Running a parametric survival curves analysis with XLSTAT-Life

An Excel sheet with both the data and results can be downloaded by clicking here.

The data have been obtained in [Gehan E.A. (1965). A generalized Wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika, 52, pp 203—223] and represent a randomized clinical trial investigating the effect of the drug 6-mercaptopurine on remission times (in weeks) of acute leukemia patients.

Our goal is to determine if the failure time follows a Weibull distribution and to compare groups.

Goal of this parametric survival curves analysis

Our goal is to determine if a Weibull distribution fits well the survival time of the patients and to compare groujps of patients.

Setting up a parametric survival curves analysis

After opening XLSTAT, select the XLSTAT / XLSTAT-Life / Parametric survival curves command, or click on the corresponding button of the XLSTAT-Life toolbar (see below).

Paramtric suvival curve menu

Once you've clicked on the button, the parametric survival curves box will appear. Select the data on the Excel sheet.

The Time data corresponds to the durations when the patients either relapsed or were censored.

The Status indicator describes whether a patient relapsed (event code=1) or was censored (censored code = 0) at a given time.

So that XLSTAT takes into account the information whether the patient belongs to the control or the treated group, we need to select the groups information.

Select the Weibull distribution.

paramtric survival curve dialog box

The computations begin once you have clicked on OK. The results will then be displayed on a new Excel sheet.

Interpreting the results of a parametric survival curves analysis

The first table displays a summary of the data for the first group.

Summary statistics parametric model

The next table gives the parameter and significance level of the intercept and scale parameter of the Weibull distribution for that group.

Model parameter survival curve analysis

We can see that these coefficients are significant.

Then, we can visualize several curves, including the survival distribution function (SDF, or survivor function, or reliability function).

Survival distribtuion function plot

 All these results are also displayed for the control group.

Statistics control group

coefficients control group

Both coefficients are significant.

survival curve control

 Finally, a plot comparing both groups is displayed.

Comparison of survival curves

We can see that the control group has a much shorter survival time than the treated group.

The quantiles (percentiles) obtained with the Weibull distribution for each group are also displayed. The median time is equal to 25.75 for the treated group.

Quantiles for survival curves

If explanatory variables are also included, then the parametric survival regression model can be used.

 

About KCS

Kovach Computing Services (KCS) was founded in 1993 by Dr. Warren Kovach. The company specializes in the development and marketing of inexpensive and easy-to-use statistical software for scientists, as well as in data analysis consulting.

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