influences.RH.missingdata {CaseCohortCoxSurvival}R Documentation

influences.RH.missingdata

Description

Computes the influences on the log-relative hazard, when covariate data is missing for certain individuals in the phase-two data.

Usage

influences.RH.missingdata(mod, riskmat.phase2, dNt.phase2 = NULL,
status.phase2 = NULL, estimated.weights = FALSE, B.phase2 = NULL)

Arguments

mod

a cox model object, result of function coxph.

riskmat.phase2

at risk matrix for the phase-two data at all of the cases event times, even those with missing covariate data.

dNt.phase2

counting process matrix for failures in the phase-two data. Needs to be provided if status.phase2 = NULL.

status.phase2

vector indicating the case status in the phase-two data. Needs to be provided if dNt.phase2 = NULL.

estimated.weights

are the weights for the third phase of sampling (due to missingness) estimated? If estimated.weights = TRUE, the argument below needs to beprovided. Default is FALSE.

B.phase2

matrix for the phase-two data, with phase-three sampling strata indicators. It should have as many columns as phase-three strata (J^{(3)}), with one 1 per row, to indicate the phase-three stratum position. Needs to be provided if estimated.weights = TRUE.

Details

influences.RH.missingdata works for estimation from a case-cohort with design weights and when covariate data was missing for certain individuals in the phase-two data (i.e., case-cohort obtained from three phases of sampling and consisting of individuals in the phase-two data without missing covariate information).

If there are no missing covariates in the phase- two sample, use influences.RH with either design weights or calibrated weights.

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

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.

infl3.beta: matrix with the phase-three influences on the log-relative hazard estimates.

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

Examples


  data(dataexample.missingdata.stratified, package="CaseCohortCoxSurvival")

  cohort <- dataexample.missingdata.stratified$cohort
  phase2 <- cohort[which(cohort$phase2 == 1),] # the phase-two sample
  casecohort <- cohort[which(cohort$phase3 == 1),] # the stratified case-cohort

  B.phase2 <- cbind(1 * (phase2$W3 == 0), 1 * (phase2$W3 == 1))
  rownames(B.phase2)  <- cohort[cohort$phase2 == 1, "id"]
  B.phase3 <- cbind(1 * (casecohort$W3 == 0), 1 * (casecohort$W3 == 1))
  rownames(B.phase3)  <- cohort[cohort$phase3 == 1, "id"]
  total.B.phase2 <- colSums(B.phase2)
  J3 <- ncol(B.phase3)
  n <- nrow(cohort)

  # Quantities needed for estimation of the cumulative baseline hazard when
  # covariate data is missing
  mod.cohort <- coxph(Surv(event.time, status) ~ X2, data = cohort,
                      robust = TRUE) # X2 is available on all cohort members
  mod.cohort.detail <- coxph.detail(mod.cohort, riskmat = TRUE)

  riskmat.phase2 <- with(cohort, mod.cohort.detail$riskmat[phase2 == 1,])
  rownames(riskmat.phase2) <- cohort[cohort$phase2 == 1, "id"]
  observed.times.phase2 <- apply(riskmat.phase2, 1,
                                 function(v) {which.max(cumsum(v))})
  dNt.phase2 <- matrix(0, nrow(riskmat.phase2), ncol(riskmat.phase2))
  dNt.phase2[cbind(1:nrow(riskmat.phase2), observed.times.phase2)] <- 1
  dNt.phase2 <- sweep(dNt.phase2, 1, phase2$status, "*")
  colnames(dNt.phase2) <- colnames(riskmat.phase2)
  rownames(dNt.phase2) <- rownames(riskmat.phase2)

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

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

  est.true <- influences.missingdata(mod = mod.true, riskmat.phase2 = riskmat.phase2,
                                     dNt.phase2 = dNt.phase2, Tau1 = Tau1,
                                     Tau2 = Tau2, x = x)

  # print the influences on the log-relative hazard estimates
  # est.true$infl.beta
  # print the phase-two influences on the log-relative hazard estimates
  # est.true$infl2.beta
  # print the phase-three influences on the log-relative hazard estimates
  # est.true$infl3.beta

  # Estimation using the stratified case cohort with estimated weights, and
  # accounting for the estimation through the influences
  mod.estimated <- coxph(Surv(event.time, status) ~ X1 + X2 + X3,
                         data = casecohort, weight = weight.est, id = id,
                         robust = TRUE)

  est.estimated  <- influences.missingdata(mod.estimated,
                                           riskmat.phase2 = riskmat.phase2,
                                           dNt.phase2 = dNt.phase2,
                                           estimated.weights = TRUE,
                                           B.phase2 = B.phase2, Tau1 = Tau1,
                                           Tau2 = Tau2, x = x)

  # print the influences on the log-relative hazard estimates
  # est.estimated$infl.beta
  # print the phase-two influences on the log-relative hazard estimates
  # est.estimated$infl2.beta
  # print the phase-three influences on the log-relative hazard estimates
  # est.estimated$infl3.beta


[Package CaseCohortCoxSurvival version 0.0.36 Index]