Makoto Tokunaga, MD, PhD, Katsuhiko Sannomiya, PT
Jpn J Compr Rehabil Sci 16: 68-73, 2025
Objective: Factors affecting improvement in the
Functional Independence Measure (FIM) differ among
patient subgroups, and therefore several studies have
performed multiple regression analyses after stratifying
patients by relevant factors. However, the optimal
variables and criteria for stratification remain unclear.
Decision tree analysis can identify contributing variables
and branching criteria for stratification. This study aimed
to compare the predictive accuracy of multiple regression
analysis stratified based on decision tree analysis with
that of conventional multiple regression analysis.
Methods: We included 1,100 stroke patients admitted
to a convalescent rehabilitation ward. Based on the
decision tree results reported by Okamoto et al.,
patients were stratified into four groups according to
admission motor FIM scores: 13-18, 19-30, 31-53,
and 54-90. Stepwise multiple regression analysis was
then performed with discharge motor FIM as the
dependent variable. We compared the predictive
performance of a single regression equation derived
from conventional multiple regression with that of
four equations derived from stratified analyses by
calculating the residual sum of squares and comparing
the absolute residuals using the Wilcoxon signed-rank
test.
Results: The conventional single equation yielded a
median absolute residual of 7.5 points and a residual
sum of squares of 14.7*104. In contrast, the four
stratified equations yielded a median absolute residual
of 4.2 points and a residual sum of squares of 9.9*104,
and the absolute residuals were significantly smaller in
the stratified regression models.
Conclusion: Stratifying patients into four groups
based on decision tree analysis improved the predictive
accuracy of multiple regression analysis.
Key words: multiple regression analysis, decision tree analysis, stratification, predictive accuracy, Functional Independence Measure