Linda Collins
Linda Collins
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Professor of Social and Behavioral Sciences
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Professional overview
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Linda M. Collins is Professor of Global Public Health in the Department of Social and Behavioral Sciences, with a secondary appointment in the Department of Biostatistics. She earned her B.A. in Psychology at the University of Connecticut and her Ph.D. in Quantitative Psychology at the University of Southern California.
Collins’ research interests are focused on the development, dissemination, and application of the multiphase optimization strategy (MOST), a framework for the optimization of behavioral, biobehavioral, and social-structural interventions. The objective of MOST is to improve intervention effectiveness, efficiency, economy, and scalability. She is currently collaborating on research applying MOST in the areas of smoking cessation, the prevention of excessive drinking and risky sex in college students, and HIV services.
Collins’ research has been funded by the National Institute on Drug Abuse, the National Institute on Alcohol Abuse and Alcoholism, and the National Science Foundation, among others. She has given more than 150 presentations on MOST around the world, and her publications have appeared in journals in the fields of behavioral science, quantitative methodology, medicine, and engineering.
Collins has held tenured faculty positions at the University of Southern California and at Penn State University, where she was Distinguished Professor of Human Development and Family Studies and Director of The Methodology Center. She is a Fellow of the American Psychological Association, the Association for Psychological Science, the Society of Behavioral Medicine, and is a past president of the Society of Multivariate Experimental Psychology and the Society for Prevention Research.
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Education
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BA, Psychology, University of Connecticut, Storrs, CTPhD, Quantitative Psychology, University of Southern California, Los Angeles, CA
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Honors and awards
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Fulbright Specialist, National University of Ireland Galway (2018)Pauline Schmitt Russell Distinguished Career Award, Pennsylvania State University’s College of Health and Human Development (2017)Evan G. and Helen G. Pattishall Outstanding Research Achievement Award, Pennsylvania State University’s College of Health and Human Development (2011)President’s Award, Society for Prevention Research (2004)Faculty Scholar Medal for the Social and Behavioral Sciences, Pennsylvania State University (2000)Psychology Department Teacher of the Year, University of Southern California (1992)Psychology Department Mentorship Award, University of Southern California (1991)Society of Multivariate Experimental Psychology Award for Distinguished Early Career Contributions to Multivariate Behavioral Research (1991)
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Areas of research and study
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Behavioral ScienceCost EffectivenessCost-effective Health Programs and PoliciesDissemination and Implementation of Evidence-based Programs
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Publications
Publications
Modeling multidimensional sexual risk behavior using latent class analysis
AbstractLanza, S. T., & Collins, L. (n.d.).Publication year
2011Page(s)
119-124AbstractUnderstanding the intersection of various dimensions of sexual risk behavior in a population is critical for effective prevention. For example, frequency of sexual intercourse, the number of sexual partners, and inconsistent condom use are three dimensions of behavior that relate to the acquisition of sexually transmitted infections (STI's). Although estimating the differential risk posed by each dimension of behavior can be informative, taking a person-centered approach to modeling sexual risk behavior that incorporates multiple dimensions simultaneously can provide an intuitive and more complete picture of different profiles of behavior that are common in the population. Further, the identification of individual characteristics that predict membership in groups characterized by a profile of high-risk behavior can inform how to target intervention resources. Latent class analysis (LCA) is a latent variable model that can be used to identify risk profiles in empirical data. LCA measures an underlying, or latent, variable using a set of observed variables. The latent variable is made up of subgroups, or latent classes, that account for population heterogeneity. The variables measure dimensions of the latent classes. When applied to sexual risk behavior, each latent class represents a different multidimensional profile of risk. For example, Lanza and Collins (2008) used LCA to identify five latent classes of adolescents in the United States defined by common patterns of