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Complex Samples Cox Regression
Subject Identifier. You can easily incorporate piecewise-constant, time-dependent predictors by
splitting the observations for a single subject across multiple cases. For example, if you are
analyzing survival times for patients post-stroke, variables representing their medical history
should be useful as predictors. Over time, they may experience major medical events that alter
their medical history. The following table shows how to structure such a dataset: Patient ID is the
subject identifier, End time defines the observed intervals, Status records major medical events,
and Prior history of heart attack and Prior history of hemorrhaging are piecewise-constant,
time-dependent predictors.
Patient ID E n d time Status Prior history of
heart attack
Prior history of
hemorrhaging
1
5
Heart Attack
No No
1
7
Hemorrhaging
Yes No
18
Died
Yes Yes
224
Died
No No
38
Heart Attack
No No
315
Died
Yes No
Assumptions. The cases in the data file represent a sample from a complex design that should
be analyzed according to the specifications in the file selected in the Complex Samples Plan
dialog box.
Typically, Cox regression models assume proportional hazards—that is, the ratio of hazards
from one case to another should not vary over time. If this assumption does not hold, you may
need to add time-dependent predictors to the model.
Kaplan-Meier Analysis. If you do not select any predictors (or do not enter any selected predictors
into the model) and choose the product limit method for computing the baseline survival curve on
the Options tab, the procedure performs a Kaplan-Meier type of survival analysis.
To Obtain Complex Samples Cox Regression
E From the menus choose:
Analyze > Complex Samples > Cox Regression...
E
Select a plan file. Optionally, select a custom joint probabilities file.
E Click Continue.