influences.RH {CaseCohortCoxSurvival}R Documentation

influences.RH

Description

Computes the influences on the log-relative hazard. Can take calibration of the design weights into account.

Usage

influences.RH(mod, calibrated = NULL, A = NULL)

Arguments

mod

a cox model object, result of function coxph.

calibrated

are calibrated weights used for the estimation of the parameters? If calibrated = TRUE, the argument below needs to be provided. Default is FALSE.

A

n \times q matrix with the values of the auxiliary variables used for the calibration of the weights in the whole cohort. Needs to be provided if calibrated = TRUE.

Details

influences.RH works for estimation from a case-cohort with design weights or calibrated weights (case-cohort consisting of the subcohort and cases not in the subcohort, i.e., case-cohort obtained from two phases of sampling).

If covariate information is missing for certain individuals in the phase-two data (i.e., case-cohort obtained from three phases of sampling), use influences.RH.missingdata.

influence.RH uses the influence formulas provided in Etievant and Gail (2024).

If calibrated = FALSE, the infuences are only provided for the individuals in the case-cohort. If calibrated = TRUE, the influences are provided for all the individuals in the cohort.

Value

infl.beta: matrix with the overall influences on the log-relative hazard estimates.

infl2.beta: matrix with the phase-two influences on the log-relative hazard estimates. Returned if calibrated = TRUE.

beta.hat: vector of length p with log-relative hazard estimates.

References

Etievant, L., Gail, M. H. (2024). Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data. Lifetime Data Analysis, 30, 572-599.

See Also

estimation, estimation.CumBH, estimation.PR, influences, influences.CumBH, influences.PR, influences.missingdata, influences.RH.missingdata, influences.CumBH.missingdata,
influences.PR.missingdata, robustvariance and variance.

Examples


  data(dataexample.stratified, package="CaseCohortCoxSurvival")
  cohort <- dataexample.stratified$cohort
  casecohort <- cohort[which(cohort$status == 1 |
                       cohort$subcohort == 1),] # the stratified case-cohort
  casecohort$weights <- casecohort$strata.n / casecohort$strata.m
  casecohort$weights[which(casecohort$status == 1)] <- 1

  Tau1 <- 0
  Tau2 <- 8
  x <- c(-1, 1, -0.6) # given covariate profile for the pure risk

  # Estimation using the stratified case cohort with design weights
  mod <- coxph(Surv(event.time, status) ~ X1 + X2 + X3, data = casecohort,
               weight = weights, id = id, robust = TRUE)
  est <- influences(mod, Tau1 = Tau1, Tau2 = Tau2, x = x)

  # print the influences on the log-relative hazard estimates
  # est$infl.beta


[Package CaseCohortCoxSurvival version 0.0.36 Index]