sexual risk behavior: Non-daters, Daters, Monogamous, Multipartner Safe, and Multipartner Exposed. A longitudinal extension of this approach, where transitions to more risky stages are estimated, can provide information on stability and change in sexual risk behavior profiles over time. Such a model would provide important insight about sexual risk behavior subgroups that are most at-risk of making a transition to high-risk behavior in the future. In addition, individual characteristics can be incorporated in the model to predict transitions to risky behavior. Identification of risk behavior profiles, modeling transitions between profiles over time, and predicting profile membership and transitions between profiles all have direct implications for prevention of HIV/AIDS and other STI's.New methods for tobacco dependence treatment research
AbstractBaker, T. B., Mermelstein, R., Collins, L., Piper, M. E., Jorenby, D. E., Smith, S. S., Christiansen, B. A., Schlam, T. R., Cook, J. W., & Fiore, M. C. (n.d.).Publication year
2011Journal title
Annals of Behavioral MedicineVolume
41Issue
2Page(s)
192-207AbstractIntroduction: Despite advances in tobacco dependence treatment in the past two decades, progress has been inconsistent and slow. This paper reviews pervasive methodological issues that may contribute to the lack of timely progress in tobacco treatment science including the lack of a dynamic model or framework of the cessation process, inefficient study designs, and the use of distal outcome measures that poorly index treatment effects. The authors then present a phase-based cessation framework that partitions the cessation process into four discrete phases based on current theories of cessation and empirical data. These phases include: (1) Motivation, (2) Precessation, (3) Cessation, and (4) Maintenance. Discussion: Within this framework, it is possible to identify phase-specific challenges that a smoker would encounter while quitting smoking, intervention components that would address these phase-specific challenges, mechanisms via which such interventions would exert their effects, and optimal outcome measures linked to these phase-specific interventions. Investigation of phase-based interventions can be accelerated by using efficient study designs that would permit more timely development of an optimal smoking cessation treatment package.Replication in Prevention Science
AbstractValentine, J. C., Biglan, A., Boruch, R. F., Castro, F. G., Collins, L., Flay, B. R., Kellam, S., Mościcki, E. K., & Schinke, S. P. (n.d.).Publication year
2011Journal title
Prevention ScienceVolume
12Issue
2Page(s)
103-117AbstractReplication research is essential for the advancement of any scientific field. In this paper, we argue that prevention science will be better positioned to help improve public health if (a) more replications are conducted; (b) those replications are systematic, thoughtful, and conducted with full knowledge of the trials that have preceded them; and (c) state-of-the art techniques are used to summarize the body of evidence on the effects of the interventions. Under real-world demands it is often not feasible to wait for multiple replications to accumulate before making decisions about intervention adoption. To help individuals and agencies make better decisions about intervention utility, we outline strategies that can be used to help understand the likely direction, size, and range of intervention effects as suggested by the current knowledge base. We also suggest structural changes that could increase the amount and quality of replication research, such as the provision of incentives and a more vigorous pursuit of prospective research registers. Finally, we discuss methods for integrating replications into the roll-out of a program and suggest that strong partnerships with local decision makers are a key component of success in replication research. Our hope is that this paper can highlight the importance of replication and stimulate more discussion of the important elements of the replication process. We are confident that, armed with more and better replications and state-of-the-art review methods, prevention science will be in a better position to positively impact public health.The multiphase optimization strategy for engineering effective tobacco use interventions
AbstractCollins, L., Baker, T. B., Mermelstein, R. J., Piper, M. E., Jorenby, D. E., Smith, S. S., Christiansen, B. A., Schlam, T. R., Cook, J. W., & Fiore, M. C. (n.d.).Publication year
2011Journal title
Annals of Behavioral MedicineVolume
41Issue
2Page(s)
