To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
- The response variable should be continuous
- Continuous data are measurements that may potentially take on any numeric value within a range of values along a continuous scale, including fractional or decimal values.
- The response data are often failure times
- To collect data, usually you measure the amount of time until a specific event occurs when it is subject to different conditions that are measured by one or more variables and/or factors. For example, you might measure the time until failure for an item running at different temperatures.
- If the response data of a subject is characterized by a time interval in which the subject is at risk, use Fit Cox Model in
a Counting Process
Form.
- The failure times must be independent
- The failure time for one item should not influence the failure time of another item. If the failure times are dependent, the results may not be accurate. For example, times between failures for a repairable system are often not independent. If you have data where the subject can experience the event of interest multiple times, like a repairable system, use Fit Cox Model in
a Counting Process
Form.
- The predictor variables must be fixed
- A predictor is fixed when you know its value at the beginning of the study and it does not change during the study period. For example, the birthplace of a subject is a fixed predictor.
- You must account for incomplete data
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Because the response data is time-to-event, it is subject to censoring and truncation. For Cox regression models, the most common form of censoring is right-censoring, and the most common form of truncation is left-truncation. You can specify a column to indicate which response times are censored and uncensored.
- Right censored: A subject response time is right-censored if the subject does not experience the event of interest before the study ends, or if the subject is removed from the study before they experience the event. For example, a censored observation occurs if a unit functions after the test period or if a subject relocates to a new city and withdraws from a study.
- Left-truncation or delay entries: Left truncation occurs when you do not observe a subject at the start of the study. Instead, you include them later in the study when an intermediate event occurs. The time when the subject enters the study is known as the entry time or truncation time. For example, you don't include patients on a waiting list for an organ transplant until an organ is available for transplant.
- Subjects on different treatments experience the event at proportional rates
- The Cox regression model does not require you to specify a parametric distribution for the response data. However, the model assumes that individuals in two different treatments have proportional hazards or risks to experience the event. The proportional hazards assumption provides a simple interpretation of the regression coefficients in terms of hazard ratios or relative risks. If the proportional hazards assumption does not hold, then the relative risks table can yield wrong conclusions. Use the tests for proportional hazards table, the Andersen plot, and the Arjas plot to verify this assumption.
- The model must be full rank
- A full rank model includes enough data to estimate all the terms in your model. Missing data, insufficient data, or high collinearity can prevent a model from being full rank. If the model is not full rank, Minitab will alert you when you perform the analysis. You can often resolve this issue by removing unimportant, higher-order interactions from the model.
