Survival Times and White Blood Counts for Leukaemia Patients

Description

A data frame of data from 33 leukaemia patients.

Usage

leuk

Format

A data frame with columns:

wbc

white blood count.

ag

a test result, "present" or "absent".

time

survival time in weeks.

Details

Survival times are given for 33 patients who died from acute myelogenous leukaemia. Also measured was the patient's white blood cell count at the time of diagnosis. The patients were also factored into 2 groups according to the presence or absence of a morphologic characteristic of white blood cells. Patients termed AG positive were identified by the presence of Auer rods and/or significant granulation of the leukaemic cells in the bone marrow at the time of diagnosis.

Source

Cox, D. R. and Oakes, D. (1984) Analysis of Survival Data. Chapman & Hall, p. 9.

Taken from

Feigl, P. & Zelen, M. (1965) Estimation of exponential survival probabilities with concomitant information. Biometrics 21, 826–838.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

library(survival)
plot(survfit(Surv(time) ~ ag, data = leuk), lty = 2:3, col = 2:3)

# now Cox models
leuk.cox <- coxph(Surv(time) ~ ag + log(wbc), leuk)
summary(leuk.cox)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(MASS)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MASS/leuk.Rd_%03d_medium.png", width=480, height=480)
> ### Name: leuk
> ### Title: Survival Times and White Blood Counts for Leukaemia Patients
> ### Aliases: leuk
> ### Keywords: datasets
> 
> ### ** Examples
> 
> library(survival)
> plot(survfit(Surv(time) ~ ag, data = leuk), lty = 2:3, col = 2:3)
> 
> # now Cox models
> leuk.cox <- coxph(Surv(time) ~ ag + log(wbc), leuk)
> summary(leuk.cox)
Call:
coxph(formula = Surv(time) ~ ag + log(wbc), data = leuk)

  n= 33, number of events= 33 

             coef exp(coef) se(coef)      z Pr(>|z|)   
agpresent -1.0691    0.3433   0.4293 -2.490  0.01276 * 
log(wbc)   0.3677    1.4444   0.1360  2.703  0.00687 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

          exp(coef) exp(-coef) lower .95 upper .95
agpresent    0.3433     2.9126     0.148    0.7964
log(wbc)     1.4444     0.6923     1.106    1.8857

Concordance= 0.726  (se = 0.065 )
Rsquare= 0.377   (max possible= 0.994 )
Likelihood ratio test= 15.64  on 2 df,   p=0.0004014
Wald test            = 15.06  on 2 df,   p=0.0005365
Score (logrank) test = 16.49  on 2 df,   p=0.0002629

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>