A non-parametric Bayesian approach for adjusting partial compliance in sequential decision making.

Existing methods for estimating the mean outcome under a given sequential treatment rule often rely on intention-to-treat analyses, which estimate the effect of following a certain treatment rule regardless of compliance behavior of patients. There are two major concerns with intention-to-treat analyses: (1) the estimated effects are often biased toward the null effect; (2) the results are not generalizable and reproducible due to the potentially differential compliance behavior. These are particularly problematic in settings with a high level of non-compliance, such as substance use disorder studies. Our work is motivated by the Adaptive Treatment for Alcohol and Cocaine Dependence study (ENGAGE), which is a multi-stage trial that aimed to construct optimal treatment strategies to engage patients in therapy. Due to the relatively low level of compliance in this trial, intention-to-treat analyses essentially estimate the effect of being randomized to a certain treatment, instead of the actual effect of the treatment. We obviate this challenge by defining the target parameter as the mean outcome under a dynamic treatment regime conditional on a potential compliance stratum. We propose a flexible non-parametric Bayesian approach based on principal stratification, which consists of a Gaussian copula model for the joint distribution of the potential compliances, and a Dirichlet process mixture model for the treatment sequence specific outcomes. We conduct extensive simulation studies which highlight the utility of our approach in the context of multi-stage randomized trials. We show robustness of our estimator to non-linear and non-Gaussian settings as well.

Addicted to green: priming effect of menthol cigarette packaging on brain response to smoking cues.

INTRODUCTION: Mentholated tobacco cigarettes are believed to be more addictive than non-menthol ones. Packaging of most menthol cigarette brands includes distinctive green hues, which may act as conditioned stimuli (ie, cues) and promote menthol smoking. To examine the cue properties of menthol cigarette packaging, we used a priming paradigm to assess the effect of packaging on the neural substrates of smoking cue reactivity. We hypothesised that menthol packaging will exert a specific priming effect potentiating smoking cue reactivity in menthol compared with non-menthol smokers. METHODS: Forty-two menthol and 33 non-menthol smokers underwent functional MRI while viewing smoking and neutral cues. The cues were preceded (ie, primed) by briefly presented images of menthol or non-menthol cigarette packages. Participants reported craving for cigarettes in response to each cue. RESULTS: Menthol packaging induced greater frontostriatal and occipital smoking cue reactivity in menthol smokers than in non-menthol smokers. Menthol packaging also enhanced the mediation by neural activity of the relationship between cue exposure and cigarette craving in menthol but not non-menthol smokers. Dynamic causal modelling showed stronger frontostriatal-occipital connectivity in response to menthol packaging in menthol compared with non-menthol smokers. The effects of non-menthol packaging did not differ between categories of smokers. CONCLUSIONS: Our findings demonstrate heightened motivational and perceptual salience of the green-hued menthol cigarette packaging that may exacerbate menthol smokers' susceptibility to smoking cues. These effects could contribute to the greater addiction severity among menthol smokers and could be considered in the development of science-based regulation and legal review of tobacco product marketing practices.

Effects of current smoking severity on brain gray matter volume in opioid use disorder - a voxel-based morphometry study.

Background: Cigarette smoking (CS) and opioid use disorder (OUD) significantly alter brain structure. Although OUD and cigarette smoking are highly comorbid, most prior neuroimaging research in OUD did not control for smoking severity. Specifically, the combined effect of smoking and OUD on the brain gray matter volume (GMV) remains unknown.Objectives: We used structural magnetic resonance imaging (sMRI) to examine: (1) the GMV differences between OUD and non-OUD individuals with comparable smoking severity; and (2) the differential effect of smoking severity on the brain GMV between individuals with and without OUD.Methods: We performed a secondary analysis of existing sMRI datasets of 116 individuals who smoked cigarettes daily, among whom 60 had OUD (CS-OUD; 37 male, 23 female) and 56 did not (CS; 31 male, 25 female). Brain GMV was estimated by voxel-based morphometry analysis.Results: Compared to the CS group, the CS-OUD group had a higher GMV in the occipital cortex and lower GMV in the prefrontal and temporal cortex, striatum, and pre/postcentral gyrus (whole-brain corrected-p < .05). There was a significant interaction between group and smoking severity on GMV in the medial orbitofrontal cortex (whole-brain corrected-p < .05), such that heavier smoking was associated with lower medial orbitofrontal GMV in the CS-OUD but not CS participants (r=-0.32 vs. 0.12).Conclusions: Our findings suggest a combination of independent and interactive effects of cigarette smoking and OUD on the brain gray matter. Elucidating the neuroanatomical correlates of comorbid opioid and tobacco use may shed the light on the development of novel interventions for affected individuals.

