Racioppi M, Salmaso L, Brombin C, Arboretti R, D'Agostino D, Colombo R, Serretta V, Brausi M, Casetta G, Gontero P, Hurle R, Tenaglia R, Altieri V, Bartoletti R, Maffezzini M, Siracusano S, Morgia G, Bassi PF. The clinical use of statistical permutation test methodology: a tool for identifying predictive variables of outcome.
Urol Int 2014;
94:262-9. [PMID:
25171377 DOI:
10.1159/000365292]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Accepted: 06/16/2014] [Indexed: 11/19/2022]
Abstract
OBJECTIVES
To identify the predictive variables affecting the outcome after radical surgery for bladder cancer by a newer statistical methodology, i.e. nonparametric combination (NPC).
METHODS
A multicenter study enrolled 1,312 patients who had undergone radical cystectomy for bladder cancer in 11 Italian oncological centers from January 1982 to December 2002. A statistical analysis of their medical history and diagnostic, pathological and postoperative variables was performed using a NPC test. The patients were included in a comprehensive database with medical history and clinical and pathological data. Five-year survival was used as the dependent variable, and p values were corrected for multiplicity using a closed testing procedure. The newer nonparametric approach was used to evaluate the prognostic importance of the variables. All of the analyses were performed using routines developed in MATLAB© and the significance level was set at α = 0.05.
RESULTS
A significant prognostic predictive value (p < 0.01) for tumor clinical staging, hydronephrosis, tumor pathological staging, grading, presence of concomitant carcinoma in situ, regional lymph node involvement, corpora cavernosa invasion, microvascular invasion, lymphatic invasion and prostatic stroma involvement was found.
CONCLUSIONS
The NPC test could handle any type of variable (categorical and quantitative) and take into account the multivariate relation among variables. This newer methodology offers a significant contribution in biomedical studies with several endpoints and is recommended in presence of non-normal data and missing values, as well as solving high-dimensional data and problems relating to small sample sizes.
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