qgcompint (version 1.0.2)

qgcomp.emm.cox.noboot: EMM for Quantile g-computation with survival outcomes under linearity/additivity

Description

This function performs quantile g-computation in a survival setting, , allowing effect measure modification by a binary, categorical or continuous covariate. This allows testing of statistical interaction as well as estimation of stratum specific effects.

Usage

qgcomp.emm.cox.noboot(
  f,
  data,
  expnms = NULL,
  emmvar = NULL,
  q = 4,
  breaks = NULL,
  id = NULL,
  weights,
  cluster = NULL,
  alpha = 0.05,
  errcheck = TRUE,
  ...
)

Value

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 weights/standardized coefficients in the positive (pos.weights) and negative (neg.weights) directions.

Arguments

f

R style survival formula, which includes Surv in the outcome definition. E.g. Surv(time, event) ~ exposure. Offset terms can be included via Surv(time, event) ~ exposure + offset(z)

data

data frame

expnms

character vector of exposures of interest

emmvar

(character) name of effect measure modifier in dataset (if categorical, must be coded as a factor variable)

q

NULL or number of quantiles used to create quantile indicator variables representing the exposure variables. If NULL, then gcomp proceeds with un-transformed version of exposures in the input datasets (useful if data are already transformed, or for performing standard g-computation)

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)

weights

"case weights" - passed to the "weight" argument of coxph

cluster

not yet implemented

alpha

alpha level for confidence limit calculation

errcheck

(logical, default=TRUE) include some basic error checking. Slightly faster if set to false (but be sure you understand risks)

...

arguments to glm (e.g. family)

See Also

qgcomp.cox.noboot

Examples

set.seed(5)
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+z
(fit1 <- survival::coxph(f, data = dat))
(obj <- qgcomp.emm.cox.noboot(f, expnms = expnms, emmvar="z", data = dat))

#categorical emm
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=sample(0:2, N, replace=TRUE))
 dat$z = as.factor(dat$z)
expnms=paste0("x", 1:2)
f = survival::Surv(time, d)~x1 + x2+z
(obj2 <- qgcomp.emm.cox.noboot(f, expnms = expnms, emmvar="z", data = dat))