Past Issues
Models to Predict Unresolved Post-traumatic
Stress Symptoms in Earthquake Survivors:
Comparison of Classifi cation and Regression
Tree and Logistic Regression
Kuan-Yi Tsai, M.D., M.S.1,Chao-Yueh Su, MEdu2, Frank Huang-Chih Chou, M.D., M.S., Ph.D.1,3, Kuan-Chia Lin, Ph.D.4, Wen-Kuo Lin, M.D1, Ren-Yi Liu, M.D.1, Wei-Jen Chen, M.D., M.S. 5, Wen-Wei Ho, M.D.1, Shih-Pei Shen, B.S. 5
Objective: This study was to evaluate the implementation and performance of
a classifi cation 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. Verifi ed 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 fi nancial loss,” “mental component summary (MCS) score
≦36.0”, and “reliving the traumatic experience”. These three factors were also included
in the six signifi cant independent variables identifi ed in logistic regression.
Conclusion: Decision tree analysis confi rmed 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.
a classifi cation 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. Verifi ed 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 fi nancial loss,” “mental component summary (MCS) score
≦36.0”, and “reliving the traumatic experience”. These three factors were also included
in the six signifi cant independent variables identifi ed in logistic regression.
Conclusion: Decision tree analysis confi rmed 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.
| Key Word | Post-traumatic stress symptoms (PTSS), classifi cation and regression tree (CART), decision tree analysis |
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