GLMprob: Sampling probabilities estimated with logistic regression. In multipleNCC: Weighted Cox-Regression for Nested Case-Control Data

Description

Estimates sampling probabilities with logistic regression. 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.

`survtime`, `left.time` and continuous matching variables are included in the logistic regression as continuous variables while categorical matching variables are taken as factors.

Usage

 `1` ```GLMprob(survtime, samplestat, left.time = 0, match.var = 0, match.int = 0) ```

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. `left.time` Entry time if the survival times are left-truncated. Cohort dimension. `match.var` If the controls are matched to the cases (on other variables than time), match.var is the vector of matching variables. Cohort dimension. `match.int` A vector of length 2*number of matching variables. For caliper matching (matched on value pluss/minus epsilon) match.int should consist of c(-epsilon,epsilon). For exact matching match.int should consist of c(0,0).

Value

A vector of cohort dimension of sampling probabilities.

Author(s)

Nathalie C. Stoer

References

Stoer NC and Samuelsen SO (2013): Inverse probability weighting in nested case-control studies with additional matching - a simulation study. Statistics in Medicine, 32(30), 5328-5339.

`wpl`, `coxph`, `Chenprob`, `GAMprob`, `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) GLMprob(agestop,samplestat,agestart) GLMprob(agestop,samplestat,agestart,match.var=cbind(sex,bmi),match.int=c(0,0,-2,2)) ## The function is currently defined as function (survtime, samplestat, left.time = 0, match.var = 0, match.int = 0) { n.cohort = length(survtime) status = rep(0, n.cohort) status[samplestat > 1] = 1 samplestat[samplestat > 1] = 1 pglm = pGLM(status, survtime, samplestat, n.cohort, left.time, match.var, match.int) p = rep(1, n.cohort) p[status == 0] = pglm p } ```