A randomized, double-blind, placebo-controlled, pharmacogenetic study of ondansetron for treating alcohol use disorder.

BACKGROUND: In a previous study, ondansetron, a serotonin 5-HT3 receptor antagonist, reduced drinking intensity (drinks/drinking day [DPDD]) among European-ancestry (EA) participants with moderate-to-severe alcohol use disorder (AUD) and variants in genes encoding the serotonin transporter (SLC6A4) and 5-HT3A (HTR3A), and 5-HT3B (HTR3B) receptors. We tested whether (1) ondansetron reduces DPDD among individuals of either European or African ancestry (AA), and (2) that reductions in DPDD are greatest among ondansetron-treated individuals with population-specific combinations of genotypes at SLC6A4, HTR3A, and HTR3B. METHODS: In this 16-week, double-blind, placebo-controlled, parallel-group clinical trial, adults with AUD were randomized to receive low-dose oral ondansetron (0.33 mg twice daily) or placebo stratified by "responsive" versus "nonresponsive" genotype defined using population-specific genotypes at the three genetic loci. Generalized estimating equation regression models and a modified intent-to-treat analysis were used to compare the treatment groups on the primary outcome-DPDD-and two secondary outcomes-heavy drinking days per week [HDD] and drinks per day [DPD] across the 16 weeks of treatment. RESULTS: Of 296 prospective participants screened, 95 (58 EA and 37 AA) were randomized and received at least one dose of study medication. In the modified intent-to-treat analysis, the ondansetron group averaged 0.40 more DPDD (p = 0.51), 1.35 times as many HDD (p = 0.16), and 1.06 times as many DPD (p = 0.59) as the placebo group. There were no significant interactions with genotype. There were no study-related serious adverse events (AEs) and similar proportions of participants in the two treatment groups experienced AEs across organ systems. CONCLUSIONS: We found no evidence that low-dose oral ondansetron is beneficial in the treatment of AUD, irrespective of genotype, thus failing to confirm prior study findings. However, the study was underpowered to identify medication by genotype interactions.

Bayesian set of best dynamic treatment regimes: Construction and sample size calculation for SMARTs with binary outcomes.

Sequential, multiple assignment, randomized trials (SMARTs) compare sequences of treatment decision rules called dynamic treatment regimes (DTRs). In particular, the Adaptive Treatment for Alcohol and Cocaine Dependence (ENGAGE) SMART aimed to determine the best DTRs for patients with a substance use disorder. While many authors have focused on a single pairwise comparison, addressing the main goal involves comparisons of >2 DTRs. For complex comparisons, there is a paucity of methods for binary outcomes. We fill this gap by extending the multiple comparisons with the best (MCB) methodology to the Bayesian binary outcome setting. The set of best is constructed based on simultaneous credible intervals. A substantial challenge for power analysis is the correlation between outcome estimators for distinct DTRs embedded in SMARTs due to overlapping subjects. We address this using Robins' G-computation formula to take a weighted average of parameter draws obtained via simulation from the parameter posteriors. We use non-informative priors and work with the exact distribution of parameters avoiding unnecessary normality assumptions and specification of the correlation matrix of DTR outcome summary statistics. We conduct simulation studies for both the construction of a set of optimal DTRs using the Bayesian MCB procedure and the sample size calculation for two common SMART designs. We illustrate our method on the ENGAGE SMART. The R package SMARTbayesR for power calculations is freely available on the Comprehensive R Archive Network (CRAN) repository. An RShiny app is available at https://wilart.shinyapps.io/shinysmartbayesr/.

