dataOFSEP: A Simulated Sample From the OFSEP Cohort.

In RISCA: Causal Inference and Prediction in Cohort-Based Analyses

A Simulated Sample From the OFSEP Cohort.

Description

A data frame with 1300 simulated French patients with multiple sclerosis from the OFSEP cohort. The baseline is 1 year after the initiation of the first-line treatment.

Usage

data(dataOFSEP)

Format

A data frame with 1300 observations for the 3 following variables:

time

This numeric vector represents the follow up time in years (until disease progression or censoring)

event

This numeric vector represents the disease progression indicator at the follow-up end (1=progression, 0=censoring)

age

This numeric vector represents the patient age (in years) at baseline.

duration

This numeric vector represents the disease duration (in days) at baseline.

period

This numeric vector represents the calendar period: 1 in-between 2014 and 2018, and 0 otherwise.

gender

This numeric vector represents the gender: 1 for women.

relapse

This numeric vector represents the diagnosis of at least one relapse since the treatment initiation : 1 if at leat one event, and 0 otherwise.

edss

This vector of character string represents the EDSS level : "miss" for missing, "low" for EDSS between 0 to 2, and "high" otherwise.

t1

This vector of character string represents the new gadolinium-enhancing T1 lesion : "missing", "0" or "1+" for at least 1 lesion.

t2

This vector of character string represents the new T2 lesions : "no" or "yes".

rio

This numeric vector represents the modified Rio score.

References

Sabathe C et al. SuperLearner for survival prediction from censored data: extension of the R package RISCA. Submited.

Examples

data(dataOFSEP)

### Kaplan and Meier estimation of the disease progression free survival
plot(survfit(Surv(time, event) ~ 1, data = dataOFSEP),
     ylab="Disease progression free survival",
     xlab="Time after the first anniversary of the first-line treatment in years")

RISCA documentation built on March 31, 2023, 11:06 p.m.