Chenprob {multipleNCC}

Sampling probabilities estimated with local averaging.

Description

Estimates sampling probabilities with local averaging (Chen, 2001). The weights included in the Cox-regressions (wpl) and which could be used for other procedures are inverse sampling probabilities i.e. the inverse of these probabilities. The probabilties are estimated for all subjects in the cohort.

Usage

Chenprob(survtime, samplestat, no.intervals = 10, left.time = 0, 
no.intervals.left = c(3,4))

Arguments

survtime

Follow-up time for all cohort subjects

samplestat

A vector containing sampling and status information: 0 represents non-sampled subjects in the cohort, 1: sampled controls, 2,3,... indicate different events. Cohort dimension.

no.intervals

Number of intervals for censoring times for Chen-weights with only right censoring

left.time

Entry time if the survival times are left-truncated. Cohort dimension.

no.intervals.left

Number of intervals for Chen-weights with left-truncation. A vector on the form [number of intervals for left truncated time, number of intervals for survival time].

Value

A vector of cohort dimension of sampling probabilities.

Author(s)

Nathalie C. Stoer

References

Chen KN (2001) Generalized case-cohort sampling. J Roy Stat Soc Ser B 63(4):791-809
Stoer NC and Samuelsen SO (2012): Comparison of estimators in nested case-control studies with multiple outcomes. Lifetime Data Analysis, 18(3), 261-283.

See Also

wpl, coxph, GAMprob, GLMprob, KMprob

Examples

   

data(CVD_Accidents)
attach(CVD_Accidents)
Chenprob(agestop,samplestat,left.time=agestart)
Chenprob(agestop,samplestat,left.time=agestart,no.intervals.left=c(3,4))

function (survtime, samplestat, no.intervals, left.time = 0, no.intervals.left = 0) 
{
    n.cohort = length(survtime)
    status = rep(0, n.cohort)
    status[samplestat > 1] = 1
    samplestat[samplestat > 1] = 1
    ind.no = 1:length(samplestat)
    p = pChen(status, survtime, samplestat, ind.no, n.cohort, 
        no.intervals, left.time, no.intervals.left)
    p[status == 1] = 1
    p
  }