Effect of Pharmacogenomic Testing for Drug-Gene Interactions on Medication Selection and Remission of Symptoms in Major Depressive Disorder: The PRIME Care Randomized Clinical Trial.

Importance: Selecting effective antidepressants for the treatment of major depressive disorder (MDD) is an imprecise practice, with remission rates of about 30% at the initial treatment. Objective: To determine whether pharmacogenomic testing affects antidepressant medication selection and whether such testing leads to better clinical outcomes. Design, Setting, and Participants: A pragmatic, randomized clinical trial that compared treatment guided by pharmacogenomic testing vs usual care. Participants included 676 clinicians and 1944 patients. Participants were enrolled from 22 Department of Veterans Affairs medical centers from July 2017 through February 2021, with follow-up ending November 2021. Eligible patients were those with MDD who were initiating or switching treatment with a single antidepressant. Exclusion criteria included an active substance use disorder, mania, psychosis, or concurrent treatment with a specified list of medications. Interventions: Results from a commercial pharmacogenomic test were given to clinicians in the pharmacogenomic-guided group (n = 966). The comparison group received usual care and access to pharmacogenomic results after 24 weeks (n = 978). Main Outcomes and Measures: The co-primary outcomes were the proportion of prescriptions with a predicted drug-gene interaction written in the 30 days after randomization and remission of depressive symptoms as measured by the Patient Health Questionnaire-9 (PHQ-9) (remission was defined as PHQ-9 ≤ 5). Remission was analyzed as a repeated measure across 24 weeks by blinded raters. Results: Among 1944 patients who were randomized (mean age, 48 years; 491 women [25%]), 1541 (79%) completed the 24-week assessment. The estimated risks for receiving an antidepressant with none, moderate, and substantial drug-gene interactions for the pharmacogenomic-guided group were 59.3%, 30.0%, and 10.7% compared with 25.7%, 54.6%, and 19.7% in the usual care group. The pharmacogenomic-guided group was more likely to receive a medication with a lower potential drug-gene interaction for no drug-gene vs moderate/substantial interaction (odds ratio [OR], 4.32 [95% CI, 3.47 to 5.39]; P < .001) and no/moderate vs substantial interaction (OR, 2.08 [95% CI, 1.52 to 2.84]; P = .005) (P < .001 for overall comparison). Remission rates over 24 weeks were higher among patients whose care was guided by pharmacogenomic testing than those in usual care (OR, 1.28 [95% CI, 1.05 to 1.57]; P = .02; risk difference, 2.8% [95% CI, 0.6% to 5.1%]) but were not significantly higher at week 24 when 130 patients in the pharmacogenomic-guided group and 126 patients in the usual care group were in remission (estimated risk difference, 1.5% [95% CI, -2.4% to 5.3%]; P = .45). Conclusions and Relevance: Among patients with MDD, provision of pharmacogenomic testing for drug-gene interactions reduced prescription of medications with predicted drug-gene interactions compared with usual care. Provision of test results had small nonpersistent effects on symptom remission. Trial Registration: ClinicalTrials.gov Identifier: NCT03170362.

A Delta-Opioid Receptor Gene Polymorphism Moderates the Therapeutic Response to Extended-Release Buprenorphine in Opioid Use Disorder.