208-226AbstractThe multiphase optimization strategy (MOST) is a new methodological approach for building, optimizing, and evaluating multicomponent interventions. Conceptually rooted in engineering, MOST emphasizes efficiency and careful management of resources to move intervention science forward steadily and incrementally. MOST can be used to guide the evaluation of research evidence, develop an optimal intervention (the best set of intervention components), and enhance the translation of research findings, particularly type II translation. This article uses an ongoing study to illustrate the application of MOST in the evaluation of diverse intervention components derived from the phase-based framework reviewed in the companion article by Baker et al. (Ann Behav Med, in press, 2011). The article also discusses considerations, challenges, and potential benefits associated with using MOST and similar principled approaches to improving intervention efficacy, effectiveness, and cost-effectiveness. The applicability of this methodology may extend beyond smoking cessation to the development of behavioral interventions for other chronic health challenges.A dynamical systems model for understanding behavioral interventions for weight loss
AbstractNavarro-Barrientos, J. E., Rivera, D. E., & Collins, L. (n.d.).Publication year
2010Page(s)
170-179AbstractWe propose a dynamical systems model that captures the daily fluctuations of human weight change, incorporating both physiological and psychological factors. The model consists of an energy balance integrated with a mechanistic behavioral model inspired by the Theory of Planned Behavior (TPB); the latter describes how important variables in a behavioral intervention can influence healthy eating habits and increased physical activity over time. The model can be used to inform behavioral scientists in the design of optimized interventions for weight loss and body composition change.Does individual risk moderate the effect of contextual-level protective factors? a latent class analysis of substance use
AbstractCleveland, M. J., Collins, L., Lanza, S. T., Greenberg, M. T., & Feinberg, M. E. (n.d.).Publication year
2010Journal title
Journal of Prevention and Intervention in the CommunityVolume
38Issue
3Page(s)
213-228AbstractThe current study investigated how individual risk factors interact with social contextual-level protective factors to predict problematic substance use among a sample of 12th-grade students (n=8,879, 53% female). Results suggested six latent classes of substance use: (1) Non-Users; (2) Alcohol Experimenters; (3) Alcohol, Tobacco, and Other Drug (ATOD) Experimenters; (4) Current Smokers; (5) Binge Drinkers; and (6) Heavy Users. Binary logistic regression models provided evidence that individual risk, family, school, and community protective factors were associated with membership in the substance use latent classes. However, the significance of interaction terms suggested that these protective influences differed according to the level of individual risk. Adolescents with high levels of individual risk benefited less from a positive family or neighborhood context than adolescents with low levels of individual risk. These findings suggest that the individual risk factors may undermine the protective effect of parental supervision, discipline, and other family factors, as well as protective aspects of cohesive neighborhoods, among these adolescents. Multi-component and adaptive intervention efforts that account for different levels of ATOD use involvement, as well as distinct profiles of risk and protection, are likely to be most effective in preventing problematic substance use.Latent Class and Latent Transition Analysis : With Applications in the Social, Behavioral, and Health Sciences
AbstractAbstractA modern, comprehensive treatment of latent class and latent transition analysis for categorical data On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical latent variable modeling for both cross-sectional and longitudinal data. The book begins with an introduction to latent class and latent transition analysis for categorical data. Subsequent chapters delve into more in-depth material, featuring: A complete treatment of longitudinal latent class models Focused coverage of the conceptual underpinnings of interpretation and evaluationof a latent class solution Use of parameter restrictions and detection of identification problems Advanced topics such as multi-group analysis and the modeling and interpretation of interactions between covariates The authors present the topic in a style that is accessible yet rigorous. Each method is presented with both a theoretical background and the practical information that is useful for any data analyst. Empirical examples showcase the real-world applications of the discussed concepts and models, and each chapter concludes with a "Points to Remember" section that contains a brief summary of key ideas. All of the analyses in the book are performed using Proc LCA and Proc LTA, the authors' own software packages that can be run within the SAS® environment. A related Web site houses information on these freely available programs and the book's data sets, encouraging readers to reproduce the analyses and also try their own variations. Latent Class and Latent Transition Analysis is an excellent book for courses on categorical data analysis and latent variable models at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the social, behavioral, and health sciences who conduct latent class and latent transition analysis in their everyday work.A prospective longitudinal model of substance use onset among south african adolescents
