Background: The aim of this article is to analyze the effect on biochemical recurrence and on overall survival of removing an extensive number of pelvic lymph nodes during prostate cancer surgery. The lack of evidence from randomized clinical trials to address this specific question has hampered the ability to determine the true effect of the number of nodes removed.Results: Our analysis is based on a large observational study, and this can lead unadjusted estimates to be very sensitive to confounding bias due to the different prognosis of individuals. We assess the effect of the number of lymph nodes removed by means of an Inverse Probability Weighting adjustment based on a Poisson regression model, and by a Doubly-robust adjustment.Conclusions: Our findings suggest that a large number of nodes removed is associated with a significant improvement in time to biochemical recurrence. However, it appears to have no impact on overall survival.

Effect of the number of removed lymph nodes on prostate cancer recurrence and survival: evidence from an observational study

Gigliarano, Chiara
;
2018-01-01

Abstract

Background: The aim of this article is to analyze the effect on biochemical recurrence and on overall survival of removing an extensive number of pelvic lymph nodes during prostate cancer surgery. The lack of evidence from randomized clinical trials to address this specific question has hampered the ability to determine the true effect of the number of nodes removed.Results: Our analysis is based on a large observational study, and this can lead unadjusted estimates to be very sensitive to confounding bias due to the different prognosis of individuals. We assess the effect of the number of lymph nodes removed by means of an Inverse Probability Weighting adjustment based on a Poisson regression model, and by a Doubly-robust adjustment.Conclusions: Our findings suggest that a large number of nodes removed is associated with a significant improvement in time to biochemical recurrence. However, it appears to have no impact on overall survival.
2018
http://www.biomedcentral.com/bmcbioinformatics/
Doubly-robust estimation; Prostate cancer; Retrospective study; Survival analysis; Structural Biology; Biochemistry; Molecular Biology; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics
Gigliarano, Chiara; Nonis, Alessandro; Briganti, Alberto; Bonetti, Marco; Di Serio, Clelia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2073510
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