BACKGROUND: Buprenorphine treatment is not equally effective in all patients with opioid use disorder (OUD). Two retrospective studies showed that, among African Americans (AAs), rs678849, a polymorphism in the delta-opioid receptor gene, moderated the therapeutic effect of sublingual buprenorphine. METHODS: We examined rs678849 as a moderator of the response to an extended-release subcutaneous buprenorphine formulation (BUP-XR) in a 24-week OUD treatment study of 127 AAs and 327 European Americans (EAs). Participants were randomly assigned to receive: (1) BUP-XR as 2 monthly injections of 300 mg followed by either 300 mg monthly or 100 mg monthly for 4 months, or (2) monthly volume-matched placebo injections. Generalized estimating equations logistic regression analyses tested, per population group, the main and interaction effects of treatment (BUP-XR vs placebo) and genotype group (rs678849*CC vs CT/TT) on weekly urine drug screens (UDS). RESULTS: Among AAs, the placebo group had higher rates of opioid-positive UDS than the BUP-XR group (log odds ratio = 1.67, 95% CI = 0.36, 2.98), but no genotype by treatment effect (P = .80). Among EAs, the placebo group also showed higher rates of opioid-positive UDS than the BUP-XR group (log odds ratio = 1.97, 95% CI = 1.14, 2.79) but a significant genotype by treatment interaction (χ 2(1) = 4.33, P = .04). CONCLUSION: We found a moderating effect of rs678849 on the response to buprenorphine treatment of OUD in EAs, but not AAs. These findings require replication in well-powered, prospective studies of both AA and EA OUD patients treated with BUP-XR and stratified on rs678849 genotype.

Pregnant Smokers Receiving Opioid Agonist Therapy Have an Elevated Nicotine Metabolite Ratio: A Replication Study.

INTRODUCTION: Pregnant women exposed chronically to opioids smoked more cigarettes per day (CPD) and had a higher nicotine metabolite ratio (NMR), 3-hydroxycotinine/cotinine, a biomarker of nicotine metabolism and clearance, than those not receiving opioids. We examined CPD and NMR in a group of pregnant smokers, a quarter of whom were receiving opioid agonist therapy (OAT). AIMS AND METHODS: Pregnant smokers recruited to participate in a placebo-controlled trial of bupropion for smoking cessation provided a blood sample for measurement of NMR. RESULTS: Half (52.4%) of the 124 women with NMR data were African American. OAT-treated women (n = 34, 27.4%; 27 receiving methadone and 7 buprenorphine) were more likely to be white (79% vs. 30%, p < .001) and to have a lower mean PHQ-9 total score (2.91 [SD = 2.83] vs. 4.83 [SD = 3.82], p = .007). OAT-treated women reported smoking more CPD (9.50 [SD = 5.26] vs. 7.20 [SD = 3.65], p = .005) and had higher NMR (0.78 [SD = 0.36] vs. 0.56 [SD = 0.25], p = .001) than the non-OAT-treated group. In a linear regression analysis adjusting for race, depression severity, and CPD, NMR was greater in the OAT group (p = .025), among whom the daily methadone-equivalent dosage correlated with NMR (Spearman's ρ = 0.49, p = .003). CONCLUSIONS: Consistent with the findings of Oncken et al. (2019), we found that OAT smokers smoked more and had higher NMR than non-OAT smokers. As higher NMR is associated with a reduced likelihood of smoking cessation, the effects on NMR of both pregnancy and OAT could contribute to a lower smoking cessation rate in pregnant smokers receiving chronic opioid therapy. IMPLICATIONS: We replicated the finding that the NMR is significantly greater among pregnant smokers receiving OAT than those not receiving this treatment for opioid use disorder. Furthermore, we found that the dosage of the OAT was significantly associated with the NMR level. These findings may contribute to a poorer response to smoking cessation treatment in pregnant women treated with OAT, particularly those receiving high-dose therapy, and raise the question of whether novel approaches are needed to treat smoking in this subgroup of pregnant smokers.

Behavioral and Accumbal Responses During an Affective Go/No-Go Task Predict Adherence to Injectable Naltrexone Treatment in Opioid Use Disorder.

