How do I generate a life table with XLSTAT-Life?
An Excel sheet with both the data and results can be downloaded by clicking here. The data have been obtained in [Lee E.T. (1992). Statistical Methods for Survival Data Analysis, Second Edition, John Wiley & Sons, New York) and represent the survival rate of patients with angina pectoris, during a 15 year period (Jan 1,1927 - Dec 31,1941). Survival time is measured as years from the time of diagnosis. The counts correspond to events (number of patients who died during a time interval) and to withdrawals (number of patients lost to follow up). Our goal is to determine to display the life table, analyse the median residual lifetime (or median survival time), and plot the non parametric estimate of the survival distribution function.
After opening XLSTAT, select the XLSTAT/XLSTAT-Life/Life table analysis command, or click on the corresponding button of the "XLSTAT-Life" toolbar (see below).
Once you've clicked on the button, the Life table analysis box will appear. Select the data on the Excel sheet. The "Time data" corresponds to interval end times. As the data correspond to counts, check the "Weighted data" option, and then select the "Died" data in the "Event indicator" field, and the "Censored" data in the "Censored indicator" field.
The size of the time intervals is set to 1.
The following charts are requested.
The computations begin once you have clicked on "OK". The results will then be displayed.
Interpreting the results of a life table analysis
The first table displays a summary of the data. The next table corresponds to the "Actuarial table". It contains the results of the life table analysis, including several key indicators such as the median survival time.
The third table isolates the median survival time and its variance. From these values we can conclude that the median residual lifetime for angina pectories is 5.3 years. In other words, out of 100 patients, 50 would be dead 5.3 years after having contracted the disease.
Last, we can visualize several curves, including the the survival distribution function (SDF, or survivor function), and the -Log(SDF) curve. From the latter, we see that the function is close to an exponential model.
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