influences.PR {CaseCohortCoxSurvival}R Documentation

influences.PR

Description

Computes the influences on the pure risk in the time interval [Tau1, Tau2] and for a given covariate profile x, from that on the log-relative hazard and cumulative baseline hazard. Can take calibration of the design weights into account.

Usage

influences.PR(beta, Lambda0.Tau1Tau2, x = NULL, infl.beta,
infl.Lambda0.Tau1Tau2, calibrated = NULL, infl2.beta = NULL,
infl2.Lambda0.Tau1Tau2 = NULL)

Arguments

beta

vector of length p with log-relative hazard values.

Lambda0.Tau1Tau2

cumulative baseline hazard in [Tau1, Tau2].

x

vector of length p, specifying the covariate profile considered for the pure risk. Default is (0,...,0).

infl.beta

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

infl.Lambda0.Tau1Tau2

vector with the overall influences on the cumulative baseline hazard estimate in [Tau1, Tau2].

calibrated

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

infl2.beta

matrix with the phase-two influences on the log-relative hazard estimates. Needs to be provided if missing.data = TRUE.

infl2.Lambda0.Tau1Tau2

vector with the phase-two influences on the cumulative baseline hazard estimate in [Tau1, Tau2]. Needs to be provided if missing.data = TRUE.

Details

influences.PR 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.PR.missingdata.

influences 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.Pi.x.Tau1Tau2.hat: vector with the overall influences on the pure risk estimate in [Tau1, Tau2] and for covariate profile x.

infl2.Pi.x.Tau1Tau2.hat: vector with the phase-two influences on the pure risk estimate in [Tau1, Tau2] and for covariate profile x. Returned if calibrated = TRUE.

Pi.x.Tau1Tau2.hat: pure risk estimate in [Tau1, Tau2] and for covariate profile x.

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.RH, influences.CumBH, 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 pure risk estimate
  # est$infl.Pi.x.Tau1Tau2

[Package CaseCohortCoxSurvival version 0.0.36 Index]