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Advancing Antisocial Behavior Research with Longitudinal Data

Explore substantive applications of longitudinal data across multiple life stages, focusing on developmental antecedents and malleability of antisocial personality disorder. Two papers delve into specific themes such as the impact of preventive interventions and predicting developments in aggression leading to violent juvenile arrests.

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Advancing Antisocial Behavior Research with Longitudinal Data

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  1. Longitudinal Data Across Multiple Stages Of Life Substantive Applications Hanno Petras, Ph.D. Johns Hopkins University

  2. Collaborators • Hendricks Brown, USF • Howard Chilcoat, JHU • Nick Ialongo, JHU • Shep Kellam, AIR & JHU • Phil Leaf, JHU • Bengt Muthen, UCLA • Prevention Science Methodology Workgroup

  3. Paper 1: Developmental Antecedents and Malleability of Antisocial Personality Disorder - Long-term Effects of a Universal Classroom Based Preventive Intervention

  4. Developmental Relationship between Aggressive Behavior and Antisocial Personality Disorder (ASPD) in One Cohort of Control Boys (N=138) BIC=2516.86 Entropy=0.861 # of Iterations=93 Elementary School Middle School Prevalence of ASPD 69.7% 35.0% F=Fall S=Spring 10.7%

  5. Methodological Topics in Growth Modeling • Impact of particular time points (e.g., in middle school) for the prediction of distal outcome, over and above class membership (u on Ys) • Individual intervention impact over and above class membership • Atypical individual development --Outliers • Modeling of Subgroup variation within Growth Modeling (e.g., in control conditions, cohorts)

  6. Paper 2: Specificity/Sensitivity of Predicting Developments in Aggression Leading to Violent Juvenile Arrest

  7. Developmental Relationship between Aggressive Behavior and Violent Juvenile Arrest in Two Cohorts of Control Boys (N=598) BIC=7343.99 Entropy=0.789 # of Iterations=149 Sensitivity Prevalence of Arrest 34.4% 30.0% 5.4% F=Fall S=Spring

  8. Methodological Topics in Predicting Developmental Trajectories • Sensitivity (false positives) versus Specificity (false negatives) • Treatment of Missing Data • Not only prediction of development, but also quality of prediction regarding distal outcomes

  9. Time-to-Event Analysis • Interval Censoring, in addition to left and right censoring (i.e., Missing Data problem) • Analysis of Survival leading to Growth development (e.g. timing of school suspension may lead to different trajectories in aggression) • Bivariate Survival analysis (Time to first drug use and time to first depressive episode in one model)

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