Multiple Cases per Subject in Complex Samples Cox Regression

Researchers investigating survival times for patients exiting a rehabilitation program post-ischemic stroke face a number of challenges.

Multiple cases per subject. Variables representing patient medical history should be useful as predictors. Over time, patients may experience major medical events that alter their medical history. In this dataset, the occurrence of myocardial infarction, ischemic stroke, or hemorrhagic stroke is noted and the time of the event recorded. You could create computable time-dependent covariates within the procedure to include this information in the model, but it should be more convenient to use multiple cases per subject. Note that the variables were originally coded so that the patient history is recorded across variables, so you will need to restructure the dataset.

Left-truncation. The onset of risk starts at the time of the ischemic stroke. However, the sample only includes patients who have survived the rehabilitation program, thus the sample is left-truncated in the sense that the observed survival times are "inflated" by the length of rehabilitation. You can account for this by specifying the time at which they exited rehabilitation as the time of entry into the study.

No sampling plan. The dataset was not collected via a complex sampling plan and is considered to be a simple random sample. You will need to create an analysis plan to use Complex Samples Cox Regression.

The dataset is collected in stroke_survival.sav. See the topic Sample Files for more information. Use the Restructure Data Wizard to prepare the data for analysis, then the Analysis Preparation Wizard to create a simple random sampling plan, and finally Complex Samples Cox Regression to build a model for survival times.

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