qgcomp (version 2.17.4)

hurdlemsm_fit: Secondary prediction method for the (hurdle) qgcomp MSM.

Description

this is an internal function called by qgcomp.hurdle.boot, but is documented here for clarity. Generally, users will not need to call this function directly.

Usage

hurdlemsm_fit(
  f,
  qdata,
  intvals,
  expnms,
  main = TRUE,
  degree = 1,
  id = NULL,
  weights,
  MCsize = 10000,
  containmix = list(count = TRUE, zero = TRUE),
  bayes = FALSE,
  x = FALSE,
  msmcontrol = hurdlemsm_fit.control(),
  ...
)

Arguments

f

an r formula representing the conditional model for the outcome, given all exposures and covariates. Interaction terms that include exposure variables should be represented via the AsIs function

qdata

a data frame with quantized exposures (as well as outcome and other covariates)

intvals

sequence, the sequence of integer values that the joint exposure is 'set' to for estimating the msm. For quantile g-computation, this is just 0:(q-1), where q is the number of quantiles of exposure.

expnms

a character vector with the names of the columns in qdata that represent the exposures of interest (main terms only!)

main

logical, internal use: produce estimates of exposure effect (psi) and expected outcomes under g-computation and the MSM

degree

polynomial bases for marginal model (e.g. degree = 2 allows that the relationship between the whole exposure mixture and the outcome is quadratic. Default=1)

id

(optional) NULL, or variable name indexing individual units of observation (only needed if analyzing data with multiple observations per id/cluster)

weights

not yet implemented

MCsize

integer: sample size for simulation to approximate marginal hazards ratios

containmix

named list of logical scalars with names "count" and "zero"

bayes

not used

x

keep design matrix? (logical)

msmcontrol

named list from hurdlemsm_fit.control

...

arguments to hurdle (e.g. dist)

Details

This function first computes expected outcomes under hypothetical interventions to simultaneously set all exposures to a specific quantile. These predictions are based on g-computation, where the exposures are `quantized', meaning that they take on ordered integer values according to their ranks, and the integer values are determined by the number of quantile cutpoints used. The function then takes these expected outcomes and fits an additional model (a marginal structural model) with the expected outcomes as the outcome and the intervention value of the exposures (the quantile integer) as the exposure. Under causal identification assumptions and correct model specification, the MSM yields a causal exposure-response representing the incremental change in the expected outcome given a joint intervention on all exposures.

See Also

qgcomp.cox.boot, and qgcomp.cox.noboot

Examples

set.seed(50)
n=100
if (FALSE) {
dat <- data.frame(y=rbinom(n, 1, 0.5)*rpois(n, 1.2), x1=runif(n), x2=runif(n), z=runif(n))
expnms = c("x1", "x2")
q = 4
qdata = quantize(dat, q=q, expnms=expnms)$data
f = y ~ x1 + x2 + z | 1
msmfit <- hurdlemsm_fit(f, qdata, intvals=(1:q)-1, expnms, main=TRUE,
  degree=1, id=NULL, MCsize=10000, containmix=list(count=TRUE, zero=FALSE),  
  x=FALSE)
msmfit$msmfit
}