Subjective stress and any drinking during alcohol treatment: Disentangling within and between person autoregressive effects.

Alcohol use has been shown to increase stress, and there is some evidence that stress predicts subsequent alcohol use during treatment for alcohol use disorder (AUD), particularly among females who are more likely to report coping-motivated drinking. Gaining a better understanding of the processes by which stress and alcohol use are linked during treatment could potentially inform AUD treatment planning. The current study aimed to characterize the association between stress and drinking during the course of AUD treatment and whether there were sex differences in these associations. Secondary data analyses of the COMBINE study (N = 1375; 69% male, 76.3% non-Hispanic and white, average age of 44.4 years) were conducted to examine self-reported perceived stress and alcohol consumption across 16 weeks of treatment for AUD using a Bayesian random-intercept cross-lagged panel model. There was stronger evidence for any alcohol use predicting greater than typical stress in subsequent weeks and less strong evidence for stress increasing the subsequent probability of alcohol use, particularly among males. For females, greater stress predicted subsequent drinking earlier in the treatment period, and a lower probability of subsequent drinking in the last week of treatment. Interventions might specifically focus on targeting reductions in stress following drinking occasions.

Effects of non-steroidal anti-inflammatory drug treatments on cognitive decline vary by phase of pre-clinical Alzheimer disease: findings from the randomized controlled Alzheimer's Disease Anti-inflammatory Prevention Trial.

OBJECTIVE: We examined the effects of non-steroidal anti-inflammatory drugs on cognitive decline as a function of phase of pre-clinical Alzheimer disease. METHODS: Given recent findings that cognitive decline accelerates as clinical diagnosis is approached, we used rate of decline as a proxy for phase of pre-clinical Alzheimer disease. We fit growth mixture models of Modified Mini-Mental State (3MS) Examination trajectories with data from 2388 participants in the Alzheimer's Disease Anti-inflammatory Prevention Trial and included class-specific effects of naproxen and celecoxib. RESULTS: We identified three classes: "no decline", "slow decline", and "fast decline", and examined the effects of celecoxib and naproxen on linear slope and rate of change by class. Inclusion of quadratic terms improved fit of the model (-2 log likelihood difference: 369.23; p < 0.001) but resulted in reversal of effects over time. Over 4 years, participants in the slow-decline class on placebo typically lost 6.6 3MS points, whereas those on naproxen lost 3.1 points (p-value for difference: 0.19). Participants in the fast-decline class on placebo typically lost 11.2 points, but those on celecoxib first declined and then gained points (p-value for difference from placebo: 0.04), whereas those on naproxen showed a typical decline of 24.9 points (p-value for difference from placebo: <0.0001). CONCLUSIONS: Our results appeared statistically robust but provided some unexpected contrasts in effects of different treatments at different times. Naproxen may attenuate cognitive decline in slow decliners while accelerating decline in fast decliners. Celecoxib appeared to have similar effects at first but then attenuated change in fast decliners.

Partnerships for the design, conduct, and analysis of effectiveness, and implementation research: experiences of the prevention science and methodology group.

What progress prevention research has made comes through strategic partnerships with communities and institutions that host this research, as well as professional and practice networks that facilitate the diffusion of knowledge about prevention. We discuss partnership issues related to the design, analysis, and implementation of prevention research and especially how rigorous designs, including random assignment, get resolved through a partnership between community stakeholders, institutions, and researchers. These partnerships shape not only study design, but they determine the data that can be collected and how results and new methods are disseminated. We also examine a second type of partnership to improve the implementation of effective prevention programs into practice. We draw on social networks to studying partnership formation and function. The experience of the Prevention Science and Methodology Group, which itself is a networked partnership between scientists and methodologists, is highlighted.

Developmental epidemiological courses leading to antisocial personality disorder and violent and criminal behavior: effects by young adulthood of a universal preventive intervention in first- and second-grade classrooms.

