Methods for Improving the Predictive Accuracy Using Multiple Linear Regression Analysis to Predict the Improvement Degree of Functional Independence Measure for Stroke Patients

Abstract

Multiple linear regression analysis is frequently used in studies investigating the degree of Functional Independence Measure (FIM) improvement in stroke patients. However, the coefficient of determination R 2 is about 0.46 to 0.73, meaning that the prediction accuracy is not necessarily high. In order to improve the prediction accuracy, the following methods are used; using appropriate explanatory variables, using FIM effectiveness which corrected the ceiling effect as the objective variable, creating multiple prediction formulas, converting numerical variable of explanatory variables into dummy variable, adding FIM improvement for one month to the explanatory variables. Even so, it is difficult to predict patients whose FIM gain is extremely large or small. It is desirable to combine these methods or develop new methods to achieve the accurate prediction.

The Threshold for Favorable Outcome should be Set According to Functional Independence Measure Score at Admission in Logistic Regression Analysis

“…Which method, multiple regression analysis or logistic regression analysis, is more appropriate for these studies is not known. The predictive accuracy of multiple regression analysis is not satisfactory [3]. Logistic regression analysis has the advantage of not requiring much rigor in the type or distribution of data, but has the disadvantage of losing a lot of information in the process of converting quantitative data of FIM into binary data of 0 and 1.…”

“…It is also used to find out how much influence the factor (explanatory variable) has on the outcome (objective variable). There are many reports of multiple regression analysis predicting Functional Independence Measure (FIM) scores at discharge or FIM gain (FIM score at discharge -FIM score at admission) [1][2][3]. But since multiple regression analysis is a parametric method, both the objective variable and explanatory variables are required for normal distribution.…”

Comparison of the accuracy of multiple regression analysis using four methods to predict the functional independence measure at discharge

“…3) Multiple regression analysis is a method of adding the effects of factors. 4) Factors other than those used for explanatory variables affect FIM at discharge [5].…”