Model selection in linear mixed effect models

AbstractMixed effect models are fundamental tools for the analysis of longitudinal data, panel data and cross-sectional data. They are widely used by various fields of social sciences, medical and biological sciences. However, the complex nature of these models has made variable selection and parameter estimation a challenging problem. In this paper, we propose a simple iterative procedure that estimates and selects fixed and random effects for linear mixed models. In particular, we propose to utilize the partial consistency property of the random effect coefficients and select groups of random effects simultaneously via a data-oriented penalty function (the smoothly clipped absolute deviation penalty function). We show that the proposed method is a consistent variable selection procedure and possesses some oracle properties. Simulation studies and a real data analysis are also conducted to empirically examine the performance of this procedure

A Retrospective Study of Substance Use and Mental Health Disorders in a Sample of Urban American Indian and Alaska Natives

This retrospective study examined the prevalence of mental health disorders, co-occurring disorders (COD), and alcohol, tobacco, and other drug use (ATOD) among a sample of urban-dwelling adult American Indian and Alaska Natives (AI/ANs) seeking behavioral health services from one metropolitan Indian clinic in Southwestern United States. A descriptive quantitative design employed retrospective data from AI/AN subjects (N = 123) verified as tribally enrolled and receiving outpatient behavioral health services. Chart abstraction included patient demographics, substance use and mental health diagnoses, and ATOD scores from the Patient Health Questionnaire-9 (PHQ-9) and the Addiction Severity Index-NAV (ASI-NAV). The t-test compared gender differences and age at first use of commonly abused substances. Chi-Square (x²) determined proportional differences among gender, mental health, ATOD, and COD. Logistic regression examined contributory factors increasing the likelihood of a mental health or substance use disorder (SUD). This urban adult AI/AN sample was evenly distributed by gender (64 males, 59 females), with a mean age of 38.94 years (SD = 11.01). Prevalence rate for current smokers was 44%, similar to nationwide data. Findings included diagnoses of mental health disorders (79%), substance abuse disorders (76%), and co-occurring disorders (55%). For those subjects who completed the PHQ-9 (n = 46), the prevalence rate for depression was 61%; the prevalence of depression among the 122 subjects with ICD-9 depression codes was 65%. ASI-NAV composite scores (CS) in subject charts (n = 43) demonstrated positive, statistically significant correlations between the psychosocial CS of alcohol use, legal (r=.35), family (r=.37), and psychiatry (r=.32); drug use, legal (r=.32), and family (r=.36). Legal also positively correlated with medical (r=.38), and family with psychiatry (r=.38). Logistic regression identified one predictor as statistically reliable in mental health (housing) and two predictors in distinguishing status of substance abuse (unemployment and education). Subjects with higher levels of education were less likely to have a diagnosed SUD. Health care providers rely on accurate data. Discerning the prevalence of mental health and substance use disorders when treating a growing native population ensures that culturally appropriate treatments are focused on the reduction of health disparities