BACKGROUND: Antisocial personality disorder (ASPD), violent and criminal behavior, and drug abuse disorders share the common antecedent of early aggressive, disruptive behavior. In the 1985-1986 school year teachers implemented the Good Behavior Game (GBG), a classroom behavior management strategy targeting aggressive, disruptive behavior and socializing children to the student role. From first through seventh grade the developmental trajectories of 2311 students from 19 Baltimore City Public Schools were examined. We report the GBG impact on these trajectories and ASPD and violent and criminal behavior by age 19-21. METHODS: In five urban, poor to lower middle class predominately African-American areas, three to four schools were matched and within each set randomly assigned to one of three conditions: (1) GBG, (2) a reading achievement program, or (3) the standard program. Classrooms and teachers were randomly assigned to intervention or control. Measures at 19-21 included self reports and juvenile court and adult incarceration records. GBG impact was assessed via General Growth Mixture Modeling based on repeated measures of aggressive, disruptive behavior. RESULTS: Three trajectories of aggressive, disruptive behavior were identified. By young adulthood, GBG significantly reduced the rates of ASPD and violent and criminal behavior among males in the persistent high aggressive, disruptive trajectory. REPLICATION: A replication was implemented with the following cohort of first-grade children using the same teachers, but with diminished mentoring and monitoring. Beneficial impact was found among persistent high males through seventh grade. By young adulthood GBG effects on ASPD and violent and criminal behavior were non-significant, but generally in the hypothesized direction.

Methods for testing theory and evaluating impact in randomized field trials: intent-to-treat analyses for integrating the perspectives of person, place, and time.

Randomized field trials provide unique opportunities to examine the effectiveness of an intervention in real world settings and to test and extend both theory of etiology and theory of intervention. These trials are designed not only to test for overall intervention impact but also to examine how impact varies as a function of individual level characteristics, context, and across time. Examination of such variation in impact requires analytical methods that take into account the trial's multiple nested structure and the evolving changes in outcomes over time. The models that we describe here merge multilevel modeling with growth modeling, allowing for variation in impact to be represented through discrete mixtures--growth mixture models--and nonparametric smooth functions--generalized additive mixed models. These methods are part of an emerging class of multilevel growth mixture models, and we illustrate these with models that examine overall impact and variation in impact. In this paper, we define intent-to-treat analyses in group-randomized multilevel field trials and discuss appropriate ways to identify, examine, and test for variation in impact without inflating the Type I error rate. We describe how to make causal inferences more robust to misspecification of covariates in such analyses and how to summarize and present these interactive intervention effects clearly. Practical strategies for reducing model complexity, checking model fit, and handling missing data are discussed using six randomized field trials to show how these methods may be used across trials randomized at different levels.

Cluster randomized trials with treatment noncompliance.

Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed.

DSM-IV alcohol dependence and abuse: further evidence of validity in the general population.

BACKGROUND: In order to understand the validity of the Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV) alcohol abuse and dependence diagnoses, studies are needed in both clinical and general population samples. The purpose of this study was to examine the construct and criterion-oriented validity of DSM-IV alcohol dependence and abuse in the general population with respect to factor structure and their relationship to family history of alcoholism, treatment utilization, and psychiatric comorbidity. METHODS: This analysis is based on data from the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), in which nationally representative data were collected in personal interviews conducted with one randomly selected adult in each sample household or group quarters. A subset (n=26,946) of the NESARC sample (total n=43,093) who reported drinking one or more drinks during the year preceding the interview formed the basis of analyses. Latent variable modeling was used to assess the concurrent validity of DSM-IV alcohol abuse and dependence symptom items. RESULTS: The latent variable modeling yielded one major factor related to alcohol dependence, a second factor related to alcohol abuse and a third smaller factor defined by tolerance. The validity of alcohol dependence in general population samples was further supported by statistically significant associations with family history of alcoholism, treatment utilization, and psychiatric and medical comorbidities. CONCLUSIONS: The factor structure and relationship to external criterion variables observed in the study provide support for the further validity of DSM-IV alcohol dependence in the general population, whereas support for the validity of DSM-IV abuse was equivocal.

Preventing disruptive behavior in elementary schoolchildren: impact of a universal classroom-based intervention.

