open access

Vol 24, No 6 (2019)
Original research articles
Published online: 2019-11-01
Submitted: 2018-10-30
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Why we should take care of the competing risk bias in survival analysis: A phase II trial on the toxicity profile of radiotherapy for prostate cancer

Annarita Tullio, Alessandro Magli, Eugenia Moretti, Francesca Valent
DOI: 10.1016/j.rpor.2019.08.001
·
Rep Pract Oncol Radiother 2019;24(6):511-519.

open access

Vol 24, No 6 (2019)
Original research articles
Published online: 2019-11-01
Submitted: 2018-10-30

Abstract

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.

Abstract

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.

Get Citation

Keywords

Competing risks; Cumulative incidence function; Fine and Gray; Subdistribution hazard; Survival analysis

About this article
Title

Why we should take care of the competing risk bias in survival analysis: A phase II trial on the toxicity profile of radiotherapy for prostate cancer

Journal

Reports of Practical Oncology and Radiotherapy

Issue

Vol 24, No 6 (2019)

Pages

511-519

Published online

2019-11-01

DOI

10.1016/j.rpor.2019.08.001

Bibliographic record

Rep Pract Oncol Radiother 2019;24(6):511-519.

Keywords

Competing risks
Cumulative incidence function
Fine and Gray
Subdistribution hazard
Survival analysis

Authors

Annarita Tullio
Alessandro Magli
Eugenia Moretti
Francesca Valent

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