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CAN THE PROPORTIONAL HAZARD STATUS OF A QUANTITATIVE VARIABLE BE DETERMINED BY ITS DISTRIBUTION?
Keywords:
distribution, proportional hazard, Schoenfeld test.DOI:
https://doi.org/10.17654/0972086323011Abstract
In the risk-truncated survival analysis, determining the proportional hazard status of the risk-truncated factor is critical to arriving at appropriate parameter estimations. Over the years, several methods for proportional hazard status determination have been proposed. In the work where the risk-truncated factor is age, can its distribution be used to determine its proportional hazard status? Three risk-truncated survival datasets were considered in this study. By boxplots, outliers of the factor were identified and histograms without the outliers overlaid with an added line and the normal densities of the factor were plotted to ascertain its distributions. Next, by the Schoenfeld test, the proportional hazard status of the factor was determined for each dataset. Finally, the distribution of the factor of each dataset was compared with its corresponding Schoenfeld test results. When the factor was skewed, it met the proportional hazard assumption in some cases and violated in another. Thus, the distribution of a quantitative variable – the risk-truncated factor – cannot be used to determine its proportional hazard status.
Received: May 8, 2023
Accepted: June 14, 2023
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