A population-based, randomized universal classroom intervention trial for the prevention of disruptive behavior (i.e., attention-deficit/hyperactivity problems, oppositional defiant problems, and conduct problems) is described. Impact on developmental trajectories in young elementary schoolchildren was studied. Three trajectories were identified in children with high, intermediate, or low levels of problems on all 3 disruptive behaviors at baseline. The intervention had a positive impact on the development of all disruptive behavior problems in children with intermediate levels of these problems at baseline. Effect sizes of mean difference at outcome were medium or small. In children with the highest levels of disruptive behavior at baseline, a positive impact of the intervention was found for conduct problems.

Hierarchical modeling of sequential behavioral data: an empirical Bayesian approach.

The authors review the common methods for measuring strength of contingency between 2 behaviors in a behavioral sequence, the binomial z score and the adjusted cell residual, and point out a number of limitations of these approaches. They present a new approach using log odds ratios and empirical Bayes estimation in the context of hierarchical modeling, an approach not constrained by these limitations. A series of hierarchical models is presented to test the stationarity of behavioral sequences, the homogeneity of sequences across a sample of episodes, and whether covariates can account for variation in sequences across the sample. These models are applied to observational data taken from a study of the behavioral interactions of 254 couples to illustrate their use.

Alcohol-related aggression and drinking at off-campus parties and bars: a national study of current drinkers in college.

OBJECTIVE: This study examines relationships between alcohol-related aggression and drinking at off-campus parties and bars. Other background variables include gender, year in school, residence and heavy drinking in college. METHOD: The study participants were respondents in the 1997 and 1999 Harvard School of Public Health College Alcohol Study, national surveys of students attending 119 4-year U.S. colleges. Based on responses from 8,426 students, 18-24 years of age, who were never married and who reported attendance at off-campus parties and bars in the past month, an exploratory factor analysis of the alcohol problem items was specified in a confirmatory factor analysis framework based on a two-factor solution (disruptive behavior and victims of altercations). In addition, the two factors were related to study variables. RESULT: Among students attending both off-campus parties and bars, level of drinking was related to higher levels of Factor 1 (disruptive behaviors) at off-campus parties and bars. Level of drinking was related to higher levels of Factor 2 (victims of altercations) at off-campus bars but not off-campus parties. Factor 1 was higher among men, freshmen and residents in coed dorms and lower among students living off-campus with parents. Factor 2 was higher among women, freshmen compared with seniors and residents of Greek houses and lower among students living off-campus residence with parents. CONCLUSIONS: Findings from this study highlight environmental factors in collegiate drinking practices and support targeted and diverse strategies for prevention activities.

The impact of current residence and high school drinking on alcohol problems among college students.

OBJECTIVE: This study examines relationships between type of (current) residence, heavy episodic drinking in high school and alcohol-related problems among college students. METHOD: The study participants were respondents in the 1993, 1997 and 1999 Harvard School of Public Health College Alcohol Study (CAS) surveys of students attending 119 4-year U.S. colleges. Based on responses from 6,525 (55.6% female) students in the 1993 CAS, an exploratory factor analysis of the alcohol problem items was specified in a confirmatory factor analysis framework based on a four-factor solution, and related to study variables. The 1993 data were cross-validated with the 1997 and 1999 surveys. RESULTS: When compared with students living in single-gender dormitories, students living off campus with parents reported lower alcohol-related problem consequences and a higher probability of drinking/driving. Students residing off campus without parents, compared with students in single-gender dorms, reported a higher probability of drinking/driving. Associations between off-campus residence and probabilities for drinking/driving were mediated by frequency of driving. Students living in coed dormitories, when compared with students in single-gender dorms, incurred more problem consequences related to drinking but reported significantly lower probabilities associated with designated driving and drinking/driving. Heavy episodic drinking in high school was related to higher probabilities of problems on all outcome measures. CONCLUSIONS: The presence of direct and independent effects for both heavy drinking prior to college and high-risk environmental factors in collegiate drinking practices support targeted and diverse strategies for prevention activities.