Adherence is a major factor in the effectiveness of the injectable extended-release naltrexone as a relapse prevention treatment in opioid use disorder. We examined the value of a variant of the Go/No-go paradigm in predicting extended-release naltrexone adherence in 27 detoxified opioid use disorder patients who were offered up to 3 monthly extended-release naltrexone injections. Before extended-release naltrexone, participants performed a Go/No-go task that comprised positively valenced Go trials and negatively valenced No-go trials during a functional magnetic resonance imaging scan. Errors of commission and neural responses to the No-go vs Go trials were independent variables. Adherence, operationalized as the completion of all 3 extended-release naltrexone injections, was the outcome variable. Fewer errors of commission and greater left accumbal response during the No-go vs Go trials predicted better adherence. These findings support the clinical potential of the behavioral and neurophysiological correlates of response inhibition in the prediction of extended-release naltrexone treatment outcomes in opioid use disorder.

A Data Analysis Method for Using Longitudinal Binary Outcome Data from a SMART to Compare Adaptive Interventions.

Sequential multiple assignment randomized trials (SMARTs) are a useful and increasingly popular approach for gathering information to inform the construction of adaptive interventions to treat psychological and behavioral health conditions. Until recently, analysis methods for data from SMART designs considered only a single measurement of the outcome of interest when comparing the efficacy of adaptive interventions. Lu et al. proposed a method for considering repeated outcome measurements to incorporate information about the longitudinal trajectory of change. While their proposed method can be applied to many kinds of outcome variables, they focused mainly on linear models for normally distributed outcomes. Practical guidelines and extensions are required to implement this methodology with other types of repeated outcome measures common in behavioral research. In this article, we discuss implementation of this method with repeated binary outcomes. We explain how to compare adaptive interventions in terms of various summaries of repeated binary outcome measures, including average outcome (area under the curve) and delayed effects. The method is illustrated using an empirical example from a SMART study to develop an adaptive intervention for engaging alcohol- and cocaine-dependent patients in treatment. Monte Carlo simulations are provided to demonstrate the good performance of the proposed technique.

Identifying a set that contains the best dynamic treatment regimes.

A dynamic treatment regime (DTR) is a treatment design that seeks to accommodate patient heterogeneity in response to treatment. DTRs can be operationalized by a sequence of decision rules that map patient information to treatment options at specific decision points. The sequential, multiple assignment, randomized trial (SMART) is a trial design that was developed specifically for the purpose of obtaining data that informs the construction of good (i.e. efficacious) decision rules. One of the scientific questions motivating a SMART concerns the comparison of multiple DTRs that are embedded in the design. Typical approaches for identifying the best DTRs involve all possible comparisons between DTRs that are embedded in a SMART, at the cost of greatly reduced power to the extent that the number of embedded DTRs (EDTRs) increase. Here, we propose a method that will enable investigators to use SMART study data more efficiently to identify the set that contains the most efficacious EDTRs. Our method ensures that the true best EDTRs are included in this set with at least a given probability. Simulation results are presented to evaluate the proposed method, and the Extending Treatment Effectiveness of Naltrexone SMART study data are analyzed to illustrate its application.

Differential Child Maltreatment Risk Across Deployment Periods of US Army Soldiers.

OBJECTIVES: We described the risk for maltreatment among toddlers of US Army soldiers over different deployment cycles to develop a systematic response within the US Army to provide families appropriate supports. METHODS: We conducted a person-time analysis of substantiated maltreatment reports and medical diagnoses among children of 112,325 deployed US Army soldiers between 2001 and 2007. RESULTS: Risk of maltreatment was elevated after deployment for children of soldiers deployed once but not for children of soldiers deployed twice. During the 6 months after deployment, children of soldiers deployed once had 4.43 substantiated maltreatment reports and 4.96 medical diagnoses per 10,000 child-months. The highest maltreatment rate among children of soldiers deployed twice occurred during the second deployment for substantiated maltreatment (4.83 episodes per 10,000 child-months) and before the first deployment for medical diagnoses of maltreatment (3.78 episodes per 10,000 child-months). CONCLUSIONS: We confirmed an elevated risk for child maltreatment during deployment but also found a previously unidentified high-risk period during the 6 months following deployment, indicating elevated stress within families of deployed and returning soldiers. These findings can inform efforts by the military to initiate and standardize support and preparation to families during periods of elevated risk.

Comparing treatment policies with assistance from the structural nested mean model.

