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CORK Bibliography: Research, Data Analysis

55 citations. January 2009 to present

Prepared: September 2012

Ahmed SH; Graupner M; Gutkin B. Computational approaches to the neurobiology of drug addiction. Pharmacopsychiatry 42(Supplement 1): S144-S152, 2009. (77 refs.)

To increase our understanding of drug addiction - notably its pharmacological and neurobiological determinants - researchers have begun to formulate computational models of drug self-administration. Currently, one can roughly distinguish between three classes of models which all have in common to attribute to brain dopamine signaling a key role in addiction. The first class of models contains quantitative pharmacological models that describe the influence of pharmacokinetic and pharmacodynamic factors on drug self-administration. These models fail, however, to explain how the drug self-administration behavior is acquired and how it eventually becomes rigid and compulsive with extended drug use. Models belonging to the second class circumvent some of these limitations by modeling how drug use usurps the function of dopamine in reinforcement learning and action selection. However, despite their behavioral plausibility, these latter models lack neurobiological plausibility and ignore the potential role of opponent processes in addiction. The third class of models attempts to surmount these pitfalls by providing a more realistic picture of the midbrain dopamine circuitry and of the complex action of drugs of abuse in the output of this circuitry. Here we provide a brief overview of these different models to illustrate the potential contribution of mathematical modeling to our understanding of the neurobiology of drug addiction.

Arndt S. Stereotyping and the treatment of missing data for drug and alcohol clinical trials. (editorial). Substance Abuse Treatment, Prevention and Policy 4: e-article 2, 2009. (7 refs.)

Stigma and stereotyping of marginalized groups often is insidious and shows up in unlikely places, for instance in how clinical trials consider dropouts in treatment research. A surprising number of studies presume that people who do not complete the study protocol relapse and code their data as if they had been observed. There is no good statistical rationale for this treatment of missing data and numerous and more defensible alternative methods are available. We need to be mindful about our attitudes and preconceptions about the people we are intending to help. There is no good reason to continue to support science built on this scientifically indefensible stereotyping, however unintentional.

Bachman JG; O'Malley PM; Johnston LD; Schulenberg JE; Wallace JM. Racial/ethnic differences in the relationship between parental education and substance use among us 8th-, 10th-, and 12th-grade students: Findings From the Monitoring the Future Project. Journal of Studies on Alcohol and Drugs 72(2): 279-285, 2011. (17 refs.)

Objective: Secondary school students' rates of substance use vary significantly by race/ethnicity and by their parents' level of education (a proxy for socioeconomic status). The relationship between students' substance use and race/ethnicity is, however, potentially confounded because parental education also differs substantially by race/ethnicity. This report disentangles the confounding by examining White, African American, and Hispanic students separately, showing how parental education relates to cigarette smoking, heavy drinking, and illicit drug use. Method: Data are from the 1999-2008 Monitoring the Future nationally representative in-school surveys of more than 360,000 students in Grades 8, 10, and 12. Results: (a) High proportions of Hispanic students have parents with the lowest level of education, and the relatively low levels of substance use by these students complicates total sample data linking parental education and substance use. (b) There are clear interactions: Compared with White students, substance use rates among African American and Hispanic students are less strongly linked with parental education (and are lower overall). (c) Among White students, 8th and 10th graders show strong negative relations between parental education and substance use, whereas by 12th grade their heavy drinking and marijuana use are not correlated with parental education. Conclusions: Low parental education appears to be much more of a risk factor for White students than for Hispanic or African American students. Therefore, in studies of substance use epidemiology, findings based on predominantly White samples are not equally applicable to other racial/ethnic subgroups. Conversely, the large proportions of minority students in the lowest parental education category can mask or weaken findings that are clearer among White students alone.

Bandyopadhyay D; DeSantis SM; Korte JE; Brady KT. Some considerations for excess zeroes in substance abuse research. American Journal of Drug and Alcohol Abuse 37(5): 376-382, 2011. (21 refs.)

Background: Count data collected in substance abuse research often come with an excess of "zeroes," which are typically handled using zero-inflated regression models. However, there is a need to consider the design aspects of those studies before using such a statistical model to ascertain the sources of zeroes. Objectives: We sought to illustrate hurdle models as alternatives to zero-inflated models to validate a two-stage decision-making process in situations of "excess zeroes." Methods: We use data from a study of 45 cocaine-dependent subjects where the primary scientific question was to evaluate whether study participation influences drug-seeking behavior. The outcome, "the frequency (count) of cocaine use days per week," is bounded (ranging from 0 to 7). We fit and compare binomial, Poisson, negative binomial, and the hurdle version of these models to study the effect of gender, age, time, and study participation on cocaine use. Results: The hurdle binomial model provides the best fit. Gender and time are not predictive of use. Higher odds of use versus no use are associated with age; however once use is experienced, odds of further use decrease with increase in age. Participation was associated with higher odds of no-cocaine use; once there is use, participation reduced the odds of further use. Conclusion: Age and study participation are significantly predictive of cocaine-use behavior. Scientific Significance: The two-stage decision process as modeled by a hurdle binomial model (appropriate for bounded count data with excess zeroes) provides interesting insights into the study of covariate effects on count responses of substance use, when all enrolled subjects are believed to be "at-risk" of use.

Barnes SA; Larsen MD; Schroeder D; Hanson A; Decker PA. Missing data assumptions and methods in a smoking cessation study. Addiction 105(3): 431-437, 2010. (31 refs.)

Aim: A sizable percentage of subjects do not respond to follow-up attempts in smoking cessation studies. The usual procedure in the smoking cessation literature is to assume that non-respondents have resumed smoking. This study used data from a study with a high follow-up rate to assess the degree of bias that may be caused by different methods of imputing missing data. Design and methods: Based on a large data set with very little missing follow-up information at 12 months, a simulation study was undertaken to compare and contrast missing data imputation methods (assuming smoking, propensity score matching and optimal matching) under various assumptions as to how the missing data arose (randomly generated missing values, increased non-response from smokers and a hybrid of the two). Findings: Missing data imputation methods all resulted in some degree of bias which increased with the amount of missing data. Conclusion: None of the missing data imputation methods currently available can compensate for bias when there are substantial amounts of missing data.

Bird SM. Database linkage: Outside reflections on health care in prisons. (commentary). Addiction 104(7): 1241-1242, 2009. (12 refs.)

This commentary addresses the article by JN Marzo, M.Rotily, F Meroueh, M Varastet, C Hunault C., et al. " RECAMS Study Group. Maintenance therapy and 3-year outcome of opioid-dependent prisoners: a prospective study in France (2003�06). Addiction 2009; 104: 1233�40. The commentary addresses issues related to data analysis and the sample size required for statistical power and ability to generalize.

Burlew AK; Feaster D; Brecht ML; Hubbard R. Measurement and data analysis in research addressing health disparities in substance abuse. Journal of Substance Abuse Treatment 36(1): 25-43, 2009. (86 refs.)

This article describes concrete strategies for conducting substance abuse research with ethnic minorities. Two issues associated with valid analysis, measurement and data analysis, are included. Both empirical (e.g., confirmatory factor analysis, item response theory, and regression) and nonempirical (e.g., focus groups, expert panels, pilot studies, and translation equivalence) approaches to improve measures are described. A discussion of the use of norms and Cutoff scores derived from a different ethnic group along with the effects of the ethnicity of the interviewer or coder on measurement is included. The section on data analysis describes why the use of race-comparison designs may lead to misleading conclusions. Alternatives to race-comparison analysis including within-group and between-group analyses are described. The shortcomings of combining ethnic groups for analyses are discussed. The article ends with a list of recommendations for research with ethnic minorities.

Burlew AK; Weekes JC; Montgomery L; Feaster DJ; Robbins MS; Rosa CL et al. Conducting research with racial/ethnic minorities: Methodological lessons from the National Institute on Drug Abuse Clinical Trials Network. American Journal of Drug and Alcohol Abuse 37(5): 324-332, 2011. (62 refs.)

