Attempt to Raise the Predictive Accuracy in Binary Logistic Regression Analysis

Abstract

Objective: It is necessary to improve the predictive accuracy of binary logistic regression analysis. This study aimed to clarify whether binary logistic regression analysis using Functional Independence Measure (FIM) gain (a 0/1 binary value) as a dependent variable increases the predictive accuracy when FIM at admission (FIMa) is categorized or when multiple predictive formulae are created. Methods: The study population consisted of 2,542 stroke patients admitted to convalescent rehabilitation wards in Japan. We compared the predictive accuracy of FIM gain between a formula using FIMa as quantitative data (A), a formula that categorized FIMa into 4 groups (B), and two predictive formulae (C). Result: The predictive accuracy of these formulae, in descending order, was found to be C (76.3%), B (76.0%), and A (68. 4%). Conclusion: Even more than using FIMa as quantitative data, the predictive accuracy of FIM gain was heightened by either categorizing FIMa into 4 groups or by creating two predictive formulae.