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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 | End time | Status | Prior history of | Prior history of |
| | | heart attack | hemorrhaging |
1 | 5 | Heart Attack | No | No |
1 | 7 | Hemorrhaging | Yes | No |
1 | 8 | Died | Yes | Yes |
2 | 24 | Died | No | No |
3 | 8 | Heart Attack | No | No |
3 | 15 | 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
EFrom the menus choose:
Analyze > Complex Samples > Cox Regression...
ESelect a plan file. Optionally, select a custom joint probabilities file.
EClick Continue.
