Application of group smoothly clipped absolute deviation method in identifying correlates of psychiatric distress among college students - Arayeshgari, Mahya (BMC Psychiatry, 2020)
BACKGROUND: College students are at an increased risk of psychiatric distress. So, identifying its important correlates using more reliable statistical models, instead of inefficient traditional variable selection methods like stepwise regression, is of great importance. The objective of this study was to investigate correlates of psychiatric distress among college students in Iran; using group smoothly clipped absolute deviation method (SCAD). METHODS: A number of 1259 voluntary college students participated in this cross-sectional study (Jan-May 2016) at Hamadan University of Medical Sciences, Iran. The data were collected using a self-administered questionnaire consisting of demographic information, a behavioral risk factors checklist and the GHQ-28 questionnaire (with a cut-off of 23 to measure psychiatric distress, recommended by the Iranian version of the questionnaire). Penalized logistic regression with a group-SCAD regularization method was used to analyze the data (α = 0.05). RESULTS: The majority of students were aged 18–25 (87.61%), and 60.76% of them were female. About 41% of students had psychiatric distress. Significant correlates of psychiatric distress among college students selected by group-SCAD included the average grade, educational level, being optimistic about future, having a boy/girlfriend, having an emotional breakup, the average daily number of cigarettes, substance abusing during previous month and having suicidal thoughts ever (P < 0.05). CONCLUSIONS: Penalized logistic regression methods such as group-SCAD and group-Adaptive-LASSO should be considered as plausible alternatives to stepwise regression for identifying correlates of a binary response. Several behavioral variables were associated with psychological distress which highlights the necessity of designing multiple factors and behavioral changes in interventional programs.