Description Usage Arguments Value See Also Examples
Interventions on up to 3 nodes are allowed: CENS
, TRT
and MONITOR
.
TMLE adjustment will be based on the inverse of the propensity score fits for the observed likelihood (g0.C, g0.A, g0.N),
multiplied by the indicator of not being censored and the probability of each intervention in intervened_TRT
and intervened_MONITOR
.
Requires column name(s) that specify the counterfactual node values or the counterfactual probabilities of each node being 1 (for stochastic interventions).
1 2 3 4 5 6 7 8 9 | fitSeqGcomp(OData, t_periods, Qforms, Qstratify = NULL,
intervened_TRT = NULL, intervened_MONITOR = NULL, useonly_t_TRT = NULL,
useonly_t_MONITOR = NULL, rule_name = paste0(c(intervened_TRT,
intervened_MONITOR), collapse = ""), stratifyQ_by_rule = FALSE,
TMLE = FALSE, iterTMLE = FALSE, IPWeights = NULL, stabilize = FALSE,
trunc_weights = 10^6, params_Q = list(), weights = NULL,
max_iter = 15, adapt_stop = TRUE, adapt_stop_factor = 10,
tol_eps = 0.001, parallel = FALSE,
verbose = getOption("stremr.verbose"))
|
OData |
Input data object created by |
t_periods |
Specify the vector of time-points for which the survival function (and risk) should be estimated |
Qforms |
Regression formulas, one formula per Q. Only main-terms are allowed. |
Qstratify |
Placeholder for future user-defined model stratification for all Qs (CURRENTLY NOT FUNCTIONAL, WILL RESULT IN ERROR) |
intervened_TRT |
Column name in the input data with the probabilities (or indicators) of counterfactual treatment nodes being equal to 1 at each time point.
Leave the argument unspecified ( |
intervened_MONITOR |
Column name in the input data with probabilities (or indicators) of counterfactual monitoring nodes being equal to 1 at each time point.
Leave the argument unspecified ( |
useonly_t_TRT |
Use for intervening only on some subset of observation and time-specific treatment nodes.
Should be a character string with a logical expression that defines the subset of intervention observations.
For example, using |
useonly_t_MONITOR |
Same as |
rule_name |
Optional name for the treatment/monitoring regimen. |
stratifyQ_by_rule |
Set to |
TMLE |
Set to |
iterTMLE |
Set to |
IPWeights |
(Optional) result of calling function |
stabilize |
Set to |
trunc_weights |
Specify the numeric weight truncation value. All final weights exceeding the value in |
params_Q |
Optional parameters to be passed to the specific fitting algorithm for Q-learning |
weights |
Optional |
max_iter |
For iterative TMLE only: Integer, set to maximum number of iterations for iterative TMLE algorithm. |
adapt_stop |
For iterative TMLE only: Choose between two stopping criteria for iterative TMLE, default is |
adapt_stop_factor |
For iterative TMLE only: The adaptive factor to choose the stopping criteria for iterative TMLE when |
tol_eps |
For iterative TMLE only: Numeric error tolerance for the iterative TMLE update.
The iterative TMLE algorithm will stop when the absolute value of the TMLE intercept update is below |
parallel |
Set to |
verbose |
... |
...
stremr-package
for the general overview of the package,
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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