Estimate causal mediation mechanism of a treatment using propensity score weighting.
wgtmed(
formula.med,
data,
a_treatment,
y_outcome = NULL,
med_interact = NULL,
total_effect_wts = NULL,
total_effect_ps = NULL,
total_effect_stop_rule = NULL,
method = "ps",
sampw = NULL,
ps_n.trees = 10000,
ps_interaction.depth = 3,
ps_shrinkage = 0.01,
ps_bag.fraction = 1,
ps_n.minobsinnode = 10,
ps_perm.test.iters = 0,
ps_verbose = FALSE,
ps_stop.method = c("ks.mean", "ks.max"),
ps_version = "gbm",
ps_ks.exact = NULL,
ps_n.keep = 1,
ps_n.grid = 25,
ps_cv.folds = 10,
ps_keep.data = FALSE
)mediation object
The mediation object includes the following:
model_a The model A ps() results.
model_m1 The model M1 ps() results.
model_m0 The model M0 ps() results.
data The data set used to compute models
stopping_methods The stopping methods passed to stop.method.
datestamp The date when the analysis was run.
For each stop.method, a list with the following:
TE The total effect.
NDE_0 The natural direct effect, holding the mediator constant at 0.
NIE_1 The natural indirect effect, holding the exposure constant at 1.
NDE_1 The natural direct effect, holding the mediator constant at 1.
NIE_0 The natural indirect effect, holding the exposure constant at 0.
expected_treatment0_mediator0 E(Y(0, M(0)))
expected_treatment1_mediator1 E(Y(1, M(1)))
expected_treatment1_mediator0 E(Y(1, M(0)))
expected_treatment0_mediator1 E(Y(0, M(1)))
dx.wts A list with information for checking covariate balance of for each
estimated effect. Elements are TE, NIE1, NDE0, NIE0, NDE1, with results of
twang dx.wts for the covariates when weighted by weights used in the
estimating the effect.
A object of class formula relating the mediatior(s) to the covariates (potential confounding variables).
A dataset of class data.frame that includes the treatment indicator, mediator(s), and covariates.
The (character) name of the treatment variable, which must be dichotomous (0, 1).
The (character) name of the outcome variable, y. If this is not provided, then
no effects will be calculated and a warning will be raised. Default : NULL.
The (character) vector of names of variables specified on the right-hand side of formula.med that consist of crossproducts or interactions between a covariate and the mediator. See the tutorial for details on these variables.
A vector of total effect weights, which if left NULL
then total_effect_ps must be supplied. Default : NULL.
A ps object that contains the total effect weights,
The stopping rule (ks.mean, ks.max, es.mean, es.max) for the total effect weights, which
only needs to be specified if total_effect_ps is provided. Default : NULL.
The method for getting weights ("ps", "logistic", or "crossval"). Default : "ps".
Optional sampling weights Default : NULL.
Number of gbm iterations passed on to gbm. Default: 10000.
A positive integer denoting the tree depth used in gradient boosting. Default: 3.
A numeric value between 0 and 1 denoting the learning rate. See gbm for more details. Default: 0.01.
A numeric value between 0 and 1 denoting the fraction of the observations randomly selected in each iteration of the gradient boosting algorithm to propose the next tree. See gbm for more details. Default: 1.0.
An integer specifying the minimum number of observations in the terminal nodes of the trees used in the gradient boosting. See gbm for more details. Default: 10.
A non-negative integer giving the number of iterations
of the permutation test for the KS statistic. If perm.test.iters=0
then the function returns an analytic approximation to the p-value. Setting
perm.test.iters=200 will yield precision to within 3% if the true
p-value is 0.05. Use perm.test.iters=500 to be within 2%. Default: 0.
If TRUE, lots of information will be printed to monitor the
the progress of the fitting. Default: FALSE.
A method or methods of measuring and summarizing balance across pretreatment
variables. Current options are ks.mean, ks.max, es.mean, and es.max. ks refers to the
Kolmogorov-Smirnov statistic and es refers to standardized effect size. These are summarized
across the pretreatment variables by either the maximum (.max) or the mean (.mean).
Default: c("ks.mean", "ks.max").
"gbm", "xgboost", or "legacy", indicating which version of the twang package to use.
"gbm" uses gradient boosting from the gbm package.
"xgboost" uses gradient boosting from the xgboost package.
"legacy" uses the prior implementation of the ps function.
NULL or a logical indicating whether the
Kolmogorov-Smirnov p-value should be based on an approximation of exact
distribution from an unweighted two-sample Kolmogorov-Smirnov test. If
NULL, the approximation based on the exact distribution is computed
if the product of the effective sample sizes is less than 10,000.
Otherwise, an approximation based on the asymptotic distribution is used.
Warning: setting ks.exact = TRUE will add substantial
computation time for larger sample sizes. Default: NULL.
A numeric variable indicating the algorithm should only
consider every n.keep-th iteration of the propensity score model and
optimize balance over this set instead of all iterations. Default : 1.
A numeric variable that sets the grid size for an initial
search of the region most likely to minimize the stop.method. A
value of n.grid=50 uses a 50 point grid from 1:n.trees. It
finds the minimum, say at grid point 35. It then looks for the actual
minimum between grid points 34 and 36.If specified with n.keep>1, n.grid
corresponds to a grid of points on the kept iterations as defined by n.keep. Default: 25.
A numeric variable that sets the number of cross-validation folds if using method='crossval'. Default: 10.
A logical variable that determines if the dataset should be saved
in the resulting ps model objects. Default: FALSE.
For users comfortable with ps, any options prefaced with
ps_ are passed directly to the ps() function.
Model A is used to estimate Pr(A=1 | X) where X is the vector of background covariates specified in formula.med. If method equals "ps" model A is fit using the twang ps function with estimand= "ATE". If method equals "logistic" then model A is fit using logistic regression. If method equals "crossval" then gbm using cross-validation is used to estimate model A. Because X might include variables not used to estimate the user-provided total effect weights, model A is fit rather than using the user-provided total effect weights to derive Pr(A | X). If the user uses the same set of variables to estimate their provided total effect weights as they enter in the wgtmed function to estimate the cross-world weights and the user uses the same estimation method and arguments as specified in the wgtmed function, then the estimated model A will match the model the user used to obtain the provided total effect weights.
ps
data("tMdat")
## tMdat is small simulated data set included in twangMediation for
## demonstrating the functions. See ?tMdat for details
head(tMdat)
## The tMdat data contains the following variables:
## w1, w2, w3 -- Simulatad covariates
## A -- Simulated dichotomous exposure indicator
## M -- Simulated discrete mediator (11 values)
## Y -- Simulated continuous outcome
## te.wgt -- Estimated inverse probability weight, estimated using
## GBM via the twang ps function
fit.es.max <- wgtmed(M ~ w1 + w2 + w3,
data = tMdat,
a_treatment = "A",
y_outcome = "Y",
total_effect_wts = tMdat$te.wgt,
method = "ps",
ps_n.trees=1500,
ps_shrinkage=0.01,
ps_stop.method=c("es.max")
)
fit.es.max
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