Stat Computing > SPlus > Textbook Examples > Applied Survival Analysis, by Hosmer and Lemeshow

SPLUS Textbook Examples
Applied Survival Analysis by Hosmer and Lemeshow
Chapter 1: Introduction

Sections noted by R indicate differences between R and Splus.

Splus program.
R program.

Delimited data sets to be used in R:
hmohiv.csv To input the data sets in R use the following code which must be executed before running the R program:

>hmohiv <- read.csv("d:/hmohiv.csv", header=T)

Here is the same data set in SPLUS hmohiv.

Data analysis in SPLUS can be done either through a point and click interface or through syntax.  In this page we have shown have to use both syntax and point and click.

R-Note that in R the marks argument in the legend function is called pch and that the numbers for the symbols are different between the two packages.

plot(hmohiv$age[hmohiv$censor==1], hmohiv$time[hmohiv$censor==1], xlab="Age",
     ylab="Time" , pch=5, ylim=c(0, 65), xlim=c(15,55))

points(hmohiv$age[hmohiv$censor==0], hmohiv$time[hmohiv$censor==0], pch=18)

legend(40, 60, c("Censor=1", "Censor=0") , marks=c(5,18) )

Creating the new variable age1 which is equal to 1000/age.

hmohiv$age1 <- 1000/hmohiv$age

Point and Click: 
Creating a new variable, age1, which is equal to 1000/age.

DATA
    TRANSFORM

Make sure that you have hmohiv displayed in the data set field.  Type age1 in the Target Column field, this will be the name of the new variable we are creating and in the Expression field type 1000/age which is the transformation we are applying to age.  Now, click on the OK button and age1 will be the last variable in your data set.

Movie for creation of age1 using point and click.

plot(hmohiv$age1[hmohiv$censor==1], hmohiv$time[hmohiv$censor==1], xlab="1000/Age",
     ylab="Time" , pch=5, ylim=c(0, 65), xlim=c(15,55))

points(hmohiv$age1[hmohiv$censor==0], hmohiv$time[hmohiv$censor==0], pch=18)

legend(15, 60, c("Censor=1", "Censor=0") , marks=c(5,18) )

Table 1.2, p. 14
Note: In order to get the complete output we must first create the object "test" which is the
fitted survival regression and then get the summary of the object by using the summary command.

test <- survReg( Surv(time, censor) ~ age, hmohiv, dist="exponential")

summary(test)

            Value    Std. Error     z         p
(Intercept) 5.8590     0.5853     10.01    1.37e-023
       age -0.0939     0.0158     -5.96    2.59e-009

Scale fixed at 1
Exponential distribution

Loglik(model)= -275 Loglik(intercept only)= -292.3
Chisq= 34.5 on 1 degrees of freedom, p= 4.3e-009

Number of Newton-Raphson Iterations: 4
n= 100

Correlation of Coefficients:
     (Intercept)
 age -0.982

Point and Click:

STATISTICS
    SURVIVAL
        PARAMETRIC SURVIVAL...

In the Distribution field use the pull down menu and select "exponential".  Then click on the Create Formula button which will bring up a new menu.  Select time by clicking on time in the Choose Variables menu and then click on the Time1 button on the right.  Then select censor and click on the Censor Codes button on the right.  Then click on Add Response and you will see Surv(time, censor, type='right')~ 1 in the formula field.  Now, we need to add the independent variable, age, to the formula.  Select age in the Choose Variables menu and click on Main Effect(+) button in the Add Terms square.  Now you will see Surv(time, censor, type='right')~ age in the formula field which is the formula that we would like use.  So, click on the OK button at the bottom which brings you back to the original menu. Click the OK button and the survival regression will be executed.

Movie for table 1.2 and fig. 1.3 using point and click.

names(survReg.distributions)

[1] "extreme" "logistic" "gaussian" "weibull" "exponential"
[6] "rayleigh" "loggaussian" "lognormal" "loglogistic" "t"

Obtaining the predicted values.

hmohiv$p <- predict(test, type="response")
 

Sorting time, age, censor and p by age.  We want to order by ascending order of the variable age so we use age as the argument for the order function.  Then we create the ordered variables age.1, time.1, censor.1 and p.1.

Point and Click:
Sorting time, age, censor and p by age.

Note: The variables time.1, age.1, censor.1 and p.1 have already been created.  If you choose to do the sort you will actually create four new variables namely time.2, age.2, censor.2 and p.2 which will be identical to time.1, age.1, censor.1 and p.1.  SPLUS does not over-write these variables thereby allowing you to have variables sorting in multiple ways available at the same time.

DATA
    RESTRUCTURE
        SORT

First, make sure that you have hmohiv in the dataset slot.  Select age, time, censor and p in the Columns window (use control key and click to select more than one variable) and select age in the Sort By Columns window.  Click on the OK button at the bottom. 

Movie of sorting time, age, censor and p by age using point and click.

Four new variables have been created: time.1, age.1, censor.1 and p.1 which have been sorted by age.  These new variables will be used in the following graph.   

Fig. 1.3, p. 16.

plot(hmohiv$age.1[hmohiv$censor.1==1], hmohiv$time.1[hmohiv$censor.1==1], xlab="Age",
ylab="Time" , pch=5, ylim=c(0, 65), xlim=c(15,55))

points(hmohiv$age.1[hmohiv$censor.1==0], hmohiv$time.1[hmohiv$censor.1==0], pch=18)

lines(hmohiv$age.1, hmohiv$p.1)

legend(40, 50, c("Censor=1", "Censor=0") , marks=c(5,18) )

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