importData: Import data, define various nodes, define dummies for factor...
In stremr: Streamlined Estimation of Survival for Static, Dynamic and Stochastic Treatment and Monitoring Regimes
Description
Usage
Arguments
Value
Examples
View source: R/main_estimation.R
Description
Import data, define various nodes, define dummies for factor columns and define OData R6 object
Usage
1
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importData(data, ID = "Subject_ID", t_name = "time_period", covars,
CENS = "C", TRT = "A", MONITOR = "N", OUTCOME = "Y", noCENScat = 0L,
verbose = getOption("stremr.verbose"))
Arguments
data
Input data in long format. Can be a data.frame or a data.table with named columns, containing the time-varying covariates (covars),
the right-censoring event indicator(s) (CENS), the exposure variable(s) (TRT), the monitoring process variable(s) (MONITOR)
and the survival OUTCOME variable (OUTCOME).
ID
Unique subject identifier column name in data.
t_name
The name of the time/period variable in data.
covars
Vector of names with time varying and baseline covariates in data. This argument does not need to be specified, by default all variables
that are not in ID, t, CENS, TRT, MONITOR and OUTCOME will be considered as covariates.
CENS
Column name of the censoring variable(s) in data.
Each separate variable specified in CENS can be either binary (0/1 valued integer) or categorical (integer).
For binary indicators of CENSoring, the value of 1 indicates the CENSoring or end of follow-up event (this cannot be changed).
For categorical CENSoring variables, by default the value of 0 indicates no CENSoring / continuation of follow-up and other
values indicate different reasons for CENSoring.
Use the argument noCENScat to change the reference (continuation of follow-up) category from default 0 to any other value.
(NOTE: Changing noCENScat has zero effect on coding of the binary CENSoring variables, those have to always use 1 to code the CENSoring event).
Note that factors are not allowed in CENS.
TRT
A column name in data for the exposure/treatment variable(s).
MONITOR
A column name in data for the indicator(s) of monitoring events.
OUTCOME
A column name in data for the survival OUTCOME variable name, code as 1 for the outcome event.
noCENScat
The level (integer) that indicates CONTINUATION OF FOLLOW-UP for ALL censoring variables. Defaults is 0.
Use this to modify the default reference category (no CENSoring / continuation of follow-up)
for variables specifed in CENS.
verbose
Set to TRUE to print messages on status and information to the console. Turn this on by default using options(stremr.verbose=TRUE).
Value
...
Examples
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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.
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Description Usage Arguments Value Examples
View source: R/main_estimation.R
Description
Import data, define various nodes, define dummies for factor columns and define OData R6 object
Usage
1 2 3 | importData(data, ID = "Subject_ID", t_name = "time_period", covars,
CENS = "C", TRT = "A", MONITOR = "N", OUTCOME = "Y", noCENScat = 0L,
verbose = getOption("stremr.verbose"))
|
Arguments
data |
Input data in long format. Can be a |
ID |
Unique subject identifier column name in |
t_name |
The name of the time/period variable in |
covars |
Vector of names with time varying and baseline covariates in |
CENS |
Column name of the censoring variable(s) in |
TRT |
A column name in |
MONITOR |
A column name in |
OUTCOME |
A column name in |
noCENScat |
The level (integer) that indicates CONTINUATION OF FOLLOW-UP for ALL censoring variables. Defaults is 0.
Use this to modify the default reference category (no CENSoring / continuation of follow-up)
for variables specifed in |
verbose |
Set to |
Value
...
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | 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)
|
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