qgcomp.cch.noboot: Quantile g-computation for survival outcomes in a case-cohort...

In qgcomp: Quantile G-Computation

Quantile g-computation for survival outcomes in a case-cohort design under linearity/additivity

Description

This function performs quantile g-computation in a survival setting. The approach estimates the covariate-conditional hazard ratio for a joint change of 1 quantile in each exposure variable specified in expnms parameter

Usage

qgcomp.cch.noboot(
  f,
  data,
  subcoh = NULL,
  id = NULL,
  cohort.size = NULL,
  expnms = NULL,
  q = 4,
  breaks = NULL,
  weights,
  cluster = NULL,
  alpha = 0.05,
  ...
)

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
subcoh	(From cch help) Vector of indicators for subjects sampled as part of the sub-cohort. Code 1 or TRUE for members of the sub-cohort, 0 or FALSE for others. If data is a data frame then subcoh may be a one-sided formula.
id	(From cch help) Vector of unique identifiers, or formula specifying such a vector.
cohort.size	(From cch help) Vector with size of each stratum original cohort from which subcohort was sampled
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 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.
weights	Not used here
cluster	not yet implemented
alpha	alpha level for confidence limit calculation
...	arguments to glm (e.g. family)

Details

For survival outcomes (as specified using methods from the survival package), this yields a conditional log hazard ratio representing a change in the expected conditional hazard (conditional on covariates) from increasing every exposure by 1 quantile. In general, this quantity quantity is not equivalent to g-computation estimates. Hypothesis test statistics and 95% confidence intervals are based on using the delta estimate variance of a linear combination of random variables.

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.

See Also

Other qgcomp_methods: qgcomp.cox.boot(), 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()

Examples

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: 

# weighted analysis
dat$w = runif(N)
qdata = quantize(dat, expnms=expnms)
(obj2 <- qgcomp.cox.noboot(f, expnms = expnms, data = dat, weight=w))
obj2$fit
survival::coxph(f, data = qdata$data, weight=w)

# not run: bootstrapped version is much slower
(obj2 <- qgcomp.cox.boot(f, expnms = expnms, data = dat, B=200, MCsize=20000))

## End(Not run)

qgcomp documentation built on Aug. 10, 2023, 5:07 p.m.