Background: Multiple studies in the National Institute on Drug Abuse Clinical Trials Network (CTN) demonstrate strategies for conducting effective substance abuse treatment research with racial/ethnic minorities (REMs). Objectives: The objectives of this article are to describe lessons learned within the CTN to (1) enhance recruitment, retention, and other outcomes; (2) assess measurement equivalence; and (3) use data analytic plans that yield more information. Method: This article includes background information and examples from multiple CTN studies on inclusion, measurement, and data analysis. Results and Conclusions: Seven recommendations are included for conducting more effective research on REMs.

Carle AC. Cross-cultural invalidity of alcohol dependence measurement across Hispanics and Caucasians in 2001 and 2002. Addictive Behaviors 34(1): 43-50, 2009. (57 refs.)

Aims: Do assessments of alcohol dependence demonstrate similarly validity across Hispanics and non-Hispanic Caucasians? This investigation examined this question. Method: it employed confirmatory factor analyses for ordered-categorical measures to search for measurement bias on the AUDADIS, a standardized measure of alcohol dependence across Hispanic (n = 4819) and non-Hispanic Caucasians (n = 16, 109) in a nationally representative survey of alcohol use in the United States conducted in 2001 and 2002. Measurement: Analyses considered whether 27 items operationalizing the DSM-IV alcohol dependence construct provided equivalent measurement. Findings and conclusions: Nine items revealed statistically significant bias, suggesting strong caution regarding the cross-ethnic validity of alcohol dependence. Sensitivity analyses established that item level differences erroneously impact alcohol dependence estimates among the 2001-2002 US Hispanic population. Biased measurement underestimates differences between Hispanics and non-Hispanic Caucasians, underestimates Hispanics' true use levels, and falsely minimizes current increases in drinking behavior evidenced among Hispanics. Findings urge improved public health efforts among the Hispanic community and underscore the necessity for cultural sensitivity when generalizing measures and constructs developed in the majority to Hispanic individuals.

Chen HY; Gao SS. Estimation of average treatment effect with incompletely observed longitudinal data: Application to a smoking cessation study. Statistics in Medicine 28(19): 2451-2472, 2009. (38 refs.)

We study the problem of estimation and inference on the average treatment effect in a smoking, cessation trial where an outcome and some auxiliary information were measured longitudinally. and both were subject to missing Values. Dynamic generalized linear mixed effects models linking the outcome, the auxiliary information, and the covariates are proposed. The maximum likelihood approach is applied to the estimation and inference of the model parameters. The average treatment effect is estimated by the G-computation approach and the sensitivity of the treatment effect estimate to the nonignorable missing data mechanisms is investigated through the local sensitivity analysis approach. The proposed approach call handle missing data that form arbitrary missing patterns over little. We applied the proposed method to the analysis of the smoking cessation trial.

Clarke H; Byford M. Addictive drug management policies in a long-run economic model. Australian Economic Papers 48(2): 151-165, 2009. (20 refs.)

A model of illicit, addictive drug use is proposed when users have foresight. Impacts of drug use penalties, penalties on drug use-related crime, support for drug user rehabilitation as well as the effects of health-related, harm-minimisation policies are analysed. In the short run, government policies impact only on the drug use intensities of existing addicted and casual users. Longer term policy-induced user-cost changes impact on new user and addict numbers through their effect on recruitment into addiction and quit dynamics. Effects of policies on user numbers, usage intensities and impacts on long-run social costs are analysed over this long-term horizon. The model provides a setting for analysing the long-run effects of illicit drug management policies on the social costs of illicit drug use and allows assessment of drug use abstinence and harm minimisation policy tradeoffs.

Cook PJ. Comment on "Explaining change and stasis in alcohol consumption" (editorial). Addiction Research & Theory 17(6): 586-587, 2009. (4 refs.)

The author comments on the article "Explaining Change and Stasis in Alcohol Consumption," which highlights the findings of a research that alcohol consumption in Nordic countries in 2003 and 2004 was unresponsive to reductions in price. He argues that although the research provides a consistent set of findings, he thinks that there was more drinking and alcohol abuse immediately following the reduction in price.

Crits-Christoph P; Johnson J; Gallop R; Gibbons MBC; Ring-Kurtz S; Hamilton JL et al. A generalizability theory analysis of group process ratings in the treatment of cocaine dependence. Psychotherapy Research 21(3): 252-266, 2011. (45 refs.)

Videotaped group drug counseling sessions were rated for alliance, self-disclosure, positive and negative feedback, group cohesion, and degree of participation of each group member. Interrater reliability was good to excellent for most measures. However, generalizability coefficients based on statistical models that included terms for patient, counselor, session, group, and rater revealed that some measures had inadequate dependability at the patient level if only two raters and two sessions were used to create patient-level scores. In contrast, good generalizability coefficients based on two raters and two sessions were obtained for alliance, non-positive learning statements received from counselor, participation variables, and self-disclosures about the past. The implications of the findings for the design of process-outcome studies are discussed.

Falcaro M; Povey AC; Fielder A; Nahit E; Pickles A. Estimating intervention effects in a complex multi-level smoking prevention study. International Journal of Environmental Research and Public Health 6(2): 463-477, 2009. (36 refs.)

This paper illustrates how to estimate cumulative and non-cumulative treatment effects in a complex school-based smoking intervention study. The instrumental variable method is used to tackle non-compliance and measurement error for a range of treatment exposure measures (binary, ordinal and continuous) in the presence of clustering and dropout. The results are compared to more routine analyses. The empirical findings from this study provide little encouragement for believing that poorly resourced school-based interventions can bring about substantial long-lasting reductions in smoking behaviour but that novel components such as a computer game might have some short-term effect.

Feaster DJ; Mikulich-Gilbertson S; Brincks AM. Modeling site effects in the design and analysis of multi-site trials. American Journal of Drug and Alcohol Abuse 37(5): 383-391, 2011. (46 refs.)

Background: Careful consideration of site effects is important in the analysis of multi-site clinical trials for drug abuse treatment. The statistical choices for modeling these effects have implications for both trial planning and interpretation of findings. Objectives: Three broad approaches for modeling site effects are presented: omitting site from the analysis; modeling site as a fixed effect; and modeling site as a random effect. Both the direct effect of site and the interaction of site and treatment are considered. Methods: The statistical model, and consequences, for each approach are presented along with examples from existing clinical trials. Power analysis calculations provide sample size requirements for adequate statistical power for studies utilizing 6, 8, 10, 12, 14, and 16 treatment sites. Results: Results of the power analyses showed that the total sample required falls rapidly as the number of sites increases in the random effect approach. In the fixed effect approach in which the interaction of site and treatment is considered, the required number of participants per site decreases as the number of sites increases. Conclusions: Ignoring site effects is not a viable option in multi-site clinical trials. There are advantages and disadvantages to the fixed effect and random effect approaches to modeling site effects. Scientific Significance: The distinction between efficacy trials and effectiveness trials is rarely sharp. The choice between random effect and fixed effect statistical modeling can provide different benefits depending on the goals of the study.

Flaherty BP. Latent class and mixture models' potential contributions to understanding connections between menthol and other cigarette smoking characteristics. (editorial). Addiction 105(Supplement 1): 11-12, 2010. (15 refs.)

Foxcroft DR; Kypri K; Simonite V. Bayes' Theorem to estimate population prevalence from Alcohol Use Disorders Identification Test (AUDIT) scores. Addiction 104(7): 1132-1137, 2009. (13 refs.)

The aim in this methodological paper is to demonstrate, using Bayes' Theorem, an approach to estimating the difference in prevalence of a disorder in two groups whose test scores are obtained, illustrated with data from a college student trial where 12-month outcomes are reported for the Alcohol Use Disorders Identification Test (AUDIT). Using known population prevalence as a background probability and diagnostic accuracy information for the AUDIT scale, we calculated the post-test probability of alcohol abuse or dependence for study participants. The difference in post-test probability between the study intervention and control groups indicates the effectiveness of the intervention to reduce alcohol use disorder rates. In the illustrative analysis, at 12-month follow-up there was a mean AUDIT score difference of 2.2 points between the intervention and control groups: an effect size of unclear policy relevance. Using Bayes' Theorem, the post-test probability mean difference between the two groups was 9% (95% confidence interval 3-14%). Interpreted as a prevalence reduction, this is evaluated more easily by policy makers and clinicians. Important information on the probable differences in real world prevalence and impact of prevention and treatment programmes can be produced by applying Bayes' Theorem to studies where diagnostic outcome measures are used. However, the usefulness of this approach relies upon good information on the accuracy of such diagnostic measures for target conditions.