Genome-wide association study in bipolar patients stratified by co-morbidity.

BACKGROUND: Bipolar disorder is a severe psychiatric disorder with high heritability. Co-morbid conditions are common and might define latent subgroups of patients that are more homogeneous with respect to genetic risk factors. METHODOLOGY: In the Caucasian GAIN bipolar disorder sample of 1000 cases and 1034 controls, we tested the association of single nucleotide polymorphisms with patient subgroups defined by co-morbidity. RESULTS: Bipolar disorder with psychosis and/or substance abuse in the absence of alcohol dependence was associated with the rare variant rs1039002 in the vicinity of the gene phosphodiesterase 10A (PDE10A) on chromosome 6q27 (p = 1.7×10⁻8). PDE10A has been implicated in the pathophysiology of psychosis. Antagonists to the encoded protein are currently in clinical testing. Another rare variant, rs12563333 (p = 5.9×10⁻8) on chromosome 1q41 close to the MAP/microtubule affinity-regulating kinase 1 (MARK1) gene, approached the genome-wide level of significance in this subgroup. Homozygotes for the minor allele were present in cases and absent in controls. Bipolar disorder with alcohol dependence and other co-morbidities was associated with SNP rs2727943 (p = 3.3×10⁻8) on chromosome 3p26.3 located between the genes contactin-4 precursor (BIG-2) and contactin 6 (CNTN6). All three associations were found under the recessive genetic model. Bipolar disorder with low probability of co-morbid conditions did not show significant associations. CONCLUSION: Conceptualizing bipolar disorder as a heterogeneous disorder with regard to co-morbid conditions might facilitate the identification of genetic risk alleles. Rare variants might contribute to the susceptibility to bipolar disorder.

Antidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder.

Individuals with Major Depressive Disorder (MDD) vary regarding the rate, magnitude and stability of symptom changes during antidepressant treatment. Growth mixture modeling (GMM) can be used to identify patterns of change in symptom severity over time. Quantitative electroencephalographic (QEEG) cordance within the first week of treatment has been associated with endpoint clinical outcomes but has not been examined in relation to patterns of symptom change. Ninety-four adults with MDD were randomized to eight weeks of double-blinded treatment with fluoxetine 20mg or venlafaxine 150mg (n=49) or placebo (n=45). An exploratory random effect GMM was applied to Hamilton Depression Rating Scale (Ham-D(17)) scores over 11 timepoints. Linear mixed models examined 48-h, and 1-week changes in QEEG midline-and-right-frontal (MRF) cordance for subjects in the GMM trajectory classes. Among medication subjects an estimated 62% of subjects were classified as responders, 21% as non-responders, and 17% as symptomatically volatile-i.e., showing a course of alternating improvement and worsening. MRF cordance showed a significant class-by-time interaction (F((2,41))=6.82, p=.003); as hypothesized, the responders showed a significantly greater 1-week decrease in cordance as compared to non-responders (mean difference=-.76, Std. Error=.34, df=73, p=.03) but not volatile subjects. Subjects with a volatile course of symptom change may merit special clinical consideration and, from a research perspective, may confound the interpretation of typical binary endpoint outcomes. Statistical methods such as GMM are needed to identify clinically relevant symptom response trajectories.

Two-Part Factor Mixture Modeling: Application to an Aggressive Behavior Measurement Instrument.

This study introduces a two-part factor mixture model as an alternative analysis approach to modeling data where strong floor effects and unobserved population heterogeneity exist in the measured items. As the names suggests, a two-part factor mixture model combines a two-part model, which addresses the problem of strong floor effects by decomposing the data into dichotomous and continuous response components, with a factor mixture model, which explores unobserved heterogeneity in a population by establishing latent classes. Two-part factor mixture modeling can be an important tool for situations in which ordinary factor analysis produces distorted results and can allow researchers to better understand population heterogeneity within groups. Building a two-part factor mixture model involves a consecutive model building strategy that explores latent classes in the data for each part as well as a combination of the two-part. This model building strategy was applied to data from a randomized preventive intervention trial in Baltimore public schools administered by the Johns Hopkins Center for Early Intervention. The proposed model revealed otherwise unobserved subpopulations among the children in the study in terms of both their tendency toward and their level of aggression. Furthermore, the modeling approach was examined using a Monte Carlo simulation.

