Longitudinal measurements of the left ventricular mass index (LVMI) and survival times for 256 patients who received an aortic valve replacement (Collett, 2023, Example 8.5).
Format
A data frame with 988 rows and 11 variables:
- id
Patient number (1-256).
- futime
Total follow-up time from the date of surgery, in years.
- status
Event indicator: 1 = death, 0 = right-censored.
- time
Time of the LVMI measurement after surgery, in years.
- lvmi
Standardised left ventricular mass index.
- age
Age of the patient in years.
- sex
Sex: 0 = male, 1 = female.
- redo
Previous cardiac surgery: 0 = no, 1 = yes.
- emerg
Operative urgency: 0 = elective, 1 = urgent or emergency.
- dm
Preoperative diabetes mellitus: 0 = no, 1 = yes.
- type
Type of valve: 1 = human tissue, 2 = porcine tissue.
Source
Collett, D. (2023) Modelling Survival Data in Medical Research, 4th ed. Chapman & Hall/CRC, Example 8.5 (Table 8.10). Data from Lim et al. (2008).
Details
The file is in long format: one row per LVMI measurement, and the baseline
and survival variables (futime, status, age, ...) are repeated on each
row for the patient. There are 988 measurement rows for 256 patients; 54 of
the patients died (status is 1, repeated on each of their rows). Patients
who had a second procedure are censored at that time so that they do not
appear twice in the survival data.
The data are used for joint modelling of a longitudinal and a survival
outcome (Examples 8.5-8.9). The full data set is available as heart.valve
in the joineR package.
Aliases
The data set is available under the aliases d8.5, d8.6, d8.7, d8.8,
d8.9; dX.Y is the alias for the data set used in Example X.Y of Collett
(2023). See dataset_by_exercise for the complete index.
Examples
# Collett (2023), Chapter 17 style -----------------------------------
# Examples 8.5-8.9: LVMI as a time-dependent covariate, built with tmerge()
base <- aortic[!duplicated(aortic$id), c("id", "futime", "status", "age", "sex")]
long <- aortic[, c("id", "time", "lvmi")]
cp <- survival::tmerge(base, base, id = id, endpt = event(futime, status))
cp <- survival::tmerge(cp, long, id = id, lvmi = tdc(time, lvmi))
survival::coxph(
survival::Surv(tstart, tstop, endpt) ~ lvmi + age + sex,
data = cp
)
#> Call:
#> survival::coxph(formula = survival::Surv(tstart, tstop, endpt) ~
#> lvmi + age + sex, data = cp)
#>
#> coef exp(coef) se(coef) z p
#> lvmi 0.005902 1.005920 0.001754 3.364 0.000767
#> age 0.108400 1.114493 0.018143 5.975 2.3e-09
#> sex 0.133898 1.143276 0.308666 0.434 0.664438
#>
#> Likelihood ratio test=55.94 on 3 df, p=4.326e-12
#> n= 913, number of events= 54
#> (254 observations deleted due to missingness)