A competing-risks nomogram for sarcoma-specific death following local recurrence.
The majority of staging systems focus on the definition of stage, and, therefore, prediction of prognosis. In the current era of clinical trial research, it has become apparent that the clinical stage alone is not sufficient to assess patient risk of treatment failure. As the number of biological markers increases, our ability to partition the traditional disease classification system improves, and our ability to predict patient success continues to increase. One approach to quantifying individual patient risk is through the nomogram. Nomograms are graphical representations of statistical models, which provide the probability of treatment outcome based on patient-specific covariates. We will focus on the use of the nomogram when the response variable is time to failure and there are multiple, possibly dependent, competing causes of failure. In this setting, estimation of the failure probability through direct application of the Cox proportional hazards model provides the probability of failure (for example, death from cancer) assuming failure from a dependent competing cause will not occur. In many clinical settings this is an unrealistic assumption. The purpose of this study is to illustrate the use of the conditional cumulative incidence function for providing a patient-specific prediction of the probability of failure in the setting of competing risks. A competing risks nomogram is produced to estimate the probability of death due to sarcoma for patients who have already developed a local recurrence of their initially treated soft-tissue sarcoma.
Department of Urology, Memorial Sloan-Kettering Cancer Center, New York, USA. kattanm@mskcc.org
Michael W
MW
Kattan
Glenn
G
Heller
Murray F
MF
Brennan
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000401:mortality / Q000473:pathology*; D018570:Risk Assessment / Q000379:methods; D012509:Sarcoma / Q000401:mortality / Q000473:pathology*; D016019:Survival Analysis