Growth mixture modelling in families of the Framingham Heart Study.

Growth mixture modelling, a less explored method in genetic research, addresses unobserved heterogeneity in population samples. We applied this technique to longitudinal data of the Framingham Heart Study. We examined systolic blood pressure (BP) measures in 1060 males from 692 families and detected three subclasses, which varied significantly in their developmental trajectories over time. The first class consisted of 60 high-risk individuals with elevated BP early in life and a steep increase over time. The second group of 131 individuals displayed first normal BP, but showed a significant increase over time and reached high BP values late in their life time. The largest group of 869 individuals could be considered a normative group with normal BP on all exams. To identify genetic modulators for this phenotype, we tested 2,340 single-nucleotide polymorphisms on chromosome 8 for association with the class membership probabilities of our model. The probability of being in Class 1 was significantly associated with a very rare variant (rs1445404) present in only four individuals from four different families located in the coding region of the gene EYA (eyes absent homolog 1 in Drosophila) (p = 1.39 x 10-13). Mutations in EYA are known to cause brachio-oto-renal syndrome, as well as isolated renal malformations. Renal malformations could cause high BP early in life. This result awaits replication; however, it suggests that analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multi-factorial diseases.

A multilevel linear mixed model of the association between candidate genes and weight and body mass index using the Framingham longitudinal family data.

Obesity has become an epidemic in many countries and is one of the major risk conditions for disease including type 2 diabetes, coronary heart disease, stroke, dyslipidemia, and hypertension. Recent genome-wide association studies have identified two genes (FTO and near MC4R) that were unequivocally associated with body mass index (BMI) and obesity. For the Genetic Analysis Workshop 16, data from the Framingham Heart Study were made available, including longitudinal anthropometric and metabolic traits for 7130 Caucasian individuals over three generations, each with follow-up data at up to four time points. We explored the associations between four single-nucleotide polymorphisms (SNPs) on FTO (rs1121980, rs9939609) or near MC4R (rs17782313, rs17700633) with weight and BMI under an additive model. We applied multilevel linear mixed model for continuous outcomes, using the Affymetrix 500k genome-wide genotype data for the four SNPs. The results of the multilevel modeling in the entire sample indicated that the minor alleles of the four SNPs were associated with higher weight and higher BMI. The most significant associations were between rs9939609 and weight (p = 4.7 x 10-6) and BMI (p = 8.9 x 10-8). The results also showed that, for SNPs at FTO, the homozygotes for the minor allele had the most pronounced increase in weight and BMI, while the common allele homozygotes gained less weight and BMI during the follow-up period. Linkage disequilibrium (LD) between the two FTO SNPs was strong (D' = 0.997, r2 = 0.875) but their haplotype was not significantly associated with either weight or BMI. The two SNPs near MC4R were in weak LD (D' = 0.487, r2 = 0.166).

Intention-to-treat analysis in cluster randomized trials with noncompliance.

In cluster randomized trials (CRTs), individuals belonging to the same cluster are very likely to resemble one another, not only in terms of outcomes but also in terms of treatment compliance behavior. Although the impact of resemblance in outcomes is well acknowledged, little attention has been given to the possible impact of resemblance in compliance behavior. This study defines compliance intraclass correlation as the level of resemblance in compliance behavior among individuals within clusters. On the basis of Monte Carlo simulations, it is demonstrated how compliance intraclass correlation affects power to detect intention-to-treat (ITT) effect in the CRT setting. As a way of improving power to detect ITT effect in CRTs accompanied by noncompliance, this study employs an estimation method, where ITT effect estimates are obtained based on compliance-type-specific treatment effect estimates. A multilevel mixture analysis using an ML-EM estimation method is used for this estimation.