Frigon AP; Krank MD. Self-coded indirect memory associations in a brief school-based intervention for substance use suspensions. Psychology of Addictive Behaviors 23(4): 736-742, 2009. (35 refs.)

This study assessed the concurrent validity of self-generated and self-coded substance use associations for marijuana and alcohol use. Grades seven to twelve students were assessed as part of a brief intervention program in lieu of suspension for substance use infractions in school. During the cognitive assessment, students generated memory associations to probes for high-risk situations and desirable outcomes. Later, the participant rated their responses according to categories including both non-risk and substance use. Three different coding methods were compared: (1) conservative codes using clearly unambiguous responses, (2) liberal scores adding ambiguous, but likely responses, and (3) self-coded. Self-coded scores were higher, had stronger correlations with substance use, and were better predictors of substance use and problems than either conservative or liberal coded scores. These findings suggest that self-coding may be used to improve concurrent validity, decrease ambiguities in coding, and reduce the cost of measuring memory associations. The present method promises a cost effective and valid measure of indirect substance use cognitions that can be readily adapted for interventions.

Green CE; Moeller FG; Schmitz JM; Lucke JF; Lane SD; Swann AC et al. Evaluation of heterogeneity in pharmacotherapy trials for drug dependence: A bayesian approach. American Journal of Drug and Alcohol Abuse 35(2): 95-102, 2009. (48 refs.)

Aims: Difficulty identifying effective pharmacotherapies for cocaine dependence has led to suggestions that subgroup differences may account for some of the heterogeneity in treatment response. Well-attested methodological difficulties associated with these analyses recommend the use of Bayesian statistical reasoning for evaluation of salient interaction effects. Methods: A secondary data analysis of a previously published, double-blind, randomized controlled trial examines the interaction of decision-making, as measured by the Iowa Gambling Task, and citalopram in increasing longest sustained abstinence from cocaine use. Results: Bayesian analysis indicated that there was a 99% chance that improved decision-making enhances response to citalopram. Given the strong positive nature of this finding, a formal, quantitative Bayesian approach to evaluate the result from the perspective of a skeptic was applied. Conclusions: Bayesian statistical reasoning provides a formal means of weighing evidence for the presence of an interaction in scenarios where conventional, Frequentist analyses may be less informative. [Supplementary materials are available for this article. Go to the publisher's online edition of The American Journal of Drug and Alcohol Abuse for the following free supplemental resource: Appendix 1].

Grittner U; Gmel G; Ripatti S; Bloomfield K; Wicki M. Missing value imputation in longitudinal measures of alcohol consumption. International Journal of Methods in Psychiatric Research 20(1): 50-61, 2011. (44 refs.)

Attrition in longitudinal studies can lead to biased results. The study is motivated by the unexpected observation that alcohol consumption decreased despite increased availability, which may be due to sample attrition of heavy drinkers. Several imputation methods have been proposed, but rarely compared in longitudinal studies of alcohol consumption. The imputation of consumption level measurements is computationally particularly challenging due to alcohol consumption being a semi-continuous variable (dichotomous drinking status and continuous volume among drinkers), and the non-normality of data in the continuous part. Data come from a longitudinal study in Denmark with four waves (2003-2006) and 1771 individuals at baseline. Five techniques for missing data are compared: Last value carried forward (LVCF) was used as a single, and Hotdeck, Heckman modelling, multivariate imputation by chained equations (MICE), and a Bayesian approach as multiple imputation methods. Predictive mean matching was used to account for non-normality, where instead of imputing regression estimates, "real" observed values from similar cases are imputed. Methods were also compared by means of a simulated dataset. The simulation showed that the Bayesian approach yielded the most unbiased estimates for imputation. The finding of no increase in consumption levels despite a higher availability remained unaltered.

Guan YT; Li YH; Sinha R. Cocaine dependence treatment data: Methods for measurement error problems with predictors derived from stationary stochastic processes. Journal of the American Statistical Association 106(494): 480-493, 2011. (30 refs.)

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material.

Gullo MJ; Ward E; Dawe S; Powell J; Jackson CJ. Support for a two-factor model of impulsivity and hazardous substance use in British and Australian young adults. Journal of Research in Personality 45(1): 10-18, 2011. (73 refs.)

Multiple lines of evidence suggest impulsivity comprises two distinct components relevant to substance misuse. Reward drive reflects sensitivity to rewarding stimuli and subsequent approach motivation. Rash impulsiveness reflects the ability to inhibit such approach behavior in light of negative consequences. However, several studies suggest the latter trait to be a more robust predictor. This begs the question as to whether a less parsimonious two-factor model is necessary. This study employed structural equation modeling to compare the fit of one- and two-factor impulsivity models to alcohol and drug use data provided by British (n = 183) and Australian (n = 271) young adults. Results consistently supported the two-factor model and its cross-cultural consistency, with rash impulsiveness being the more robust predictor.

Hartzler B; Donovan DM; Huang Z. Rates and influences of alcohol use disorder comorbidity among primary stimulant misusing treatment-seekers: Meta-analytic findings across eight National Institute on Drug Abuse CTN Trials. American Journal of Drug and Alcohol Abuse 37(5): 460-471, 2011. (25 refs.)

Background: There is need to improve treatment effectiveness for stimulant misusers, and one means of doing so is by tailoring services to account for common diagnostic comorbidities and psychosocial challenges they face. Objectives: Using its publicly available datasets, this CTN-approved secondary analysis project examined prevalence of alcohol use disorders (AUDs) among primary stimulant misusing treatment-seekers as well as impact of AUD comorbidity on their pre-treatment psychosocial functioning. Methods: Upon identifying a primary stimulant misuser subsample (N = 1133) from among aggregated treatment-seekers across eight CTN trials, diagnostic data were used to document lifetime AUD rates. Paired comparisons, stratified by stimulant drug type (e. g., amphetamine, cocaine) then tested the influence of AUD comorbidity on psychosocial indices from the Addiction Severity Index - Lite. Results: A high AUD rate (45%) was found in this client population. Among primary cocaine misusers, those with AUD were more likely to: (i) show elevated Addiction Severity Index composite scores, (ii) perceive greater importance of drug treatment, and (iii) endorse psychiatric symptoms and perceived need for their treatment. Among primary amphetamine misusers, those with AUD were more likely to endorse specific psychiatric symptoms. Conclusion: Study findings document AUD comorbidity as a fairly common diagnostic feature of primary stimulant misusers, and suggest it is a pervasive influence on the pre-treatment psychosocial functioning of cocaine misusers. Scientific Significance: This study demonstrates the utility of CTN common assessment battery for secondary analysis projects, though challenges noted during its conduct highlight the value of consistent data collection and documentation within and across CTN trials.

Hedden SL; Woolson RF; Carter RE; Palesch Y; Upadhyaya HP; Malcolm RJ. The impact of loss to follow-up on hypothesis tests of the treatment effect for several statistical methods in substance abuse clinical trials. Journal of Substance Abuse Treatment 37(1): 54-63, 2009. (41 refs.)

"Loss to follow-up" can be substantial in substance abuse clinical trials. When extensive losses to follow-up occur, one must cautiously analyze and interpret the findings of a research study. Aims of this project were to introduce the types of missing data mechanisms and describe several methods for analyzing data with loss to follow-up. Furthermore, a simulation study compared Type I error and power of several methods when missing data amount and mechanism varies. Methods compared were the following: Last observation carried forward (LOCF), multiple imputation (MI), modified stratified summary statistics (SSS), and mixed effects models. Results demonstrated nominal Type I error for all methods; power was high for all methods except LOCF. Mixed effect model, modified SSS, and MI are generally recommended for use; however, many methods require that the data are missing at random or missing completely at random (i.e., "ignorable"). If the missing data are presumed to be nonignorable, a sensitivity analysis is recommended.

