epi.insthaz: Event instantaneous hazard based on Kaplan-Meier survival...

In epiR: Tools for the Analysis of Epidemiological Data

Event instantaneous hazard based on Kaplan-Meier survival estimates

Description

Compute event instantaneous hazard on the basis of a Kaplan-Meier survival function.

Usage

epi.insthaz(survfit.obj, conf.level = 0.95)

Arguments

survfit.obj	a survfit object, computed using the survival package.
conf.level	magnitude of the returned confidence interval. Must be a single number between 0 and 1.

Details

Computes the instantaneous hazard of the event of interest, equivalent to the proportion of the group at risk failing per unit time.

Value

A data frame with the following variables: strata the strata identifier, time the observed event times, n.risk the number of individuals at risk at the start of the event time, n.event the number of individuals that experienced the event of interest at the event time, sest the observed Kaplan-Meier survival function, slow the lower bound of the confidence interval for the observed Kaplan-Meier survival function, supp the upper bound of the confidence interval for the observed Kaplan-Meier survival function, hest the observed instantaneous hazard (the proportion of the population at risk experiencing the event of interest per unit time), hlow the lower bound of the confidence interval for the observed instantaneous hazard, and hupp the upper bound of the confidence interval for the observed instantaneous hazard.

References

Venables W, Ripley B (2002). Modern Applied Statistics with S, fourth edition. Springer, New York, pp. 353 - 385.

Singer J, Willett J (2003). Applied Longitudinal Data Analysis Modeling Change and Event Occurrence. Oxford University Press, London, pp. 348.

Examples

## EXAMPLE 1:
library(survival)
dat.df01 <- lung

dat.df01$status <- ifelse(dat.df01$status == 1, 0, dat.df01$status)
dat.df01$status <- ifelse(dat.df01$status == 2, 1, dat.df01$status)
dat.df01$sex <- factor(dat.df01$sex, levels = c(1,2), 
   labels = c("Male","Female"))

lung.km01 <- survfit(Surv(time = time, event = status) ~ 1, data = dat.df01)
lung.haz01 <- epi.insthaz(survfit.obj = lung.km01, conf.level = 0.95)

lung.shaz01 <- data.frame(
  time = lowess(lung.haz01$time, lung.haz01$hlow, f = 0.20)$x,
  hest =  lowess(lung.haz01$time, lung.haz01$hest, f = 0.20)$y,
  hlow =  lowess(lung.haz01$time, lung.haz01$hlow, f = 0.20)$y,
  hupp =  lowess(lung.haz01$time, lung.haz01$hupp, f = 0.20)$y)

plot(x = lung.haz01$time, y = lung.haz01$hest, xlab = "Time (days)", 
   ylab = "Daily probability of event", type = "s", 
   col = "grey", ylim = c(0, 0.05))
lines(x = lung.shaz01$time, y = lung.shaz01$hest, 
      lty = 1, lwd = 2, col = "black")
lines(x = lung.shaz01$time, y = lung.shaz01$hlow, 
      lty = 2, lwd = 1, col = "black")
lines(x = lung.shaz01$time, y = lung.shaz01$hupp, 
      lty = 2, lwd = 1, col = "black")

## Not run:  
library(ggplot2)

ggplot() +
  theme_bw() +
  geom_step(data = lung.haz01, aes(x = time, y = hest), colour = "grey") + 
  geom_smooth(data = lung.haz01, aes(x = time, y = hest), method = "loess", 
     colour = "black", size = 0.75, linetype = "solid", 
     se = FALSE, span = 0.20) +
  geom_smooth(data = lung.haz01, aes(x = time, y = hlow), method = "loess", 
     colour = "black", size = 0.5, linetype = "dashed", 
     se = FALSE, span = 0.20) +
  geom_smooth(data = lung.haz01, aes(x = time, y = hupp), method = "loess", 
     colour = "black", size = 0.5, linetype = "dashed", 
     se = FALSE, span = 0.20) +
  scale_x_continuous(limits = c(0,1000), name = "Time (days)") +
  scale_y_continuous(limits = c(0,0.05), name = "Daily probability of event") 

## End(Not run)


## EXAMPLE 2:
## Now stratify by gender:

lung.km02 <- survfit(Surv(time = time, event = status) ~ sex, data = dat.df01)
lung.haz02 <- epi.insthaz(survfit.obj = lung.km02, conf.level = 0.95)

## Not run:  
library(ggplot2)

ggplot() +
  theme_bw() +
  geom_step(data = lung.haz02, aes(x = time, y = hest), colour = "grey") + 
  facet_grid(strata ~ .) +
  geom_smooth(data = lung.haz02, aes(x = time, y = hest), method = "loess", 
     colour = "black", size = 0.75, linetype = "solid", 
     se = FALSE, span = 0.20) +
  geom_smooth(data = lung.haz02, aes(x = time, y = hlow), method = "loess", 
     colour = "black", size = 0.5, linetype = "dashed", 
     se = FALSE, span = 0.20) +
  geom_smooth(data = lung.haz02, aes(x = time, y = hupp), method = "loess", 
     colour = "black", size = 0.5, linetype = "dashed", 
     se = FALSE, span = 0.20) +
  scale_x_continuous(limits = c(0,1000), name = "Time (days)") +
  scale_y_continuous(limits = c(0,0.05), name = "Daily probability of event")

## End(Not run)

epiR documentation built on Sept. 30, 2024, 9:16 a.m.