Data with repeated events are known as multivariate failure time data. With this type of data individuals may experience the event, such as a recurrent infection, multiple times. A number of different models can be fit using PROC PHREG. The COVSANDWICH|COVS option on the PROC PHREG statement can be used with the AGGREGATE suboption and the ID statement to identify clusters of related observations. This use provides a robust covariance matrix when the observations are clustered (not independent) and is often employed in the analysis of multivariate failure time data.
PHREG documentation examples
A good source for information is the PROC PHREG documentation. The "Overview" section and "Analysis of Multivariate Failure Time Data" in the Details section of the PHREG documentation provide a thorough discussion of this type of data and of the different types of models that can be fit. See also the example titled "Analysis of Recurrent Events Data" in the PHREG documentation.
SAS Education Training Course
The course Survival Analysis Using the Proportional Hazards Model discusses a number of these models including the Andersen-Gill (1982) or AG counting-process model, the Prentice, Williams and Prentice (1981) or PWP conditional models, and the Wei, Lin and Weissfeld (1989) or WLW marginal model. The
course notes are available for purchase, are written at an approachable level, and give some advice on which model might be appropriate for a given situation. Much of the information in the course notes can also be found in the "Recurrent Event Models" section of Hosmer and Lemeshow (1999). This text and other references are listed below.
References:
Andersen, P. K., Borgan, Ø., Gill, R. D., and Keiding, N. (1992), Statistical Models Based on Counting Processes, Springer-Verlag, New York, NY.
Andersen, P. K. and Gill, R. D. (1982), "Cox's Regression Model Counting Process: A Large Sample Study," Annals of Statistics, 10, 1100 - 1120.
Hosmer, D.W., Jr. and Lemeshow, S. (1999) Applied Survival Analysis: Regression Modeling of Time to Event Data, John Wiley & Sons, New York, NY.
Prentice, R. L., Williams, B. J., and Peterson, A. V. (1981), "On the Regression Analysis of Multivariate Failure Time Data," Biometrika, 68, 373 - 379.
Wei, L. J., Lin, D. Y., and Weissfeld, L. (1989), "Regression Analysis of Multivariate Incomplete Failure Time Data by Modeling Marginal Distribution," Journal of the American Statistical Association, 84, 1065 - 1073.
Operating System and Release Information
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Linux | | |
Linux for x64 | | |
IRIX | | |
HP-UX IPF | | |
HP-UX | | |
AIX | | |
ABI+ for Intel Architecture | | |
64-bit Enabled Solaris | | |
64-bit Enabled HP-UX | | |
64-bit Enabled AIX | | |
Windows Vista | | |
Windows Millennium Edition (Me) | | |
Microsoft Windows XP Professional | | |
Microsoft Windows Server 2003 Standard Edition | | |
Microsoft Windows Server 2003 Enterprise Edition | | |
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Microsoft Windows NT Workstation | | |
Microsoft Windows 2000 Professional | | |
Microsoft Windows 2000 Server | | |
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Microsoft Windows 95/98 | | |
OS/2 | | |
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Microsoft® Windows® for 64-Bit Itanium-based Systems | | |
OpenVMS VAX | | |
z/OS | | |
OpenVMS Alpha | | |
OpenVMS on HP Integrity | | |
Solaris | | |
Solaris for x64 | | |
Tru64 UNIX | | |
*
For software releases that are not yet generally available, the Fixed
Release is the software release in which the problem is planned to be
fixed.