Hellemann G; Conner BT; Anglin MD; Longshore D. Seeing the trees despite the forest: Applying recursive partitioning to the evaluation of drug treatment retention. Journal of Substance Abuse Treatment 36(1): 59-64, 2009. (14 refs.)

Aims: The aim of this study is to demonstrate the utility of recursive partitioning (RP) for analyzing process and outcome data in drug treatment research. The basic methodology of RP is introduced and applied to the prediction of treatment retention. Methods: A total of 315 individuals randomly assigned to one of two treatment conditions; 289 (91.7%) completed a comprehensive baseline assessment battery. Treatment retention was assessed at a 52-week follow-up interview. Findings: The RP approach was successful in generating a parsimonious decision tree that predicted drug treatment retention from the 195 input variables. Severity of drug use (as indicated by length of time speedballing), criminal behavior (as indicated by history of property crimes), level of insight, social network, and age at intake were predictive of treatment retention. The model is estimated to explain 32% of the variability in the population. Conclusions: RP supports the notion that there are early indicators of treatment retention and that specific approaches that are tailored to individuals' needs will be potentially more successful in treatment engagement and retention than the typical "one size fits all" approach. The results also demonstrate the utility of RP for the detection of complex relationships between diverse and interdependent predictors.

Hu MC; Pavlicova M; Nunes EV. Zero-inflated and hurdle models of count data with extra zeros: Examples from an HIV-Risk Reduction Intervention Trial. American Journal of Drug and Alcohol Abuse 37(5): 367-375, 2011. (17 refs.)

Background: In clinical trials of behavioral health interventions, outcome variables often take the form of counts, such as days using substances or episodes of unprotected sex. Classically, count data follow a Poisson distribution; however, in practice such data often display greater heterogeneity in the form of excess zeros (zero-inflation) or greater spread in the values (overdispersion) or both. Greater sample heterogeneity may be especially common in community-based effectiveness trials, where broad eligibility criteria are implemented to achieve a generalizable sample. Objectives: This article reviews the characteristics of Poisson model and the related models that have been developed to handle overdispersion (negative binomial (NB) model) or zero-inflation (zero-inflated Poisson (ZIP) and Poisson hurdle (PH) models) or both (zero-inflated negative binomial (ZINB) and negative binomial hurdle (NBH) models). Methods: All six models were used to model the effect of an HIV-risk reduction intervention on the count of unprotected sexual occasions (USOs), using data from a previously completed clinical trial among female patients (N = 515) participating in community-based substance abuse treatment (Tross et al. Effectiveness of HIV/AIDS sexual risk reduction groups for women in substance abuse treatment programs: Results of National Institute on Drug Abuse Clinical Trials Network Trial. J Acquir Immune Defic Syndr 2008; 48(5):581-589). Goodness of fit and the estimates of treatment effect derived from each model were compared. Results: The ZINB model provided the best fit, yielding a medium-sized effect of intervention. Conclusions and Scientific Significance: This article illustrates the consequences of applying models with different distribution assumptions on the data. If a model used does not closely fit the shape of the data distribution, the estimate of the effect of the intervention may be biased, either over- or underestimating the intervention effect.

Hughes JR; Callas PW. Data to assess the generalizability of samples from studies of adult smokers. Nicotine & Tobacco Research 12(1): 73-76, 2010. (10 refs.)

Introduction: One major determinant of external validity is the representativeness of the sample. This article provides data to help authors and readers assess the generalizability of samples from smoking studies. Methods: We analyzed the 2007 U. S. National Health Interview Survey. Results: We provide means, SEMs, and 95% CIs for demographic and smoking behavior characteristics of never-smokers, ever-smokers, all current smokers, current daily smokers, current nondaily smokers, long-term ex-smokers, and smokers who made a quit attempt in the last year. Discussion: Our results can help studies assess generalizability, set targets for recruitment, or reweigh data to reflect U. S. averages.

Jouanjus E; Pourcel L; Saivin S; Molinier L; Lapeyre-Mestre M. Use of multiple sources and capture-recapture method to estimate the frequency of hospitalizations related to drug abuse. Pharmacoepidemiology and Drug Safety 21(7): 733-741, 2012. (35 refs.)

Purpose: Addictive behaviours are often associated with hidden characteristics that are difficult to detect by usual approaches. This study aimed to estimate the incidence of serious drug-related complications by using the capture-recapture method in defined geographical area. Methods: Hospitalizations with mention of disorders related to drug of abuse were considered serious drug-related complications. We searched these cases in and crossed three sources of data: spontaneous reports of drug of abuse related disorders called NotS (Notification Spontanee) collected by the regional addictovigilance centre, computerised hospital database Programme de Medicalisation des Systemes d'Information (PMSI) and toxicological analyses (TA) carried out for hospitalized patients. Results: In 2007 and 2008, 1509 distinct cases were captured. After data modelling, the estimated number of psychoactive drug-related hospitalizations was 4744 (95%CI?=?40605429). Most frequent products were opioids (34%), cannabis (19%) and cocaine (13%). Multiple drugs were observed in 26% of cases. The incidence of serious drug-related complications in the area covered should be estimated at 5.7 (95%CI=5.55.9) per thousand 15- to 64-year-old inhabitants. The exhaustiveness of sources were 0.4% (95%CI?=?0.20.6) for NotS, 11.6% (95%CI=10.712.5) for TA and 22.6% (95%CI?=?21.423.8) for PMSI. Conclusions: The real number of cases far exceeds that of cases that can be identified through simple counts. In particular, it confirms the underreporting and even quantifies its magnitude. These results confirm that drug users are frequently hospitalised and require heavy medical management. Moreover, these results show the real although limited advantage of hospitalization database in detecting drug associated disorders in epidemiological studies.

Kim JK; Ulfarsson GF; Shankar VN; Mannering FL. A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. Accident Analysis and Prevention 42(6): 1751-1758, 2010. (26 refs.)

Pedestrian-injury severity has been traditionally modeled with approaches that have assumed that the effect of each variable is fixed across injury observations. This assumption ignores possible unobserved heterogeneity which is likely to be particularly important in pedestrian injuries because unobserved physical health, strength, and behavior may significantly affect the pedestrians' ability to absorb collision forces. To address such unobserved heterogeneity, this research applies a mixed logit model to analyze pedestrian-injury severity in pedestrian-vehicle crashes. Using police-reported collision data from 1997 through 2000 from North Carolina, several factors were found to more than double the average probability of fatal injury for pedestrians in motor-vehicle crashes including: darkness without streetlights (400% increase in fatality probability), vehicle is a truck (370% increase), freeway (330% increase), speeding involved (360% increase), and collisions involving a motorist who had been drinking (250% increase). It was also found that the effect of pedestrian age was normally distributed across observations, and that as pedestrians became older the probability of fatal injury increased substantially. Heterogeneity in the mean of the random parameters for the freeway and pedestrian-solely-at-fault collision indicators was related to pedestrian gender, and heterogeneity in the mean of the random parameters for the traffic-sign and motorist-back-up indicators was related to pedestrian age.

Korte JE; Magruder KM; Chiuzan CC; Logan SL; Killeen T; Bandyopadhyay D et al. Assessing drug use during follow-up: Direct comparison of candidate outcome definitions in pooled analyses of addiction treatment studies. American Journal of Drug and Alcohol Abuse 37(5): 358-366, 2011. (11 refs.)