AbstractPatrick, M. E., Collins, L., Smith, E., Caldwell, L., Flisher, A., & Wegner, L. (n.d.).Publication year
2009Journal title
Substance Use and MisuseVolume
44Issue
5Page(s)
647-662AbstractSubstance use onset among Colored adolescents between eighth and ninth grades in an urban area of Cape Town, South Africa was examined using latent transition analysis. Longitudinal self-report data regarding substance use (N= 1118, 50.9% female) were collected in 2004 and 2005. Results indicated that the pattern of onset was similar across genders; adolescents first tried either alcohol or cigarettes, followed by both, then dagga (cannabis), and then inhalants. The prevalence of lifetime cigarette use was slightly greater for females; dagga (cannabis) and inhalant use were greater for males. The similarity of developmental onset in the current sample to previous international work supports the promise of adapting prevention programs across contexts. The study's limitations are noted.Comparison of a phased experimental approach and a single randomized clinical trial for developing multicomponent behavioral interventions
AbstractCollins, L., Chakraborty, B., Murphy, S. A., & Strecher, V. (n.d.).Publication year
2009Journal title
Clinical TrialsVolume
6Issue
1Page(s)
5-15AbstractBackground: Many interventions in today's health sciences are multicomponent, and often one or more of the components are behavioral. Two approaches to building behavioral interventions empirically can be identified. The more typically used approach, labeled here the classical approach, consists of constructing a likely best intervention a priori, and then evaluating the intervention in a standard randomized controlled trial (RCT). By contrast, the emergent phased experimental approach involves programmatic phases of empirical research and discovery aimed at identifying individual intervention component effects and the best combination of components and levels. Purpose: The purpose of this article is to provide a head-to-head comparison between the classical and phased experimental approaches and thereby highlight the relative advantages and disadvantages of these approaches when they are used to select program components and levels so as to arrive at the most potent intervention. Methods: A computer simulation was performed in which the classical and phased experimental approaches to intervention development were applied to the same randomly generated data. Results: The phased experimental approach resulted in better mean intervention outcomes when the intervention effect size was medium or large, whereas the classical approach resulted in better mean intervention outcomes when the effect size was small. The phased experimental approach led to identification of the correct set of intervention components and levels at a higher rate than the classical approach across all conditions. Limitations: Some potentially important factors were not varied in the simulation, for example the underlying structural model and the number of intervention components. Conclusions: The phased experimental approach merits serious consideration, because it has the potential to enable intervention scientists to develop more efficacious behavioral interventions.Design of Experiments With Multiple Independent Variables : A Resource Management Perspective on Complete and Reduced Factorial Designs
AbstractCollins, L., Dziak, J. J., & Li, R. (n.d.).Publication year
2009Journal title
Psychological MethodsVolume
14Issue
3Page(s)
202-224AbstractAn investigator who plans to conduct an experiment with multiple independent variables must decide whether to use a complete or reduced factorial design. This article advocates a resource management perspective on making this decision, in which the investigator seeks a strategic balance between service to scientific objectives and economy. Considerations in making design decisions include whether research questions are framed as main effects or simple effects; whether and which effects are aliased (confounded) in a particular design; the number of experimental conditions that must be implemented in a particular design and the number of experimental subjects the design requires to maintain the desired level of statistical power; and the costs associated with implementing experimental conditions and obtaining experimental subjects. In this article 4 design options are compared: complete factorial, individual experiments, single factor, and fractional factorial. Complete and fractional factorial designs and single-factor designs are generally more economical than conducting individual experiments on each factor. Although relatively unfamiliar to behavioral scientists, fractional factorial designs merit serious consideration because of their economy and versatility.Developing multicomponent interventions using fractional factorial designs
AbstractChakraborty, B., Collins, L., Strecher, V. J., & Murphy, S. A. (n.d.).Publication year
2009Journal title
Statistics in MedicineVolume
28Issue
21Page(s)
