Aim: The aim of the present study is to evaluate and quantify the bias of competing risks in an Italian oncologic cohort comparing results from different statistical analysis methods.
Background: Competing risks are very common in randomized clinical trials and observational studies, in particular oncology and radiotherapy ones, and their inappropriate management causes results distortions widely present in clinical scientific articles.
Materials and methods: This is a single-institution phase II trial including 41 patients affected by prostate cancer and undergoing radiotherapy (IMRT-SIB) at the University Hospital of Udine.Different outcomes were considered: late toxicities, relapse, death.Death in the absence of relapse or late toxicity was considered as a competing event.
Results: The Kaplan Meier method, compared to cumulative incidence function method, overestimated the probability of the event of interest (toxicity and biochemical relapse) and of the competing event (death without toxicity/relapse) by 9.36%. The log-rank test, compared to Gray's test, overestimated the probability of the event of interest by 5.26%.The Hazard Ratio's and cause specific hazard's Cox regression are not directly comparable to subdistribution hazard's Fine and Gray's modified Cox regression; nonetheless, the FG model, the best choice for prognostic studies with competing risks, found significant associations not emerging with Cox regression.
Conclusions: This study confirms that using inappropriate statistical methods produces a 10% overestimation in results, as described in the literature, and highlights the importance of taking into account the competing risks bias.
Keywords: Competing risks; Cumulative incidence function; Fine and Gray; Subdistribution hazard; Survival analysis.