Background: Selection of appropriate outcome measures is important for clinical studies of drug addiction treatment. Researchers use various methods for collecting drug use outcomes and must consider substances to be included in a urine drug screen (UDS); accuracy of self-report; use of various instruments and procedures for collecting self-reported drug use; and timing of outcome assessments. Objectives: We sought to define a set of candidate measures to (1) assess their intercorrelation and (2) identify any differences in results. Methods: Data were combined from completed protocols in the National Institute on Drug Abuse Clinical Trials Network (CTN), with a total of 1897 participants. We defined nine outcome measures based on UDS, self-report, or a combination. Multivariable, multilevel generalized estimating equation models were used to assess subgroup differences in intervention success, controlling for baseline differences and accounting for clustering by CTN protocols. Results: There were high correlations among all candidate outcomes. All outcomes showed consistent overall results with no significant intervention impact on drug use during follow-up. However, with most UDS variables, but not with self-report or "corrected self-report," we observed a significant gender-ethnicity interaction with benefit shown in African American women, White women, and Hispanic men. Conclusion: Despite strong associations between candidate measures, we found some important differences in results. Scientific Significance: In this study, we demonstrated the potential utility and impact of combining UDS and self-report data for drug use assessment. Our results suggest possible differences in intervention efficacy by gender and ethnicity, but highlight the need to cautiously interpret observed interactions.

Land TG; Landau AS; Manning SE; Purtill JK; Pickett K; Wakschlag L et al. Who underreports smoking on birth records: A Monte Carlo predictive model with validation. PLoS ONE 7(4): e-article 34853, 2012. (28 refs.)

Background: Research has shown that self-reports of smoking during pregnancy may underestimate true prevalence. However, little is known about which populations have higher rates of underreporting. Availability of more accurate measures of smoking during pregnancy could greatly enhance the usefulness of existing studies on the effects of maternal smoking offspring, especially in those populations where underreporting may lead to underestimation of the impact of smoking during pregnancy. Methods and Findings: In this paper, we develop a statistical Monte Carlo model to estimate patterns of underreporting of smoking during pregnancy, and apply it to analyze the smoking self-report data from birth certificates in the state of Massachusetts. Our results illustrate non-uniform patterns of underreporting of smoking during pregnancy among different populations. Estimates of likely underreporting of smoking during pregnancy were highest among mothers who were college-educated, married, aged 30 years or older, employed full-time, and planning to breastfeed. The model's findings are validated and compared to an existing underreporting adjustment approach in the Maternal and Infant Smoking Study of East Boston (MISSEB). Conclusions: The validation results show that when biological assays are not available, the Monte Carlo method proposed can provide a more accurate estimate of the smoking status during pregnancy than self-reports alone. Such methods hold promise for providing a better assessment of the impact of smoking during pregnancy.

Lee MR; Chassin L; MacKinnon D. The effect of marriage on young adult heavy drinking and its mediators: Results from two methods of adjusting for selection Into marriage. Psychology of Addictive Behaviors 24(4): 712-718, 2010. (26 refs.)

This study tested the effect of marriage on young adult heavy drinking and tested whether this effect was mediated by involvement in social activities, religiosity, and self-control reasons for limiting drinking. The sample of 508 young adults was taken from an ongoing longitudinal study of familial alcoholism that over-sampled children of alcoholics (Chassin, Rogosch. & Barrera, 1991). In order to distinguish role socialization effects of marriage from confounding effects of role selection into marriage, analyses used both the analysis of covariance (ANCOVA) method and the change score method of adjusting for pre-marriage levels of heavy drinking and the mediators. Results showed role socialization effects of marriage on post-marriage declines in heavy drinking. This effect was mediated by involvement in social activities such that marriage predicted decreased involvement in social activities, which in turn predicted decreased heavy drinking. There were no statistically significant mediated effects of religiosity. The mediated effect of self-control reasons for limiting drinking was supported by the ANCOVA method only, and further investigation suggested that this result was detected erroneously due to violation of an assumption of the ANCOVA method that is not shared by the change score method. Findings from this study offer an explanation for the maturing out of heavy drinking that takes place for some individuals over the course of young adulthood. Methodologically, results suggest that the ANCOVA method should be employed with caution, and that the change score method is a viable approach to confirming results from the ANCOVA method.

Li YM; Wiley EP; Heitjan DF. Statistical analysis of daily smoking status in smoking cessation clinical trials. Addiction 106(11): 2039-2046, 2011. (32 refs.)

Aims: Smoking cessation trials generally record information on daily smoking behavior, but base analyses on measures of smoking status at the end of treatment (EOT). We present an alternative approach that analyzes the entire sequence of daily smoking status observations. Methods We analyzed daily abstinence data from a smoking cessation trial, using two longitudinal logistic regression methods: a mixed-effects (ME) model and a generalized estimating equations (GEE) model. We compared results to a standard analysis that takes abstinence status at EOT as outcome. We evaluated time-varying covariates (smoking history and time-varying drug effect) in the longitudinal analysis and compared ME and GEE approaches. Results We observed some differences in the estimated treatment effect odds ratios across models, with narrower confidence intervals under the longitudinal models. GEE yields similar results to ME when only baseline factors appear in the model, but gives biased results when one includes time-varying covariates. The longitudinal models indicate that the quit probability declines and the drug effect varies over time. Both the previous day's smoking status and recent smoking history predict quit probability, independently of the drug effect. Conclusion When analysing outcomes of studies from smoking cessation interventions, longitudinal models with multiple outcome data points, rather than just end of treatment, can makes efficient use of the data and incorporate time-varying covariates. The generalized estimating equations approach should be avoided when using time-varying predictors.

Loughran H; McCann ME. A case for developing community drug indicators. Social Indicators Research 102(2): 229-244, 2011. (28 refs.)

The EU Action Plan on Drugs (2005-2008) calls for member states of the European Union to provide information on five key epidemiological indicators. These are: general population surveys, prevalence and patterns of problem drug use, drug related infectious diseases, drug related deaths and mortality of drug users, and demand for drug treatment. The goal is to improve the comparability of data across the Member States, which is a central task of the EMCDDA (European Monitoring Centre for Drugs and Drug Addiction). Ireland has made progress on a national level in meeting this obligation. Currently the core information systems used to monitor the drugs problem in Ireland and to inform policy making are in the health and law enforcement areas including treatment, mortality and crime data. The dominance of such objective indicators and treatment outcome measures has contributed to obscuring the view of communities experiencing drugs problems on a day to day basis. The data are summations of the individual experience of drug problems and contribute little to understanding the broader question of how drug problem effect communities. This article draws on a community drugs study to review the contribution of traditional indicators of drug problems and consider some of the limitations of this data. It then presents an analysis of community data to identify possible community indicators of drug problems.

McCabe SE; Hughes TL; Bostwick W; Morales M; Boyd CJ. Measurement of sexual identity in surveys: Implications for substance abuse research. Archives of Sexual Behavior 41(3): 649-657, 2012. (63 refs.)

Researchers are increasingly recognizing the need to include measures of sexual orientation in health studies. However, relatively little attention has been paid to how sexual identity, the cognitive aspect of sexual orientation, is defined and measured. Our study examined the impact of using two separate sexual identity question formats: a three-category question (response options included heterosexual, bisexual, or lesbian/gay), and a similar question with five response options (only lesbian/gay, mostly lesbian/gay, bisexual, mostly heterosexual, only heterosexual). A large probability-based sample of undergraduate university students was surveyed and a randomly selected subsample of participants was asked both sexual identity questions. Approximately one-third of students who identified as bisexual based on the three-category sexual identity measure chose "mostly heterosexual" or "mostly lesbian/gay" on the five-category measure. In addition to comparing sample proportions of lesbian/gay, bisexual, or heterosexual participants based on the two question formats, rates of alcohol and other drug use were also examined among the participants. Substance use outcomes among the sexual minority subgroups differed based on the sexual identity question format used: bisexual participants showed greater risk of substance use in analyses using the three-category measure whereas "mostly heterosexual" participants were at greater risk when data were analyzed using the five-category measure. Study results have important implications for the study of sexual identity, as well as whether and how to recode responses to questions related to sexual identity.

McCoy TP; Ip EH; Blocker JN; Champion H; Rhodes SD; Wagoner KG et al. Attrition bias in a US internet survey of alcohol use among college freshmen. Journal of Studies on Alcohol and Drugs 70(4): 606-614, 2009. (38 refs.)

