title: [Logistic regression against a divergent Bayesian network].
abstract: This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered); we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.
authors: Universidad de Antioquia, Medellín, Colombia. Address: Carrera 98 N° 44-33, Apartamento 201, Medellín, Colombia. Email: asan@une.net.co.
authors: Noel Antonio
authors: NA
authors: Sánchez Trujillo
mesh_terms: D001499:Bayes Theorem; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D003657:Decision Making; D006801:Humans; D016015:Logistic Models; D011655:Pulmonary Embolism / Q000209:etiology*; D012880:Skin Pigmentation; D012907:Smoking / Q000009:adverse effects
keywords: Bayes theorem; Bayesian prediction; causality; confounding variables; decision making; diagnostic tests; epidemiologic effect modifiers; logistic regression