qgcomp.cox.boot  R Documentation 
This function yields population average effect estimates for (possibly right censored) timeto event outcomes
qgcomp.cox.boot(
f,
data,
expnms = NULL,
q = 4,
breaks = NULL,
id = NULL,
weights,
cluster = NULL,
alpha = 0.05,
B = 200,
MCsize = 10000,
degree = 1,
seed = NULL,
parallel = FALSE,
parplan = FALSE,
...
)
f 
R style survival formula, which includes 
data 
data frame 
expnms 
character vector of exposures of interest 
q 
NULL or number of quantiles used to create quantile indicator variables representing the exposure variables. If NULL, then gcomp proceeds with untransformed version of exposures in the input datasets (useful if data are already transformed, or for performing standard gcomputation) 
breaks 
(optional) NULL, or a list of (equal length) numeric vectors that characterize the minimum value of each category for which to break up the variables named in expnms. This is an alternative to using 'q' to define cutpoints. 
id 
(optional) NULL, or variable name indexing individual units of observation (only needed if analyzing data with multiple observations per id/cluster). Note that qgcomp.glm.noboot will not produce clusterappropriate standard errors. qgcomp.glm.boot can be used for this, which will use bootstrap sampling of clusters/individuals to estimate clusterappropriate standard errors via bootstrapping. 
weights 
"case weights"  passed to the "weight" argument of

cluster 
not yet implemented 
alpha 
alpha level for confidence limit calculation 
B 
integer: number of bootstrap iterations (this should typically be >=200, though it is set lower in examples to improve runtime). 
MCsize 
integer: sample size for simulation to approximate marginal hazards ratios (if < sample size, then set to sample size). Note that large values will slow down the fitting, but will result in higher accuracy  if you run the function multiple times you will see that results vary due to simulation error. Ideally, MCsize would be set such that simulation error is negligible in the precision reported (e.g. if you report results to 2 decimal places, then MCsize should be set high enough that you consistenty get answers that are the same to 2 decimal places). 
degree 
polynomial bases for marginal model (e.g. degree = 2 allows that the relationship between the whole exposure mixture and the outcome is quadratic. 
seed 
integer or NULL: random number seed for replicable bootstrap results 
parallel 
logical (default FALSE): use future package to speed up bootstrapping 
parplan 
(logical, default=FALSE) automatically set future::plan to plan(multisession) (and set to existing plan, if any, after bootstrapping) 
... 
arguments to glm (e.g. family) 
qgcomp.cox.boot' estimates the log(hazard ratio) per quantile increase in the joint exposure to all exposures in
expnms'. This function uses gcomputation to estimate the parameters of a
marginal structural model for the population average effect of increasing all
exposures in ‘expnms’ by a single quantile. This approach involves specifying
an underlying conditional outcome model, given all exposures of interest (possibly
with nonlinear basis function representations such as splines or product terms)
and confounders or covariates of interest. This model is fit first, which is used
to generate expected outcomes at each quantile of all exposures, which is then
used in a second model to estimate a population average doseresponse curve that
is linear or follows a simple polynomial function. See section on MCSize below
Test statistics and confidence intervals are based on a nonparametric bootstrap, using the standard deviation of the bootstrap estimates to estimate the standard error. The bootstrap standard error is then used to estimate Waldtype confidence intervals. Note that no bootstrapping is done on estimated quantiles of exposure, so these are treated as fixed quantities
MCSize is crucial to get accurate point estimates. In order to get marginal
estimates of the population hazard under different values of the joint exposure
at a given quantile for all exposures in expnms
, qgcomp.cox.boot
uses
Monte Carlo simulation to generate outcomes implied by the underlying conditional model
and then fit a separate (marginal structural) model to those outcomes. In order to get
accurate results that don't vary much from runtorun of this approach, MCsize
must be set large enough so that results are stable across runs according to a predetermined
precision (e.g. 2 significant digits).
a qgcompfit object, which contains information about the effect measure of interest (psi) and associated variance (var.psi), as well as information on the model fit (fit) and information on the marginal structural model (msmfit) used to estimate the final effect estimates.
Other qgcomp_methods:
qgcomp.cch.noboot()
,
qgcomp.cox.noboot()
,
qgcomp.glm.boot()
,
qgcomp.glm.noboot()
,
qgcomp.hurdle.boot()
,
qgcomp.hurdle.noboot()
,
qgcomp.multinomial.boot()
,
qgcomp.multinomial.noboot()
,
qgcomp.partials()
,
qgcomp.zi.boot()
,
qgcomp.zi.noboot()
set.seed(50)
N=200
dat < data.frame(time=(tmg < pmin(.1,rweibull(N, 10, 0.1))),
d=1.0*(tmg<0.1), x1=runif(N), x2=runif(N), z=runif(N))
expnms=paste0("x", 1:2)
f = survival::Surv(time, d)~x1 + x2
(fit1 < survival::coxph(f, data = dat))
(obj < qgcomp.cox.noboot(f, expnms = expnms, data = dat))
## Not run:
# not run (slow when using boot version to proper precision)
(obj2 < qgcomp.cox.boot(f, expnms = expnms, data = dat, B=10, MCsize=20000))
# weighted analysis
# using future package, marginalizing over confounder z
(obj3 < qgcomp.cox.boot(survival::Surv(time, d)~x1 + x2 + z, expnms = expnms, data = dat,
B=1000, MCsize=20000, parallel=TRUE, parplan=TRUE))
# nonconstant hazard ratio, nonlinear terms
(obj4 < qgcomp.cox.boot(survival::Surv(time, d)~factor(x1) + splines::bs(x2) + z,
expnms = expnms, data = dat,
B=1000, MCsize=20000, parallel=FALSE, degree=1))
# weighted analysis
dat$w = runif(N)
(objw1 < qgcomp.cox.noboot(f, expnms = expnms, data = dat, weights=w))
(objw2 < qgcomp.cox.boot(f, expnms = expnms, data = dat, weights=w, B=5, MCsize=20000))
## End(Not run)
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