"(t > 0L) & (barTIm1eq0 == 0L)" # MODEL TRT CONTINUATION (BOTH MONITORED AND NOT MONITORED)
))
gform_CENS <- c("C ~ highA1c + t")
# stratify_CENS <- list(C=c("t < 16", "t == 16"))
# stratify_CENS <- list()
gform_MONITOR <- "N ~ 1"
# **** really want to define it like this ****
# gform_TRT = c(list("TI[t] ~ CVD[t] + highA1c[t] + N[t-1]", t==0),
# list("TI[t] ~ CVD[t] + highA1c[t] + N[t-1]", t>0))
OData <- fitPropensity(OData, gform_CENS = gform_CENS, gform_TRT = gform_TRT,
stratify_TRT = stratify_TRT, gform_MONITOR = gform_MONITOR)
wts.St.dlow <- getIPWeights(OData, intervened_TRT = "gTI.dlow")
surv1 <- survNPMSM(wts.St.dlow, OData)
wts.St.dhigh <- getIPWeights(OData, intervened_TRT = "gTI.dhigh")
surv2 <- survNPMSM(wts.St.dhigh, OData)
# ------------------------------------------------------------------
# Piping the workflow
# ------------------------------------------------------------------
require("magrittr")
St.dlow <- getIPWeights(OData, intervened_TRT = "gTI.dlow", intervened_MONITOR = "gPois3.yrly") %>%
survNPMSM(OData) %$%
IPW_estimates
St.dlow
St.dhigh <- getIPWeights(OData, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "gPois3.yrly") %>%
survNPMSM(OData) %$%
IPW_estimates
St.dhigh
# ------------------------------------------------------------------
# Running IPW-adjusted MSM for the hazard
# ------------------------------------------------------------------
MSM.IPAW <- survMSM(OData,
wts_data = list(dlow = wts.St.dlow, dhigh = wts.St.dhigh),
t_breaks = c(1:8,12,16)-1,
est_name = "IPAW", getSEs = TRUE)
# names(MSM.IPAW)
# nrow(t(MSM.IPAW$IC.Var.S.d$gTI.dlow$IC.S))
# nrow(t(MSM.IPAW$IC.Var.S.d$gTI.dhigh$IC.S))
# ncol(t(MSM.IPAW$IC.Var.S.d$gTI.dlow$IC.S))
# ncol(t(MSM.IPAW$IC.Var.S.d$gTI.dhigh$IC.S))
# # MSM.IPAW$St
# names(MSM.IPAW$wts_data)
# OData$nuniqueIDs
# ---------------------------------------------------------------------------------------------------------
# TMLE / GCOMP
# ---------------------------------------------------------------------------------------------------------
t.surv <- c(4,5)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
params = list(fit.package = "speedglm", fit.algorithm = "glm")
gcomp_est3 <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
gcomp_est3$estimates[]
# stratified modeling by rule followers only:
tmle_est3 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE)
tmle_est3$estimates[]
# pooling all observations (no stratification):
tmle_est4 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
tmle_est4$estimates[]
# ------------------------------------------------------------------------
# RUN in PARALLEL seq-GCOMP & TMLE over t.surv (MUCH FASTER)
# ------------------------------------------------------------------------
# require("doParallel")
# registerDoParallel(cores = 2)
t.surv <- c(0,1,4)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
gcomp_est1 <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", rule_name = "pooledGCOMP.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
gcomp_est1$estimates[]
gcomp_est2 <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dlow", rule_name = "pooledGCOMP.dlow", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
gcomp_est2$estimates[]
# tmle_est_par1 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", rule_name = "pool.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE, parallel = TRUE)
tmle_est_par1 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", rule_name = "pooledTMLE.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE, parallel = FALSE)
tmle_est_par1$estimates[]
# tmle_est_par2 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dlow", rule_name = "pool.dlow", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE, parallel = TRUE)
tmle_est_par2 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dlow", rule_name = "pooledTMLE.dlow", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE, parallel = FALSE)
tmle_est_par2$estimates[]
# tmle_est_par3 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", rule_name = "strat.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE, parallel = TRUE)
tmle_est_par3 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", rule_name = "stratTMLE.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE, parallel = FALSE)
tmle_est_par3$estimates[]