2687-2708AbstractMulticomponent interventions composed of behavioral, delivery, or implementation factors in addition to medications are becoming increasingly common in health sciences. A natural experimental approach to developing and refining such multicomponent interventions is to start with a large number of potential components and screen out the least active ones. Factorial designs can be used efficiently in this endeavor. We address common criticisms and misconceptions regarding the use of factorial designs in these screening studies. We also provide an operationalization of screening studies. As an example, we consider the use of a screening study in the development of a multicomponent smoking cessation intervention. Simulation results are provided to support the discussions.Erratum : Comparison of a phased experimental approach and a single randomized clinical trial for developing multicomponent behavioural interventions (Clinical Trials (2009) vol. 6 (5-15) 10.1177/1740774508100973)
AbstractCollins, L., Chakraborty, B., Murphy, S. A., & Strecher, V. (n.d.).Publication year
2009Journal title
Clinical TrialsVolume
6Issue
3Page(s)
289Abstract~A New SAS Procedure for Latent Transition Analysis : Transitions in Dating and Sexual Risk Behavior
AbstractLanza, S. T., & Collins, L. (n.d.).Publication year
2008Journal title
Developmental psychologyVolume
44Issue
2Page(s)
446-456AbstractThe set of statistical methods available to developmentalists is continually being expanded, allowing for questions about change over time to be addressed in new, informative ways. Indeed, new developments in methods to model change over time create the possibility for new research questions to be posed. Latent transition analysis, a longitudinal extension of latent class analysis, is a method that can be used to model development in discrete latent variables, for example, stage processes, over 2 or more times. The current article illustrates this approach using a new SAS procedure, PROC LTA, to model change over time in adolescent and young adult dating and sexual risk behavior. Gender differences are examined, and substance use behaviors are included as predictors of initial status in dating and sexual risk behavior and transitions over time.Engineering control approaches for the design and analysis of adaptive behavioral interventions
AbstractRivera, D. E., & Collins, L. (n.d.).Publication year
2008Abstract~Tobacco, alcohol, and marijuana use among first-year U.S. college students : A time series analysis
AbstractDierker, L., Stolar, M., Lloyd-Richardson, E., Tiffany, S., Flay, B., Collins, L., Nichter, M., Bailey, S., Clayton, R., Abrams, D. S., Balster, R., Dahl, R., Giovino, G., Henningfield, J., Koob, G., McMahon, R., Merikangas, K., Shiffman, S., Prager, D., … Stroud, L. (n.d.).Publication year
2008Journal title
Substance Use and MisuseVolume
43Issue
5Page(s)
680-699AbstractThe present study sought to evaluate the day-to-day patterns of tobacco, alcohol, and marijuana use among first-year college students in the United States. Using 210 days of weekly time-line follow-back diary data collected in 2002 to 2003, the authors examined within-person patterns of use. The sample was 48% female and 90% Caucasian. Sixty-eight percent of the participants were permanent residents of Indiana. Univariate time series analysis was employed to evaluate behavioral trends for each substance across the academic year and to determine the predictive value of day-to-day substance use. Some of the most common trends included higher levels of substance use at the beginning or end of the academic year. Use on any given day could be predicted best from the amount of corresponding substance use 1 day prior. Conclusions: Although universal intervention might best be focused in the earliest weeks on campus and at the end of the year when substance use is at its highest, the diversity of substance use trajectories suggests the need for more targeted approaches to intervention. Study limitations are noted.Trajectories of smoking among freshmen college students with prior smoking history and risk for future smoking : Data from the University Project Tobacco Etiology Research Network (UpTERN) study
AbstractColder, C. R., Flay, B. R., Segawa, E., Hedeker, D., Abrams, D. B., Agnew, C., Balster, R. L., Clayton, R. R., Collins, L., Dahl, R. E., Dierker, L. C., Donny, E. C., Dorn, L., Eissenberg, T., Flaherty, B. P., Giovino, G. A., Henningfield, J., Koob, G. F., Liang, L., … Tiffany, S. (n.d.).Publication year
2008Journal title
AddictionVolume
103Issue
9Page(s)
