Introduction:

Implicative statistical analysis is a method based on multivariate statistical techniques, quasi-implication theory, artificial intelligence, and Boolean algebra. It is used to model interrelationships between subjects and variables that allow the structuring of knowledge in the form of generalized norms and rules.

Objective:

To characterize implicative statistical analysis as a tool for the processing of statistical information in health sciences.

Methods:

A search of bibliographic sources was carried out to characterize the method, and its use in prognostic factors and profiles of visual functional organization in pathologies that can be extrapolated to different sample sizes.

Development:

Implicative statistical analysis organizes information, favors the appropriate statistical treatment in the analysis of the data, and allows the results to be graphed. Likewise, the rules obtained lead to hypotheses of causality without restricting the number of variables and the size of the sample. Its use has contributed to studies of prognostic factors in pathologies such as cancer and profiles in visual processing in dyslexics.

Conclusions:

Implicative statistical analysis creates hypotheses of causality through methodological rules of relationship between study variables. In addition, it makes it possible to structure, analyze and understand links between subjects and variables of health research.

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