Objectives-To determine any long-term effects, 6 and 8 years after childhood enrollment, of the randomly assigned 14-month treatments in the Multimodal Treatment Study of Children with ADHD (MTA; N=436); to test whether Attention-Deficit/Hyperactivity Disorder (ADHD) symptom trajectory through 3-years predicts outcome in subsequent years; to examine functioning level of the MTA adolescents relative to their non-ADHD peers (Local Normative Comparison Group or LNCG; N=261).Method-Mixed effects regression models with planned contrasts at 6-and 8-years tested a wide range of symptom and impairment variables assessed by parent, teacher, and youth report.Results-In nearly every analysis, the originally randomized treatment groups did not differ significantly on repeated measures or newly-analyzed variables (e.g., grades earned in school, The other authors report no conflicts of interest. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Conclusions-Type or intensity of 14 months of treatment for ADHD in childhood (at age 7.0-9.9 years old) does not predict functioning six-to-eight years later. Rather, early ADHD symptom trajectory regardless of treatment type is prognostic. This finding implies that children with behavioral and sociodemographic advantage, with the best response to any treatment, will have the best longterm prognosis. As a group, however, despite initial symptom improvement during treatment that is largely maintained post-treatment, children with Combined-Type ADHD exhibit significant impairment in adolescence. Innovative treatment approaches targeting specific areas of adolescent impairment are needed. NIH Public Access Keywords ADHD; adolescence; clinical trial; longitudinalThe Multimodal Treatment Study of Children with Attention-Deficit/Hyperactivity Disorder (ADHD), abbreviated as MTA, compared four distinct treatment strategies during childhood for 579 children diagnosed with DSM-IV ADHD, Combined type. Children were randomly assigned to 14 months of (a) systematic medication management (MedMgt), which was initial placebo-controlled titration, thrice-daily dosing, seven days per week, and monthly 30-minute clinic visits, (b) multicomponent behavior therapy (Beh), which included 27-session group parent training supplemented with eight individual parent sessions, an 8-week summer treatment program, 12 weeks of classroom administered behavior therapy with a half-time aide and 10 teacher consultation sessions, (c) their combination (Comb), or (d) usual community care (CC).1 -2 This randomized, 6-site, controlled clinical trial, conducted in parallel at 6 performance sites, feat...

Random-effects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the random-effects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of random-effects pattern-mixture models to further handle and describe the influence of missing data in longitudinal studies. For this approach, subjects are first divided into groups depending on their missing-data pattern and then variables based on these groups are used as model covariates. Tn this way, researchers are able to examine the effect of missing-data patterns on the outcome (or outcomes) of interest. Furthermore, overall estimates can be obtained by averaging over the missing-data patterns. A psychiatric clinical trials data set is used to illustrate the random-effects pattern-mixture approach to longitudinal data analysis with missing data.Longitudinal studies occupy an important role in psychological and psychiatric research. In these stud-

The modular approach outperformed usual care and standard evidence-based treatments on multiple clinical outcome measures. The modular approach may be a promising way to build on the strengths of evidence-based treatments, improving their utility and effectiveness with referred youths in clinical practice settings. Trial Registration  clinicaltrials.gov Identifier: NCT01178554.

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In this paper we provide theoretical and empirical analyses of an asymmetric-information model of layoffs in which the current employer is better informed about its workers' abilities than prospective employers are. The key feature of the model is that when firms have discretion with respect to whom to lay off, the market infers that laid-off workers are of low ability. Since no such negative inference should be attached o workers displaced in a plant closing, our model predicts that the postdisplacement wages of otherwise observationally equivalent workers will be higher for those displaced by plant closings than for those displaced by layoffs. An extension of our model predicts that the average postdisplacement unemployment spell of otherwise observationally equivalent workers will be shorter for those displaced by plant closings than for those displaced by layoffs. In our empirical work, we use data from the Displaced Workers Supplements in the January 1984 and 1986 Current Population Surveys. We find that the evidence (with respect to both re-employment wages and postdisplacement unemployment duration) is consistent with the idea that laidoff workers are viewed less favorably by the market than are those losing jobs in plant closings. Our findings are much stronger for workers laidoff from jobs where employers have discretion over whom to lay off.

A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data. This model is developed for both the probit and logistic response functions. The threshold concept is used, in which it is assumed that the observed ordered category is determined by the value of a latent unobservable continuous response that follows a linear regression model incorporating random effects. A maximum marginal likelihood (MML) solution is described using Gauss-Hermite quadrature to numerically integrate over the distribution of random effects. An analysis of a dataset where students are clustered or nested within classrooms is used to illustrate features of random-effects analysis of clustered ordinal data, while an analysis of a longitudinal dataset where psychiatric patients are repeatedly rated as to their severity is used to illustrate features of the random-effects approach for longitudinal ordinal data.

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