Objective: Attrition bias is an important issue in survey research on alcohol, tobacco, and other drug use. The issue is even more salient for Internet studies, because these studies often have higher rates of attrition than face-to-face or telephone surveys, and there is limited research examining the issue in the field of drug usage, specifically for college underclassmen. This study assessed whether measures of high-risk drinking and alcohol-related consequences were related to attrition groups ("stayers" or "leavers") in a cohort of college freshmen. Method: Data were collected in 2003 and 2004 from 2,144 first-year college students at 10 universities in the southeastern United States. Demographics, indicators of high-risk drinking, and alcohol-related consequences were compared between cohort stayers and leavers in statistical analyses using two methods. Results: Analyses indicated that cohort leavers reported significantly higher levels of high-risk drinking (past-30-day heavy episodic drinking, weekly drunkenness) and past-30-day smoking but not significantly increased alcohol-related consequences. The directionality of bias was modestly consistent across outcomes and comparison methods. Conclusions: The current study's findings suggest that intervention efforts to reduce smoking or high-risk drinking need to consider attrition bias during study follow-up or account for it in analyses.

McMillan GP; Bedrick E; C'deBaca J. A Bayesian model for estimating the effects of drug use when drug use may be under-reported. Addiction 104(11): 1820-1826, 2009. (15 refs.)

Aims: We present a statistical model for evaluating the effects of substance use when substance use might be under-reported. The model is a special case of the Bayesian formulation of the 'classical' measurement error model, requiring that the analyst quantify prior beliefs about rates of under-reporting and the true prevalence of substance use in the study population. Design: Prospective study. Setting: A diversion program for youths on probation for drug-related crimes. Participants: A total of 257 youths at risk for re-incarceration. Measurements: The effects of true cocaine use on recidivism risks while accounting for possible under-reporting. Findings: The proposed model showed a 60% lower mean time to re-incarceration among actual cocaine users. This effect size is about 75% larger than that estimated in the analysis that relies only on self-reported cocaine use. Sensitivity analysis comparing different prior beliefs about prevalence of cocaine use and rates of under-reporting universally indicate larger effects than the analysis that assumes that everyone tells the truth about their drug use. Conclusion: The proposed Bayesian model allows one to estimate the effect of actual drug use on study outcome measures.

McPherson S; Barbosa-Leiker C; Burns GL; Howell D; Roll J. Missing data in substance abuse treatment research: Current methods and modern approaches. Experimental And Clinical Psychopharmacology 20(3): 243-250, 2012. (23 refs.)

Two common procedures for the treatment of missing information, listwise deletion and positive urine analysis (UA) imputation (e.g., if the participant fails to provide urine for analysis, then score the UA positive), may result in significant biases during the interpretation of treatment effects. To compare these approaches and to offer a possible alternative, these two procedures were compared to the multiple imputation (MI) procedure with publicly available data from a recent clinical trial. Listwise deletion, single imputation (i.e., positive UA imputation), and MI missing data procedures were used to comparatively examine the effect of two different buprenorphine/naloxone tapering schedules (7- or 28-days) for opioid addiction on the likelihood of a positive UA (Clinical Trial Network 0003; Ling et al., 2009). The listwise deletion of missing data resulted in a nonsignificant effect for the taper while the positive UA imputation procedure resulted in a significant effect, replicating the original findings by Ling et al. (2009). Although the MI procedure also resulted in a significant effect, the effect size was meaningfully smaller and the standard errors meaningfully larger when compared to the positive UA procedure. This study demonstrates that the researcher can obtain markedly different results depending on how the missing data are handled. Missing data theory suggests that listwise deletion and single imputation procedures should not be used to account for missing information, and that MI has advantages with respect to internal and external validity when the assumption of missing at random can be reasonably supported.

Nahra TA; Mendez D; Alexander JA. Employing super-efficiency analysis as an alternative to DEA: An application in outpatient substance abuse treatment. European Journal of Operational Research 196(3): 1097-1106, 2009. (78 refs.)

A common technique for conducting efficiency analyses consists of a two-stage procedure that combines data envelopment analysis (DEA) with Tobit regression. As the DEA scores are censored at one, this method has the drawback of masking important information at the upper tail of the distribution of scores. In this paper, we present a DEA-based methodology for a two-stage efficiency analysis where the upper bound constraint of one for the efficiency scores is relaxed. This method, super-efficiency DEA, is contrasted with the two-stage approach that employs traditional, bounded DEA scores. We use data from the National Drug Abuse Treatment Survey to examine how the relative efficiency of the treatment units is affected by the organizational structures, operating characteristics and treatment modalities of a nationally representative sample of outpatient substance abuse treatment units. Our results show that the super-efficiency DEA approach offers advantages over the traditional methodology. It is easy to implement, and, for the same sample size provides more information.

Nunes EV. The design and analysis of multisite effectiveness trials: A decade of progress in the National Drug Abuse Clinical Trials Network. (editorial). American Journal of Drug and Alcohol Abuse 37(5): 269-272, 2011. (28 refs.)

Oden NL; VanVeldhuisen PC; Wakim PG; Trivedi MH; Somoza E; Lewis D. Power of automated algorithms for combining time-line follow-back and urine drug screening test results in stimulant-abuse clinical trials. American Journal of Drug and Alcohol Abuse 37(5): 350-357, 2011. (11 refs.)

Background: In clinical trials of treatment for stimulant abuse, researchers commonly record both Time-Line Follow-Back (TLFB) self-reports and urine drug screen (UDS) results. Objectives: To compare the power of self-report, qualitative (use vs. no use) UDS assessment, and various algorithms to generate self-report-UDS composite measures to detect treatment differences via t-test in simulated clinical trial data. Methods: We performed Monte Carlo simulations patterned in part on real data to model self-report reliability, UDS errors, dropout, informatively missing UDS reports, incomplete adherence to a urine donation schedule, temporal correlation of drug use, number of days in the study period, number of patients per arm, and distribution of drug-use probabilities. Investigated algorithms include maximum likelihood and Bayesian estimates, self-report alone, UDS alone, and several simple modifications of self-report (referred to here as ELCON algorithms) which eliminate perceived contradictions between it and UDS. Results: Among the algorithms investigated, simple ELCON algorithms gave rise to the most powerful t-tests to detect mean group differences in stimulant drug use. Conclusions: Further investigation is needed to determine if simple, naive procedures such as the ELCON algorithms are optimal for comparing clinical study treatment arms. But researchers who currently require an automated algorithm in scenarios similar to those simulated for combining TLFB and UDS to test group differences in stimulant use should consider one of the ELCON algorithms. Scientific Significance: This analysis continues a line of inquiry which could determine how best to measure outpatient stimulant use in clinical trials (National Institute on Drug Abuse. National Institute on Drug Abuse Monograph-57: Self-Report Methods of Estimating Drug Abuse: Meeting Current Challenges to Validity. NTIS PB 88248083. Bethesda, MD: National Institutes of Health, 1985; National Institute on Drug Abuse. National Institute on Drug Abuse Research Monograph 73: Urine Testing for Drugs of Abuse. NTIS PB 89151971. Bethesda, MD: National Institutes of Health, 1987; National Institute on Drug Abuse. National Institute on Drug Abuse Research Monograph 167: The Validity of Self-Reported Drug Use: Improving the Accuracy of Survey Estimates. NTIS PB 97175889. GPO 017-024-01607-1. Bethesda, MD: National Institutes of Health, 1997).

Piper ME; Loh WY; Smith SS; Japuntich SJ; Baker TB. Using decision tree analysis to identify risk factors for relapse to smoking. Substance Use & Misuse 46(4): 492-510, 2011. (75 refs.)

This research used classification tree analysis and logistic regression models to identify risk factors related to short- and long-term abstinence. Baseline and cessation outcome data from two smoking cessation trials, conducted from 2001 to 2002 in two Midwestern urban areas, were analyzed. There were 928 participants (53.1%% women, 81.8%% White) with complete data. Both analyses suggest that relapse risk is produced by interactions of risk factors and that early and late cessation outcomes reflect different vulnerability factors. The results illustrate the dynamic nature of relapse risk and suggest the importance of efficient modeling of interactions in relapse prediction.

Poulsen PB; Dollerup J; Moller AM. Is a percentage a percentage? Systematic review of the effectiveness of Scandinavian behavioural modification smoking cessation programmes. (review). Clinical Respiratory Journal 4(1): 3-12, 2010. (46 refs.)

