International Biometric Society
How does lymph nodes removal affect cancer patients survival: a statistical approach to
prostate cancer setting.
Chiara Gigliarano2, Alessandro Nonis1, Marco Bonetti3 and Clelia Di Serio1.
1University
Centre of Statistics for Biomedical Sciences (CUSSB), Vita-Salute San Raffaele University,
Milan, Italy
2Dipartimento di Scienze Economiche e Sociali, Università Politecnica delle Marche
3Dipartimento di Analisi delle Politiche e Management Pubblico, Università Bocconi di Milano.
In cancer studies it is still debatable how removing and analyzing a sufficient number of lymph nodes
at the time of intervention plays an important role both from a prognostic and a diagnostic point of
view. Many factors may influence the number of lymph nodes examined, including extent of surgical
resection, patient age, tumor location, and pathology techniques. More extensive nodal resection has
been associated with lower rates of cancer recurrence; allows for more accurate cancer staging and
thus, more appropriate use of adjuvant chemotherapy for node-positive patients; and has been
associated with improved survival following resection for colon and rectal cancers. However since
node negative patients treated with inadequate extent of nodal dissection may harbor a non- negligible
risk of residual or recurrent nodal disease after radical prostatectomy, it is fundamental to understand
how the probability of missing a positive node affects cancer patients survival is a fundamental
feature. In prostate cancer it has been seen this probability estimated by means of a beta-binomial
distribution, decreases with an increasing number of nodes examined.
The main aim of this contribution is to understand whether removing more nodes during prostate
surgery on may improve patient survival through better treatment assignment optimizing use and
extent of post-operative treatments based on the risk of nodal status misclassification. To do this, first
we analyze stochastic dominance between marginal survival curves testing differences in the
heterogeneity of survival distributions by means of Gini concentration coefficient for evaluating the
degree of inequality in the distribution of the time to an event of interest within a given population.
Results from this preliminary analysis may suggest the presence of a differential treatment effect for
some groups of patients, thus understanding whether treatment patients allocation was attributable to
some latent classification. Then we estimate the conditional survival function, given treatment
assignment, pre-operative covariates, and number of positive lymph nodes, that is S(t| X; TR ;N+). We
aim at building a statistical tool for better assignment of prostate cancer to therapies based on
comparison between marginal and conditional survival . A related goal is also the determination of the
optimal number of lymph nodes (if it exists), and of the prognostic value of the observed proportion of
positive nodes among the ones that are examined. Data from patients with localized prostate cancer
treated with radical prostatectomy and pelvic lymph node dissection (PLND; n=7135) between 20002011was analyzed.
International Biometric Conference, Florence, ITALY, 6 – 11 July 2014