Cutpoint Determination Methods in Survival Analysis using SAS®: Updated %FINDCUT macro

Statistical analysis that uses data from clinical or epidemiological studies, include continuous variables such as patient’s age, blood pressure, and various biomarkers. Over the years there has been increase in studies that focus on assessing associations between biomarkers and disease of interest. Many of the biomarkers are measured as continuous variables. Investigators seek to identify the possible cutpoint to classify patients as high risk versus low risk based on the value of the biomarker. Several data-oriented techniques such as median and upper quartile, and outcome-oriented techniques based on score, Wald and likelihood ratio tests are commonly used in the literature. Contal and O’Quigley (1999) presented a technique that used log rank test statistic in order to estimate the cutpoint. Their method was computationally intensive and hence was overlooked due to the unavailability of built in options in standard statistical software. In 2003, we had provided the %FINDCUT macro that used Contal and O’Quigley’s approach to identify a cutpoint when the outcome of interest was measured as time to event. Over the past decade demand for this macro has continued to grow that has led us to consider updating the %FINDCUT macro to incorporate new tools and procedures from SAS such as array processing, Graph Template Language, and the REPORT procedure. New and updated features will include: results presented in a much cleaner report format, user specified cut points, macro parameter error checking, temporary data set clean-up, preserving current option settings, and increased processing speed. We intend to present the utility and added options of the revised %FINDCUT macro using a real life dataset. In addition, we will critically compare this method with some of the existing methods and discuss the use and misuse of categorizing a continuous covariate.