# tmle_est_par4 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dlow", rule_name = "strat.dlow", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE, parallel = TRUE)
tmle_est_par4 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dlow", rule_name = "stratTMLE.dlow", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE, parallel = FALSE)
tmle_est_par4$estimates[]
# ------------------------------------------------------------------
# Make a report:
# ------------------------------------------------------------------
# report.path <- "path/to/report/dir"
# file.path = report.path,
# test for opening file in local OS
if (rmarkdown::pandoc_available(version = "1.12.3"))
make_report_rmd(OData, file.name = "sim.data.example.fup2", title = "Custom", author = "Insert Author Name", openFile = FALSE)
if (rmarkdown::pandoc_available(version = "1.12.3"))
make_report_rmd(OData, NPMSM = list(surv1, surv2), MSM = MSM.IPAW, GCOMP = list(gcomp_est1, gcomp_est2), TMLE = list(tmle_est_par1, tmle_est_par2),
format = "html",
FUPtables = get_FUPtimes(MSM.IPAW$wts_data, IDnode = "ID", tnode = "t"),
# openFile = TRUE,
openFile = FALSE,
MSM.RDtables = get_MSM_RDs(MSM.IPAW, t.periods.RDs = c(12, 15), getSEs = TRUE),
TMLE.RDtables = get_TMLE_RDs(list(tmle_est_par1, tmle_est_par2), t.periods.RDs = c(1, 4)),
WTtables = get_wtsummary(MSM.IPAW$wts_data, cutoffs = c(0, 0.5, 1, 10, 20, 30, 40, 50, 100, 150), by.rule = TRUE),
file.name = "sim.data.example.fup", title = "Custom Report Title", author = "Insert Author Name", x_legend = 9.5, y_legend = 0.99,
save_report_data = TRUE
)
load(file = file.path(getOption('stremr.file.path'), "sim.data.example.fup") %+% ".Rd")
names(report_results_list)
if (rmarkdown::pandoc_available(version = "1.12.3"))
make_report_rmd(OData, NPMSM = report_results_list$NPMSM, MSM = report_results_list$MSM, GCOMP = report_results_list$GCOMP, TMLE = report_results_list$TMLE,
format = "html",
FUPtables = report_results_list$FUPtables,
# openFile = TRUE,
openFile = FALSE,
MSM.RDtables = report_results_list$MSM.RDtables,
TMLE.RDtables = report_results_list$TMLE.RDtables,
WTtables = report_results_list$WTtables,
file.name = "sim.data.example.fup",
title = "Custom Report Title", author = "Insert Author Name", x_legend = 9.5, y_legend = 0.99
)
if (rmarkdown::pandoc_available(version = "1.12.3"))
make_report_rmd(OData, NPMSM = list(surv1, surv2), MSM = MSM.IPAW, GCOMP = list(gcomp_est1, gcomp_est2), TMLE = list(tmle_est_par1, tmle_est_par2),
format = "html",
AddFUPtables = TRUE,
# openFile = TRUE,
openFile = FALSE,
MSM.RDtables = get_MSM_RDs(MSM.IPAW, t.periods.RDs = c(12, 15), getSEs = TRUE),
TMLE.RDtables = get_TMLE_RDs(list(tmle_est_par1, tmle_est_par2), t.periods.RDs = c(1, 4)),
WTtables = get_wtsummary(MSM.IPAW$wts_data, cutoffs = c(0, 0.5, 1, 10, 20, 30, 40, 50, 100, 150), by.rule = TRUE),
file.name = "sim.data.example.fup", title = "Custom Report Title", author = "Insert Author Name")
if (rmarkdown::pandoc_available(version = "1.12.3"))
make_report_rmd(OData, NPMSM = list(surv1, surv2), MSM = MSM.IPAW, GCOMP = list(gcomp_est1, gcomp_est2), TMLE = list(tmle_est_par1, tmle_est_par2),
format = "html",
AddFUPtables = TRUE,
# openFile = TRUE,
openFile = FALSE,
MSM.RDtables = get_MSM_RDs(MSM.IPAW, t.periods.RDs = c(12, 15), getSEs = TRUE),
TMLE.RDtables = get_TMLE_RDs(list(tmle_est_par1, tmle_est_par2), t.periods.RDs = c(1, 4)),
WTtables = get_wtsummary(MSM.IPAW$wts_data, cutoffs = c(0, 0.5, 1, 10, 20, 30, 40, 50, 100, 150), by.rule = TRUE),
file.name = "sim.data.example.fup", title = "Custom Report Title", author = "Insert Author Name", x_legend = "topright")
if (rmarkdown::pandoc_available(version = "1.12.3"))
make_report_rmd(OData, NPMSM = list(surv1, surv2), plotKM = TRUE, MSM = MSM.IPAW, GCOMP = list(gcomp_est1, gcomp_est2), TMLE = list(tmle_est_par1, tmle_est_par2),
format = "html",
AddFUPtables = TRUE,
openFile = FALSE,
MSM.RDtables = get_MSM_RDs(MSM.IPAW, t.periods.RDs = c(12, 15), getSEs = TRUE),
TMLE.RDtables = get_TMLE_RDs(list(tmle_est_par1, tmle_est_par2), t.periods.RDs = c(1, 4)),
WTtables = get_wtsummary(MSM.IPAW$wts_data, cutoffs = c(0, 0.5, 1, 10, 20, 30, 40, 50, 100, 150), by.rule = TRUE),
file.name = "sim.data.example.fup", title = "Custom Report Title", author = "Insert Author Name", x_legend = 9.5, y_legend = 0.99)
if (rmarkdown::pandoc_available(version = "1.12.3"))
make_report_rmd(OData, NPMSM = list(surv1, surv2), MSM = MSM.IPAW, TMLE = list(tmle_est_par1, tmle_est_par2),
format = "pdf",
AddFUPtables = TRUE,
openFile = FALSE,