Introduction: Tobacco smoke is the leading preventable cause of death in the world. A total of 50% of all smokers will die from a smoking-related disease with a major impact upon quality of life and health-care costs. Tobacco-controlling policies, including smoking cessation, have increasingly been implemented across European countries. Reported effectiveness data on smoking cessation interventions are important for decision making. Objective: This study aimed to conduct a literature review on how the effectiveness (quit rates) of behavioural modification smoking cessation programmes (BMSCPs) - counselling, quitlines and quit-and-win contests - were analysed in Denmark, Sweden and Norway. Methods: A systematic review was carried out by using the search engines Medline (U.S. National Library of Medicine, Bethesda, MD, USA), Cinahl (CINAHL Information Systems, EBSCO Industries, Ipswich, MA, USA), Embase (Elsevier, New York, NY, USA) and the grey literature. Following the Russell Standards, studies were selected according to design, analysis of data [intention-to-treat (ITT)/per protocol (PP)], documentation of abstinence and length of follow-up. Cochrane reviews of pharmacological studies were used as the benchmark. Results: Although ITT analysis is the standard scientific approach advocated, most studies of BMSCPs reviewed were analysed by using the PP approach and were based on self-reported point prevalence estimates. This resulted in the reported 1-year quit rates between 16%-45% (PP) and 9%-23% (ITT). In contrast, pharmacological studies are conservative, as they are randomised, use ITT analysis and have continuous quit rates with biochemical verification of abstinence. Conclusion: This literature review reveals that quit rates of smoking cessation interventions are not always comparable. Scandinavian BMSCPs reported optimistic quit rates, confirmed by Cochrane literature review criteria. Care should be exercised when comparing smoking cessation interventions.

Prochaska JO. Commentary on Sendzik et al. What do we know about unplanned quit attempts: Practically nothing or nothing practical? Addiction 106(11): 2014-2015, 2011. (6 refs.)

The author raises questions about the methodology and data analysis of the study reported in this issue, "Planned quit attempts among Ontario smokers: Impact on abstinence." Among the questions raised is what constitutes a "planned" quit attempt versus a spontaneous effort. Questions are also raised about the range of aids cited. A response by the study authors follows.

Raiff BR; Faix C; Turturici M; Dallery J. Breath carbon monoxide output is affected by speed of emptying the lungs: Implications for laboratory and smoking cessation research. Nicotine & Tobacco Research 12(8): 834-838, 2010. (16 refs.)

Introduction: Researchers have used breath carbon monoxide (CO) cutoff values ranging from 4 to 10 ppm to define abstinence in cigarette-smoking cessation research and reductions in CO as a measure of acute abstinence in laboratory research. The current study used a reversal design to investigate effects of exhalation speed on CO output in four groups (non-, light, moderate, and heavy smokers; n = 20 per group). Methods: In one condition, participants were instructed to empty their lungs as quickly as possible (fast), whereas in a different condition, participants were instructed to empty their lungs at a slow pace (slow). Conditions were counterbalanced and repeated twice for each participant. Results: For all groups, speed of exhalation was significantly lower during the slow condition than during the fast condition, and CO output was significantly higher during the slow condition than during the fast condition. Sensitivity and specificity analyses revealed that the optimal CO cutoff for smoking abstinence was 3 ppm during the fast condition versus 4 ppm during the slow condition. Additionally, when heavy smokers switched from exhaling slow to exhaling fast, they showed an approximately 30% reduction in CO. Discussion: The results suggest that exhalation speed should be monitored when CO is used as a measure of smoking status for laboratory and smoking cessation research. If exhalation speed is not monitored when using CO to verify smoking cessation, then more conservative CO cutoff values should be used to avoid false negative CO readings.

Resnicow K; Zhang NH; Vaughan RD; Reddy SP; James S; Murray DM. When intraclass correlation coefficients go awry: A case study from a school-based smoking prevention study in South Africa. American Journal of Public Health 100(9): 1714-1718, 2010. (21 refs.)

Objectives. We conducted a group randomized trial of 2 South African school-based smoking prevention programs and examined possible sources and implications of why our actual intraclass correlation coefficients (ICCs) were significantly higher than the ICC of 0.02 used to compute initial sample size requirements. Methods. Thirty-six South African high schools were randomly assigned to 1 of 3 experimental groups. On 3 occasions, students completed questionnaires on tobacco and drug use attitudes and behaviors. We used mixed-effects models to partition individual and school-level variance components, with and without covariate adjustment. Results. For 30-day smoking, unadjusted ICCs ranged from 0.12 to 0.17 across the 3 time points. For lifetime smoking, ICCs ranged from 0.18 to 0.22; for other drug use variables, 0.02 to 0.10; and for psychosocial variables, 0.09 to 0.23. Covariate adjustment substantially reduced most ICCs. Conclusions. The unadjusted ICCs we observed for smoking behaviors were considerably higher than those previously reported. This effectively reduced our sample size by a factor of 17. Future studies that anticipate significant cluster-level racial homogeneity may consider using higher-value ICCs in sample-size calculations to ensure adequate statistical power.

Schmidt CM; Tauchmann H. Heterogeneity in the intergenerational transmission of alcohol consumption: A quantile regression approach. Journal of Health Economics 30(1): 33-42, 2011. (24 refs.)

This paper addresses the question of whether the effect of parental drinking on children's later consumption of alcohol - which is frequently found to be of positive sign - exhibits a certain pattern of heterogeneity. In particular, if this effect is more prominent in the upper tail than elsewhere in the distribution of children's alcohol consumption, conventional regression analyses that focus on the mean effect may substantially underrate parental drinking as a risk factor for children's later alcohol abuse. In our empirical application, we address this issue by applying censored quantile regression methods to German survey data. The supposed pattern of heterogeneity is indeed found in the data, at least for daily parental drinking. In addition, the intergenerational transmission of alcohol consumption exhibits gender-specific heterogeneity.

Schuckit MA; Smith TL; Trim RS; Tolentino NJ; Hall SA. Comparing structural equation models that use different measures of the level of response to alcohol. Alcoholism: Clinical and Experimental Research 34(5): 861-868, 2010. (50 refs.)

Background: The two measures of a low level of response (LR) to alcohol, an alcohol challenge and the retrospective Self-Report of the Effects of Alcohol questionnaire (SRE), each identify individuals at high risk for heavy drinking and alcohol problems. These measures also perform similarly in identifying subjects with unique functional brain imaging characteristics. However, few data are available regarding whether alcohol challenge-based and SRE-based LRs operate similarly in structural equation models (SEMs) that search for characteristics, which help to mediate how LR impacts alcohol outcomes. Methods: Two hundred and ninety-four men from the San Diego Prospective Study were evaluated for their LR to alcohol using alcohol challenges at similar to age 20. At similar to age 35, the same subjects filled out the SRE regarding the number of drinks needed for effects 15 to 20 years earlier. The two different LR scores for these men were used in SEM analyses evaluating how LR relates to future heavy drinking and to drinking in peers (PEER), alcohol expectancies (EXPECT), and drinking to cope (COPE) as potential mediators of the LR to drinking pattern (ALCOUT) relationships. Results: While the 2 LR measures that were determined 15 years apart related to each other at a modest level (r = 0.17, p < 0.01), the SEM results were similar regardless of the LR source. In both alcohol challenge-based and SRE-based LR models, LR related directly to ALCOUT, with partial mediation from PEER and COPE, but not through EXPECT in these 35-year-old men. Conclusions: Consistent with the > 60% overlap in prediction of outcomes for the 2 LR measures, and with results from functional brain imaging, alcohol challenge- and SRE-based LR values operated similarly in SEM models in these men.

Thomas DP; Fitz JW; Johnston V; Townsend J; Kneebone W. Wholesale data for surveillance of Australian Aboriginal tobacco consumption in the Northern Territory. Tobacco Control 20(4): 291-295, 2011. (31 refs.)

