Introduction:

implicit statistical analysis (ASI) is a data mining technique, to model the quasi-implication between events and variables of a data set.

Objective:

to evaluate the utility of ASI in the identification of prognostic factors in evolution of renal cancer.

Method:

a case-control study was carried out to identify the prognostic factors that influence the evolution of renal cancer in patients treated at the Clinical Surgical Teaching Hospital "Hermanos Ameijeiras" in Havana, January 2006 to January 2016. This technique was applied together with the binary logistic regression, which was considered as a gold standard.

Results:

the binary logistic regression identified four prognostic factors, while the implicative statistical analysis identified nine.

Conclusions:

the implicative statistical analysis proved to be an appropriate technique, which complements the logistic regression in the identification of prognostic factors, allowing a more complete interpretation of the phenomenon of causality.

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