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.

1 2 |

`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]. |

A vector of cohort dimension of sampling probabilities.

Nathalie C. Stoer

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.

`wpl`

, `coxph`

, `GAMprob`

, `GLMprob`

,
`KMprob`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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
}
``` |

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