MSM.RDtables = get_MSM_RDs(MSM.IPAW, t.periods.RDs = c(12, 15), getSEs = TRUE),
TMLE.RDtables = get_TMLE_RDs(list(tmle_est_par1, tmle_est_par2), t.periods.RDs = c(1, 4)),
WTtables = get_wtsummary(MSM.IPAW$wts_data, cutoffs = c(0, 0.5, 1, 10, 20, 30, 40, 50, 100, 150), by.rule = TRUE),
file.name = "sim.data.example.fup", title = "Custom Report Title", author = "Insert Author Name", x_legend = 9.5, y_legend = 0.99)
# ---------------------------------------------------------------------------------------------------------
# TMLE / GCOMP with a stochastic intervention on MONITOR
# *** make a separate function
# **** +1 add evaluation of IPW & TMLE under static NDE
# **** +2 add evaluation of IPW & TMLE under stochastic NDE intervention on monitoring
# **** +3 add evaluation of IPW & TMLE under stochastic intervention on treatment
# ---------------------------------------------------------------------------------------------------------
t.surv <- c(4)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
params = list(fit.package = "speedglm", fit.algorithm = "glm")
gcomp_est3 <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "gPois3.yrly", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
# stratified modeling by rule followers only:
tmle_est3 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "gPois3.yrly", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE)
tmle_est3$estimates[]
# pooling all observations (no stratification):
tmle_est4 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "gPois3.yrly", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
tmle_est4$estimates[]
}
# ---------------------------------------------------------------------------
# Test speedglm
# ---------------------------------------------------------------------------
test.speedglm.stochastic.TMLE.NDE.1Kdata <- function() {
options(stremr.verbose = FALSE)
options(width = 100)
`%+%` <- function(a, b) paste0(a, b)
require("data.table")
data(OdatDT_10K)
Odat_DT <- OdatDT_10K
Odat_DT <- Odat_DT[ID %in% (1:5000), ]
# define intervention on N as 0101010101...
Odat_DT[, ("N.star.0101") := t%%2]
setkeyv(Odat_DT, cols = c("ID", "t"))
# ---------------------------------------------------------------------------
# Define some summaries (lags C[t-1], A[t-1], N[t-1])
# ---------------------------------------------------------------------------
ID <- "ID"; t <- "t"; TRT <- "TI"; I <- "highA1c"; outcome <- "Y.tplus1";
lagnodes <- c("C", "TI", "N")
newVarnames <- lagnodes %+% ".tminus1"
Odat_DT[, (newVarnames) := shift(.SD, n=1L, fill=0L, type="lag"), by=ID, .SDcols=(lagnodes)]
# indicator that the person has never been on treatment up to current t
Odat_DT[, ("barTIm1eq0") := as.integer(c(0, cumsum(get(TRT))[-.N]) %in% 0), by = eval(ID)]
Odat_DT[, ("lastNat1.factor") := as.factor(lastNat1)]
# ----------------------------------------------------------------
# IMPORT DATA
# ----------------------------------------------------------------
set_all_stremr_options(fit.package = "speedglm", fit.algorithm = "glm")
OData <- importData(Odat_DT, ID = "ID", t = "t", covars = c("highA1c", "lastNat1", "lastNat1.factor"), CENS = "C", TRT = "TI", MONITOR = "N", OUTCOME = outcome)
# ------------------------------------------------------------------
# Fit propensity scores for Treatment, Censoring & Monitoring
# ------------------------------------------------------------------
gform_TRT <- "TI ~ CVD + highA1c + N.tminus1"
stratify_TRT <- list(
TI=c("t == 0L", # MODEL TI AT t=0
"(t > 0L) & (N.tminus1 == 1L) & (barTIm1eq0 == 1L)", # MODEL TRT INITATION WHEN MONITORED
"(t > 0L) & (N.tminus1 == 0L) & (barTIm1eq0 == 1L)", # MODEL TRT INITATION WHEN NOT MONITORED
"(t > 0L) & (barTIm1eq0 == 0L)" # MODEL TRT CONTINUATION (BOTH MONITORED AND NOT MONITORED)
))
gform_CENS <- c("C ~ highA1c + t")
gform_MONITOR <- "N ~ 1"
OData <- fitPropensity(OData, gform_CENS = gform_CENS, gform_TRT = gform_TRT,
stratify_TRT = stratify_TRT, gform_MONITOR = gform_MONITOR)
# ---------------------------------------------------------------------------------------------------------
# IPW-KM with stochastic intervention on MONITOR
# ---------------------------------------------------------------------------------------------------------
wts.St.dlow <- getIPWeights(OData, intervened_TRT = "gTI.dlow", intervened_MONITOR = "gPois3.yrly")
surv1.stoch <- survNPMSM(wts.St.dlow, OData)