1534-1543AbstractAims: Little is known about smoking during the transition to college. The current study examined trajectories of smoking among college freshmen, how trajectories predicted later smoking and the social context of smoking. Design: Weekly assessments of daily smoking were collected via the web during the first year of college for a large cohort with a previous history of smoking. Participants and setting: A total of 193 college freshmen from a large public university with a previous history of smoking who smoked frequently enough to be included in trajectory analysis. Measurements: Measures included weekly reports of daily smoking, family smoking, perceived peer attitudes and smoking, social norms and social smoking environment. Findings: Seven trajectories were identified: one of low-level sporadic smoking, one of low-level smoking with a small increase during the year, two classes with a substantial decrease during the year, two classes with relatively small decreases and one class with a substantial increase in smoking. Trajectories of smoking in the freshman year predicted levels of sophomore year smoking, and some social context variables tended to change as smoking increased or decreased for a given trajectory class. Conclusions: The transition into college is marked by changes in smoking, with smoking escalating for some students and continuing into the sophomore year. Shifts in social context that support smoking were associated with trajectories of smoking. Despite the focus of developmental models on smoking in early adolescence, the transition into college warrants further investigation as a dynamic period for smoking.Web-Based Smoking-Cessation Programs. Results of a Randomized Trial
AbstractStrecher, V. J., McClure, J. B., Alexander, G. L., Chakraborty, B., Nair, V. N., Konkel, J. M., Greene, S. M., Collins, L., Carlier, C. C., Wiese, C. J., Little, R. J., Pomerleau, C. S., & Pomerleau, O. F. (n.d.).Publication year
2008Journal title
American journal of preventive medicineVolume
34Issue
5Page(s)
373-381AbstractBackground: Initial trials of web-based smoking-cessation programs have generally been promising. The active components of these programs, however, are not well understood. This study aimed to (1) identify active psychosocial and communication components of a web-based smoking-cessation intervention and (2) examine the impact of increasing the tailoring depth on smoking cessation. Design: Randomized fractional factorial design. Setting: Two HMOs: Group Health in Washington State and Henry Ford Health System in Michigan. Participants: 1866 smokers. Intervention: A web-based smoking-cessation program plus nicotine patch. Five components of the intervention were randomized using a fractional factorial design: high- versus low-depth tailored success story, outcome expectation, and efficacy expectation messages; high- versus low-personalized source; and multiple versus single exposure to the intervention components. Measurements: Primary outcome was 7 day point-prevalence abstinence at the 6-month follow-up. Findings: Abstinence was most influenced by high-depth tailored success stories and a high-personalized message source. The cumulative assignment of the three tailoring depth factors also resulted in increasing the rates of 6-month cessation, demonstrating an effect of tailoring depth. Conclusions: The study identified relevant components of smoking-cessation interventions that should be generalizable to other cessation interventions. The study also demonstrated the importance of higher-depth tailoring in smoking-cessation programs. Finally, the use of a novel fractional factorial design allowed efficient examination of the study aims. The rapidly changing interfaces, software, and capabilities of eHealth are likely to require such dynamic experimental approaches to intervention discovery.Consider a CMA as the CFO [3]
AbstractCollins, L. (n.d.).Publication year
2007Journal title
Behavioral HealthcareVolume
27Issue
9Page(s)
10Abstract~Customizing treatment to the patient : Adaptive treatment strategies
AbstractCollins, L., Murphy, S. A., Collins, L. M., & Rush, A. J. (n.d.).Publication year
2007Journal title
Drug and alcohol dependenceVolume
88Issue
SUPPL. 2Page(s)
S1-S3Abstract~Patterns of substance use onset among Hispanics in Puerto Rico and the United States
AbstractMaldonado-Molina, M. M., Collins, L., Lanza, S. T., Prado, G., Ramírez, R., & Canino, G. (n.d.).Publication year
2007Journal title
Addictive BehaviorsVolume
32Issue
10Page(s)
2432-2437AbstractPurpose: Examine patterns of progression in substance use among Hispanic youth 13 to 17 years of age from two longitudinally representative studies. Method: Patterns of substance use among youth in Puerto Rico were examined using a longitudinal study (n = 663) of adolescents living on the island. The National Longitudinal Study of Youth was used to examine patterns of substance use among Hispanics living in the United States (n = 1445). Latent transition analysis was used to estimate the probability of membership in each stage of substance use and incidence of transitions between different substance use stages over time. Results: Six stages best described the heterogeneity in substance use among youth in Puerto Rico. Five stages were sufficient to describe patterns of substance use among youth in the United States. Youth living in Puerto Rico reported lower rates of smoking and illicit drug use, but higher rates of alcohol use, when compared with rates among Hispanics in the United States. Discussion: Similar patterns of substance use were identified for Hispanic youth living in the United States and youth living in Puerto Rico.PROC LCA : A SAS procedure for latent class analysis