Treatment policies, also known as dynamic treatment regimes, are sequences of decision rules that link the observed patient history with treatment recommendations. Multiple, plausible, treatment policies are frequently constructed by researchers using expert opinion, theories, and reviews of the literature. Often these different policies represent competing approaches to managing an illness. Here, we develop an "assisted estimator" that can be used to compare the mean outcome of competing treatment policies. The term "assisted" refers to the fact estimators from the Structural Nested Mean Model, a parametric model for the causal effect of treatment at each time point, are used in the process of estimating the mean outcome. This work is motivated by our work on comparing the mean outcome of two competing treatment policies using data from the ExTENd study in alcohol dependence.

Comparing dynamic treatment regimes using repeated-measures outcomes: modeling considerations in SMART studies.

A dynamic treatment regime (DTR) is a sequence of decision rules, each of which recommends a treatment based on a patient's past and current health status. Sequential, multiple assignment, randomized trials (SMARTs) are multi-stage trial designs that yield data specifically for building effective DTRs. Modeling the marginal mean trajectories of a repeated-measures outcome arising from a SMART presents challenges, because traditional longitudinal models used for randomized clinical trials do not take into account the unique design features of SMART. We discuss modeling considerations for various forms of SMART designs, emphasizing the importance of considering the timing of repeated measures in relation to the treatment stages in a SMART. For illustration, we use data from three SMART case studies with increasing level of complexity, in autism, child attention deficit hyperactivity disorder, and adult alcoholism. In all three SMARTs, we illustrate how to accommodate the design features along with the timing of the repeated measures when comparing DTRs based on mean trajectories of the repeated-measures outcome.

Which alcohol use disorder criteria contribute to the association of ADH1B with alcohol dependence?

Although alcohol dependence (AD) is approximately 50% heritable, little is known about how specific genetic loci affect AD risk. In a genome-wide association study (GWAS), we identified highly significant associations between two population-specific functional variants in the alcohol dehydrogenase 1B gene (ADH1B) and AD in African-Americans (AAs; rs2066702) and European-Americans (EAs; rs1229984). In the current study, we determined which specific diagnostic criteria contributed to the observed associations of ADH1B SNPs with AD. Our analysis included both the DSM-IV and DSM-5 diagnostic systems. We also investigated the relationship of ADH1B variants to the maximum number of drinks consumed in a 24-hour period (MaxDrinks), a presumed intermediate phenotype of AD. We found that, although all criteria made strong individual contributions to the associations, the largest contributions came from those reflecting neuroadaptation: tolerance (rs2066702) and withdrawal (rs1229984). Overall, evidence for association with DSM-5 criteria was slightly stronger than for DSM-IV criteria. For rs2066702, results were similar for DSM-IV and DSM-5 criteria. However, the most significant DSM-5 criterion associated with rs1229984 was alcohol-related social/interpersonal problems. Both ADH1B variants were associated with MaxDrinks, a measure of innate tolerance, and MaxDrinks mediated the associations between ADH1B and alcohol outcomes. We replicated the findings for rs2066702 and tolerance in an independent sample of AAs. Taken together, these results suggest that variation in ADH1B affects the adaptation to heavy drinking, highlighting population-specific differences in genetic risk for AUD. They also suggest that the revisions reflected in DSM-5 AUD may enhance the utility of that diagnosis for gene finding.

Treatment choices and subsequent attendance by substance-dependent patients who disengage from intensive outpatient treatment.

In an effort to increase engagement in effective treatment, we offered a choice of alternate evidence-based treatments to 137 alcohol- or cocaine-dependent adults (110 males, 27 females) who entered an intensive outpatient program (IOP) but disengaged within the first 8 weeks. We hypothesized that disengaged patients would choose and subsequently attend alternatives to IOP when given the chance, that their choices would be consistent with their previously-stated preferences, and that demographic and clinical characteristics would be predictive of alternatives chosen. Of 96 participants reached by phone, 19% chose no treatment; 49% chose to return to IOP; 24% chose individual psychotherapy; 6% chose telephone counseling; 2% chose naltrexone with medication management. There were few relationships between participant characteristics and choices made upon disengagement. Participants who chose alternative treatments were equally likely to attend their chosen treatment as those who chose IOP. Limited interest in alternative treatments may reflect allegiance to IOP, which was initially chosen by all participants. Implications for implementation of patient-centered adaptive treatment are discussed.

