influences.CumBH.missingdata {CaseCohortCoxSurvival} | R Documentation |

## influences.CumBH.missingdata

### Description

Computes the influences on the log-relative hazard, baseline hazards at each unique event time, and on the cumulative baseline hazard in a given time interval [Tau1, Tau2], when covariate data is missing for certain individuals in the phase-two data.

### Usage

```
influences.CumBH.missingdata(mod, riskmat.phase2, dNt.phase2 = NULL,
status.phase2 = NULL, Tau1 = NULL, Tau2 = 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` |
vector indicating the case status in the phase-two data.
Needs to be provided if |

`Tau1` |
left bound of the time interval considered for the cumulative baseline hazard and pure risk. Default is the first event time. |

`Tau2` |
right bound of the time interval considered for the cumulative baseline hazard and pure risk. Default is the last event time. |

`estimated.weights` |
are the weights for the third phase of sampling (due to
missingness) estimated? If |

`B.phase2` |
matrix for the phase-two data, with phase-three sampling strata
indicators. It should have as many columns as phase-three strata ( |

### Details

`influences.CumBH.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).

If there are no missing covariates in the phase-two sample, use `influences.CumBH`

with either design weights or calibrated weights.

`influences.CumBH.missingdata`

uses the influence formulas provided in Etievant
and Gail (2023). More precisely, as in Section 5.4 if
`estimated.weights = TRUE`

, and as in Web Appendix H if
`estimated.weights = FALSE`

.

### Value

`infl.beta`

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

`infl.lambda0.t`

: matrix with the overall influences on the baseline hazards estimates at each unique event time.

`infl.Lambda0.Tau1Tau2.hat`

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

`infl2.beta`

: matrix with the phase-two influences on the log-relative hazard estimates.

`infl2.lambda0.t`

: matrix with the phase-two influences on the baseline hazards estimates at each unique event time.

`infl2.Lambda0.Tau1Tau2.hat`

: vector with the phase-two influences on the cumulative baseline hazard estimate in [Tau1, Tau2].

`infl3.beta`

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

`infl3.lambda0.t`

: matrix with the phase-three influences on the baseline hazards estimates at each unique event time.

`infl3.Lambda0.Tau1Tau2.hat`

: vector with the phase-three influences on the cumulative baseline hazard estimate in [Tau1, Tau2].

`beta.hat`

: vector of length `p`

with log-relative hazard estimates.

`lambda0.t.hat`

: vector with baseline hazards estimates at each unique event time.

`Lambda0.Tau1Tau2.hat`

: cumulative baseline hazard estimate in [Tau1, Tau2].

### References

Etievant, L., Gail, M.H. (2023). Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data. Submitted.

### See Also

`estimation`

, `estimation.CumBH`

, `estimation.PR`

,
`influences.missingdata`

, `influences.RH.missingdata`

,
`influences.PR.missingdata`

, `influences`

, `influences.RH`

, `influences.CumBH`

,
`influences.PR`

, `robustvariance`

and `variance`

.

### Examples

```
data(dataexample.missingdata, package="CaseCohortCoxSurvival")
cohort <- dataexample.missingdata$cohort # a simulated cohort
casecohort <- dataexample.missingdata$casecohort # a simulated stratified case-cohort
# phase-two data: dataexample.missingdata$casecohort.phase2
riskmat.phase2 <- dataexample.missingdata$riskmat.phase2
dNt.phase2 <- dataexample.missingdata$dNt.phase2
B.phase2 <- dataexample.missingdata$B.phase2
# Estimation using the stratified case-cohort with true known design weights
mod <- coxph(Surv(times, status) ~ X1 + X2 + X3, data = casecohort,
weight = weights.true, id = id, robust = TRUE)
estimation <- influences.CumBH.missingdata(mod = mod,
riskmat.phase2 = riskmat.phase2,
dNt.phase2 = dNt.phase2, Tau1 = 0,
Tau2 = 8)
# print the influences on the log-relative hazard estimates
#estimation$infl.beta
# print the influences on the cumulative baseline hazard estimate
#estimation$infl.Lambda0.Tau1Tau2
# print the phase-two influences on the log-relative hazard estimates
#estimation$infl2.beta
# print the phase-two influences on the cumulative baseline hazard estimate
#estimation$infl2.Lambda0.Tau1Tau2
# print the phase-three influences on the log-relative hazard estimates
#estimation$infl3.beta
# print the phase-three influences on the cumulative baseline hazard estimate
#estimation$infl3.Lambda0.Tau1Tau2
# Estimation using the stratified case-cohort with estimated weights, and
# accounting for the estimation through the influences
mod.est <- coxph(Surv(times, status) ~ X1 + X2 + X3, data = casecohort,
weight = weights.est, id = id, robust = TRUE)
estimation.est <- influences.CumBH.missingdata(mod.est,
riskmat.phase2 = riskmat.phase2,
dNt.phase2 = dNt.phase2,
estimated.weights = TRUE,
B.phase2 = B.phase2,
Tau1 = 0, Tau2 = 8)
```

*CaseCohortCoxSurvival*version 0.0.34 Index]