AbstractLanza, S. T., Collins, L., Lemmon, D. R., & Schafer, J. L. (n.d.).Publication year
2007Journal title
Structural Equation ModelingVolume
14Issue
4Page(s)
671-694AbstractLatent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.Research Design and Methods
AbstractCollins, L., & Collins, L. M. (n.d.).Publication year
2007Page(s)
V2-433-V2-442Abstract~The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART). New Methods for More Potent eHealth Interventions
AbstractCollins, L., Murphy, S. A., & Strecher, V. (n.d.).Publication year
2007Journal title
American journal of preventive medicineVolume
32Issue
5 SUPPL.Page(s)
S112-S118AbstractAbstract: In this article two new methods for building and evaluating eHealth interventions are described. The first is the Multiphase Optimization Strategy (MOST). It consists of a screening phase, in which intervention components are efficiently identified for inclusion in an intervention or for rejection, based on their performance; a refining phase, in which the selected components are fine tuned and issues such as optimal levels of each component are investigated; and a confirming phase, in which the optimized intervention, consisting of the selected components delivered at optimal levels, is evaluated in a standard randomized controlled trial. The second is the Sequential Multiple Assignment Randomized Trial (SMART), which is an innovative research design especially suited for building time-varying adaptive interventions. A SMART trial can be used to identify the best tailoring variables and decision rules for an adaptive intervention empirically. Both the MOST and SMART approaches use randomized experimentation to enable valid inferences. When properly implemented, these approaches will lead to the development of more potent eHealth interventions.Using engineering control principles to inform the design of adaptive interventions : A conceptual introduction
AbstractRivera, D. E., Pew, M. D., & Collins, L. (n.d.).Publication year
2007Journal title
Drug and alcohol dependenceVolume
88Issue
SUPPL. 2Page(s)
S31-S40AbstractThe goal of this paper is to describe the role that control engineering principles can play in developing and improving the efficacy of adaptive, time-varying interventions. It is demonstrated that adaptive interventions constitute a form of feedback control system in the context of behavioral health. Consequently, drawing from ideas in control engineering has the potential to significantly inform the analysis, design, and implementation of adaptive interventions, leading to improved adherence, better management of limited resources, a reduction of negative effects, and overall more effective interventions. This article illustrates how to express an adaptive intervention in control engineering terms, and how to use this framework in a computer simulation to investigate the anticipated impact of intervention design choices on efficacy. The potential benefits of operationalizing decision rules based on control engineering principles are particularly significant for adaptive interventions that involve multiple components or address co-morbidities, situations that pose significant challenges to conventional clinical practice.A mixture model of discontinuous development in heavy drinking from ages 18 to 30 : The role of college enrollment
AbstractLanza, S. T., & Collins, L. (n.d.).Publication year
2006Journal title
Journal of Studies on AlcoholVolume
67Issue
4Page(s)
552-561AbstractObjective: The purpose of this study was to illustrate the use of latent class analysis to examine change in behavior over time. Patterns of heavy drinking from ages 18 to 30 were explored in a national sample; the relationship between college enrollment and pathways of heavy drinking, particularly those leading to adult heavy drinking, was explored. Method: Latent class analysis for repeated measures is used to estimate common pathways through a stage-sequential process. Common patterns of development in a categorical variable (presence or absence of heavy drinking) are estimated and college enrollment is a grouping variable. Data were from the National Longitudinal Survey of Youth (N = 1,265). Results: Eight patterns of heavy drinking were identified: no heavy drinking (53.7%); young adulthood only (3.7%); young adulthood and adulthood (3.7%); college age only (2.6%); college age, young adulthood, and adulthood (8.7%); high school and college age (4.4%); high school, college age, and young adulthood (6.3%); and persistent heavy drinking (16.9%). Conclusions: We found no evidence that prevalence of heavy drinking for those enrolled in college exceeds the prevalence for those not enrolled at any of the four developmental periods studied. In fact, there is some evidence that being enrolled in college appears to be a protective factor for young adult and adult heavy drinking. College-enrolled individuals more often show a pattern characterized by heavy drinking during college ages only, with no heavy drinking prior to and after the college years, whereas nonenrolled individuals not drinking heavily during high school or college ages are at increased risk for adult heavy drinking.