Content matters: neuroimaging investigation of brain and behavioral impact of televised anti-tobacco public service announcements.

Televised public service announcements are video ads that are a key component of public health campaigns against smoking. Understanding the neurophysiological correlates of anti-tobacco ads is an important step toward novel objective methods of their evaluation and design. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate the brain and behavioral effects of the interaction between content ("argument strength," AS) and format ("message sensation value," MSV) of anti-smoking ads in humans. Seventy-one nontreatment-seeking smokers viewed a sequence of 16 high or 16 low AS ads during an fMRI scan. Dependent variables were brain fMRI signal, the immediate recall of the ads, the immediate change in intentions to quit smoking, and the urine levels of a major nicotine metabolite cotinine at a 1 month follow-up. Whole-brain ANOVA revealed that AS and MSV interacted in the inferior frontal, inferior parietal, and fusiform gyri; the precuneus; and the dorsomedial prefrontal cortex (dMPFC). Regression analysis showed that the activation in the dMPFC predicted the urine cotinine levels 1 month later. These results characterize the key brain regions engaged in the processing of persuasive communications and suggest that brain fMRI response to anti-smoking ads could predict subsequent smoking severity in nontreatment-seeking smokers. Our findings demonstrate the importance of the quality of content for objective ad outcomes and suggest that fMRI investigation may aid the prerelease evaluation of televised public health ads.

A randomized clinical trial of alcohol care management delivered in Department of Veterans Affairs primary care clinics versus specialty addiction treatment.

BACKGROUND: Alcohol use disorder is one of the leading causes of disability worldwide. Despite the availability of efficacious treatments, few individuals with an alcohol use disorder are actively engaged in treatment. Available evidence suggests that primary care may play a crucial role in the identification of patients with an alcohol use disorder, delivery of interventions, and the success of treatment. OBJECTIVE: The principal aims of this study were to test the effectiveness of a primary care-based Alcohol Care Management (ACM) program for alcohol use disorder and treatment engagement in veterans. DESIGN: The design of the study was a 26-week single-blind randomized clinical trial. The study was conducted in the primary care practices at three VA medical centers. Participants were randomly assigned to treatment in ACM or standard treatment in a specialty outpatient addiction treatment program. PARTICIPANTS: One hundred and sixty-three alcohol-dependent veterans were randomized. INTERVENTION: ACM focused on the use of pharmacotherapy and psychosocial support. ACM was delivered in-person or by telephone within the primary care clinic. MAIN MEASUREMENTS: Engagement in treatment and heavy alcohol consumption. KEY RESULTS: The ACM condition had a significantly higher proportion of participants engaged in treatment over the 26 weeks [OR = 5.36, 95 % CI = (2.99, 9.59)]. The percentage of heavy drinking days were significantly lower in the ACM condition [OR = 2.16, 95 % CI = (1.27, 3.66)], while overall abstinence did not differ between groups. CONCLUSIONS: Results demonstrate that treatment for an alcohol use disorder can be delivered effectively within primary care, leading to greater rates of engagement in treatment and greater reductions in heavy drinking.

Tom Ten Have's contributions to causal inference and biostatistics: review and future research directions.

Tom Ten Have made many contributions to causal inference and biostatistics before his untimely death. This paper reviews Tom's contributions and discusses potential related future research directions. We focus on Tom's contributions to longitudinal/repeated measures categorical data analysis and particularly his contributions to causal inference. Tom's work on causal inference was primarily in the areas of estimating the effect of receiving treatment in randomized trials with nonadherence and mediation analysis. A related area to mediation analysis he was working on at the time of his death was posttreatment effect modification with applications to designing adaptive treatment strategies.