Objectives Effective monitoring of trends in tobacco use is an essential element of tobacco control policy. Monitoring tobacco consumption using tobacco wholesale data has advantages over other methods of surveillance. In the present work, a research project that monitored tobacco consumption in 25 remote Aboriginal communities and its translation to a policy to implement this monitoring routinely in the entire Northern Territory of Australia is described. Methods. Tobacco consumption and trends were estimated using wholesale (or occasionally sales) data from all retail outlets in 25 remote Aboriginal communities. Self-reported consumption was estimated from the National Aboriginal and Torres Strait Islander Social Survey in 2008. Local consumption results were fed back in posters to local organisations and health staff. Results. Estimates of consumption from wholesale data and self-report were similar (6.8 and 6.7 cigarettes/day/person aged 15 and over). Consumption was higher in the tropical Top End than in arid Central Australia, and 24% of tobacco was consumed as loose tobacco. The overall trend in monthly consumption was not significantly different from 0. Local communities could be ranked by their local trends in monthly consumption. Conclusions. Monitoring tobacco consumption using wholesale tobacco data is a practical and unobtrusive surveillance method that is being introduced as a new condition of tobacco retail licenses in the Northern Territory of Australia. It overcomes some problems with consumption estimates from routine surveys, enables rapid feedback and use of results and is particularly well suited for hard-to-reach discrete populations, such as remote Aboriginal communities in Australia. It has already been used to evaluate the impact of local tobacco control activities.

Torvik FA; Rognmo K; Tambs K. Alcohol use and mental distress as predictors of non-response in a general population health survey: The HUNT study. Social Psychiatry and Psychiatric Epidemiology 47(5): 805-816, 2012. (52 refs.)

To investigate to what degree alcohol use and mental distress are associated with non-response in a population-based health study. From 1995 to 1997, 91,488 persons were invited to take part in a health study at Nord-Trondelag, Norway, and the response rate was 69.2%. Demographics were available for everyone. Survey answers from a previous survey were available for most of the participants and a majority of non-participants. In addition, the survey responses from spouses and children of the invitees were used to predict participation in the aforementioned study. Crude and adjusted ORs for a number of predictors, among these alcohol consumption and mental distress, are reported. Both heavy drinkers (OR = 1.27) and abstainers (OR = 1.64) had a higher probability of dropping out in comparison to people who usually do not drink. High levels of mental distress (OR = 1.84) also predicted attrition. Alcohol use and mental distress are moderately associated with non-response, though probably not a major cause, as controlling for other variables weakened the associations. Nevertheless, the moderate but clear underrepresentation at the crude level of people with high alcohol consumption, abstainers and people with poor mental health should be taken into consideration when interpreting results from health surveys.

Wheeler DC; Waller LA. Comparing spatially varying coefficient models: A case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests. Journal of Geographical Systems 11(1): 1-22, 2009. (24 refs.)

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.

Wicki M; Gustafsson NK; Makela P; Gmel G. Dimensionality of drinking consequences - cross-cultural comparability and stability over time. Addiction Research & Theory 17(1): 2-16, 2009. (40 refs.)

Despite the long tradition for asking about the negative social and health consequences of alcohol consumption in surveys, little is known about the dimensionality of these consequences. Analysing cross-sectional and longitudinal data from the Nordic Taxation Study collected for Sweden, Finland, and Denmark in two waves in 2003 and 2004 by means of an explorative principal component analysis for categorical data (CATPCA), it is tested whether consequences have a single underlying dimension across cultures. It further tests the reliability, replicability, concurrent and predictive validity of the consequence scales. A one-dimensional solution was commonly preferable. Whereas the two-dimensional solution was unable to distinguish clearly between different concepts of consequences, the one-dimensional solution resulted in interpretable, generally very stable scales within countries across different samples and time.

Woods ER; Buka SL; Martin CR; Salganik M; Howard MB; Gueguen JA et al. Assessing youth risk behavior in a clinical trial setting: Lessons from the Infant Health and Development Program. Journal of Adolescent Health 46(5): 429-436, 2010. (36 refs.)

Purpose: The purpose of this article was to describe the use of the Youth Risk Behavior Surveillance System (YRBSS) with known 17-18-year-old patients in follow-up of a multisite randomized clinical trial, and to develop a new scoring algorithm indicating the degree of risk-taking behavior for between-group analyses. Methods: Seventy-five questions from the YRBSS were incorporated into the study questionnaire, with the development of safety plans to guide the disposition of participants. The YRBSS questions were grouped into two categories (with three subdomains each) named problem behaviors (conduct problems, sexual behavior, and suicide/hopelessness) and substance use (cigarettes, alcohol, and marijuana use), with scores for each subdomain indicating high, moderate, and low risk. Results: Of the 677 participants, the safety plan was activated 215 times for 199 (29.4%) of participants. Risk behaviors included binge drinking (149), alcohol/substance use and driving (41), depression (22), hopelessness (37), and suicidal ideation (13; all in the past). No emergency room evaluations were required. The subdomain scaling was analyzed by demographic characteristics, and findings were consistent with the literature; for example, higher rates of conduct problems in males, more suicidal ideation in females, greater sexual risk in African Americans, more substance use in males and whites, and more alcohol use in youth with mothers with higher levels of education. Conclusions: YRBSS can be administered in a research setting with appropriate safety precautions. These results should provide a useful guide to the application of the YRBSS to other adolescent populations in the future.

Ye Y; Kaskutas LA. Using propensity scores to adjust for selection bias when assessing the effectiveness of Alcoholics Anonymous in observational studies. Drug and Alcohol Dependence 104(1-2): 56-64, 2009. (60 refs.)

Background: The effectiveness of Alcoholics Anonymous (AA) is difficult to establish. Observational studies consistently find strong dose-response relationships between AA meeting attendance and abstinence, and the only experimental studies favoring AA have been of 12-step facilitation treatment rather than of AA per se. Pending future randomized trials, this paper uses propensity score (PS) method to address the selection bias that potentially confounds the effect of AA in observational studies. Method: The study followed a treatment sample for 1 year to assess post-treatment AA attendance and abstinence (n = 569). Propensity scores were constructed based on known confounders including motivation, problem severity, and prior help-seeking. AA attendance during the 12-month follow-up period was studied as a predictor of alcohol abstinence for 30 days prior to the follow-up interview. PS stratification and PS matching techniques were used to adjust for the self-select bias associated with respondents' propensity to attend AA. Results: The overall advantage in abstinence initially observed narrowed when adjusted. The odds ratio associated with AA attendance reduced from 3.6 to 3.0 after PS stratification and 2.6 after PS matching to AA-attenders. Support for AA effectiveness was strengthened in the quintile with lower propensity scores and when AA-nonattenders were matched as the target group, but was weakened among those in the higher PS quintiles and when matching to AA-attenders. Discussion: These results confirm the robustness of AA effectiveness overall, because the results for higher abstinence associated with AA attendance following propensity score adjustment remained significant, and the reduction in the magnitude of AA's effect was moderate. However, the effect modification by propensity scores in both PS stratification and PS matching approaches seems to suggest that AA may be most helpful, or matter more, for those with a lower propensity to attend AA. Conversely, for those with a high propensity to go to AA (operationalized as higher motivation, greater problem severity, more prior AA and treatment exposure, etc.), attending AA may not make as much of a difference. It will be important that future studies replicate our results, as this is the first paper to use propensity score adjustment in this context.

Zhang JY; Borland R; Coghill K; Petrovic-Lazarevic S; Young D; Yeh CH et al. Evaluating the effect of health warnings in influencing Australian smokers' psychosocial and quitting behaviours using fuzzy causal network. Expert Systems with Applications 38(6): 6430-6438, 2011. (13 refs.)

This paper explores the application of fuzzy causal networks (FCNs) to evaluating effect of health warnings in influencing Australian smokers' psychosocial and quitting behaviour. The sample data used in this study are selected from the International Tobacco Control Policy Evaluation Survey project. Our research findings have demonstrated that new health warnings implemented in Australia have obvious impacts on smokers' psychosocial and quitting behaviours. FCN is a useful framework to investigate such impacts that overcome the limitation of using traditional statistical techniques, such as linear regression and logistics regression, to analyse non-linear data.