dataMultiv: Simulated data for two types of recurrent events and a...
In socale/frailtypack: Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints
Description
This contains a simulated sample of of 800 subjects and 1652 observations.
This dataset can be used to illustrate how to fit a joint multivariate
frailty model. Two gaussian correlated random effects were generated with
mean 0, variances 0.5 and a correlation coefficient equals to 0.5. The
coefficients α_1 and α_2 were fixed to 1. The three
baseline hazard functions followed a Weibull distribution and right
censoring was fixed at 5.
Usage
1
Format
This data frame contains the following columns:
- PATIENT
identification of patient
- obs
number of observation
for a patient
- TIME0
start of interval
- TIME1
end of interval
(death or censoring time)
- INDICREC
recurrent of type 1 status (0:no,
1:yes)
- INDICMETA
recurrent of type 2 status (0:no, 1:yes)
- INDICDEATH
censoring status (0:alive, 1:death)
- v1
dichotomous
covariate (0,1)
- v2
dichotomous covariate (0,1)
- v3
dichotomous covariate (0,1)
- TIMEGAP
time to event
socale/frailtypack documentation built on June 15, 2021, 3:37 a.m.
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Description
This contains a simulated sample of of 800 subjects and 1652 observations. This dataset can be used to illustrate how to fit a joint multivariate frailty model. Two gaussian correlated random effects were generated with mean 0, variances 0.5 and a correlation coefficient equals to 0.5. The coefficients α_1 and α_2 were fixed to 1. The three baseline hazard functions followed a Weibull distribution and right censoring was fixed at 5.
Usage
1 |
Format
This data frame contains the following columns:
- PATIENT
identification of patient
- obs
number of observation for a patient
- TIME0
start of interval
- TIME1
end of interval (death or censoring time)
- INDICREC
recurrent of type 1 status (0:no, 1:yes)
- INDICMETA
recurrent of type 2 status (0:no, 1:yes)
- INDICDEATH
censoring status (0:alive, 1:death)
- v1
dichotomous covariate (0,1)
- v2
dichotomous covariate (0,1)
- v3
dichotomous covariate (0,1)
- TIMEGAP
time to event
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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