Models to Predict Unresolved Post-traumatic Stress Symptoms in Earthquake Survivors: Comparison of Classification and Regression Tree and Logistic Regression

Abstract

Objective: This study was to evaluate the implementation and performance of a classification and regression tree (CART) and a logistic regression model to predict unresolved post-traumatic stress symptoms (PTSS) in survivors three years after the Taiwan Chi-Chi Earthquake from multivariate data presented at 0.5 year. Methods: We surveyed 4,223 respondents 0.5 year after the earthquake, and 875 (20.7%) of them were found to be positive for PTSS. Three years later, we followed up 418 (47.8%) of the 875 participants, and in 38 (9.1%) of these cases were found to have their symptoms unresolved. Verified values falling outside threshold limits were analyzed according to demographic data, quality of life (QOL), putative risk factors, and post-traumatic stress disorder (PTSD)-related symptoms with the aid of logistic regression. A decision tree was automatically produced from the root nod to target classes (remissive or unresolved PTSS). Result: With CART, we found that the predicted probability for unresolved PTSS was 53.6%, if the respondents had ”prominent financial loss,” ”mental component summary (MCS) score ≦36.0”, and ”reliving the traumatic experience”. These three factors were also included in the six significant independent variables identified in logistic regression. Conclusion: Decision tree analysis confirmed some of the results of logistic regression. This investigation shows there is knowledge to be gained from analyzing observational data with the aid of decision tree analysis.