wts.St.dhigh <- getIPWeights(OData, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "gPois3.yrly")
surv2.stoch <- survNPMSM(wts.St.dhigh, OData)
# ---------------------------------------------------------------------------------------------------------
# IPW-KM with static intervention on MONITOR
# ---------------------------------------------------------------------------------------------------------
wts.St.dlow <- getIPWeights(OData, intervened_TRT = "gTI.dlow", intervened_MONITOR = "N.star.0101")
surv1.stat <- survNPMSM(wts.St.dlow, OData)
wts.St.dhigh <- getIPWeights(OData, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "N.star.0101")
surv2.stat <- survNPMSM(wts.St.dhigh, OData)
# ---------------------------------------------------------------------------------------------------------
# IPW-KM with static intervention on MONITOR under NDE assumption
# ---------------------------------------------------------------------------------------------------------
wts.St.dlow <- getIPWeights(OData, intervened_TRT = "gTI.dlow", intervened_MONITOR = "N.star.0101", useonly_t_MONITOR = "N.star.0101 == 1")
surv1.statNDE <- survNPMSM(wts.St.dlow, OData)
wts.St.dhigh <- getIPWeights(OData, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "N.star.0101", useonly_t_MONITOR = "N.star.0101 == 1")
surv2.statNDE <- survNPMSM(wts.St.dhigh, OData)
# ---------------------------------------------------------------------------------------------------------
# TMLE / GCOMP with a stochastic intervention on MONITOR
# ---------------------------------------------------------------------------------------------------------
t.surv <- c(4,5)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
params = list(fit.package = "speedglm", fit.algorithm = "glm")
gcomp_est3 <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "gPois3.yrly", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
# stratified modeling by rule followers only:
tmle_est3 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "gPois3.yrly", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE)
tmle_est3$estimates[]
# pooling all observations (no stratification):
tmle_est4 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "gPois3.yrly", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
tmle_est4$estimates[]
# ---------------------------------------------------------------------------------------------------------
# TMLE / GCOMP with a static intervention on MONITOR
# ---------------------------------------------------------------------------------------------------------
t.surv <- c(4,5)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
params = list(fit.package = "speedglm", fit.algorithm = "glm")
gcomp_est3 <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "N.star.0101", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
gcomp_est3$estimates[]
# stratified modeling by rule followers only:
tmle_est3 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "N.star.0101", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE)
tmle_est3$estimates[]
# pooling all observations (no stratification):
tmle_est4 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "N.star.0101", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
tmle_est4$estimates[]
# ---------------------------------------------------------------------------------------------------------
# TMLE / GCOMP with a static intervention on MONITOR under NDE assumption
# ---------------------------------------------------------------------------------------------------------
t.surv <- c(4,5)
Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
params = list(fit.package = "speedglm", fit.algorithm = "glm")
gcomp_est3 <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "N.star.0101",
useonly_t_MONITOR = "N.star.0101 == 1", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
gcomp_est3$estimates[]
# stratified modeling by rule followers only:
tmle_est3 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "N.star.0101",
useonly_t_MONITOR = "N.star.0101 == 1", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = TRUE)
tmle_est3$estimates[]
# pooling all observations (no stratification):
tmle_est4 <- fitTMLE(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", intervened_MONITOR = "N.star.0101",
useonly_t_MONITOR = "N.star.0101 == 1", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
tmle_est4$estimates[]
}
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