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
Youths' Substance Use and Changes in Parental Knowledge-Related Behaviors During Middle School: A Person-Oriented Approach
AbstractLippold, M. A., Greenberg, M. T., & Collins, L. M. (n.d.).Publication year
2014Journal title
Journal of Youth and AdolescenceVolume
43Issue
5Page(s)
729-744AbstractParental knowledge is a key protective factor for youths' risky behavior. Little is known about how longitudinal combinations of knowledge-related behaviors are associated with youths' substance use. This longitudinal study uses Latent Transition Analysis to identify latent patterns of parental knowledge-related behaviors occurring in mother-youth dyads during middle school and to investigate how changes in knowledge-related patterns are associated with youths' substance use in Grade 6 and the initiation of substance use from Grade 6 to 8. Using a sample of 536 rural dyads (53 % female, 84 % White), we assessed mother and youths' reports of parental knowledge, active parental monitoring efforts, youth disclosure, and parent-youth communication to identify six latent patterns of knowledge-related behaviors: High Monitors, Low Monitors, Communication-Focused, Supervision-Focused, Maternal Over-Estimators, and Youth Over-Estimators. Fifty percent or more of dyads in the High Monitors, Communication-Focused and Youth Over-Estimators were in the same status in both 6th and 8th grade: 98 % of Low Monitors in Grade 6 were also in this status in Grade 8. The initiation of alcohol, smoking, and marijuana was associated significantly with transitions between patterns of knowledge-related behaviors. The initiation of alcohol and smoking were associated with increased odds of transitions into the Low Monitors from the Communication-Focused, Supervision-Focused, and Maternal Over-Estimators. However, the initiation of substance use was associated with decreased odds of transitions from the High Monitors to the Low Monitors and with increased odds of transitions from High Monitors to Supervision-Focused. The discussion focuses on the value of using a person-oriented dyadic approach with multiple reporters to study changes in knowledge-related behaviors over the middle school period.Parental Knowledge and Youth Risky Behavior: A Person Oriented Approach
AbstractLippold, M. A., Greenberg, M. T., & Collins, L. M. (n.d.).Publication year
2013Journal title
Journal of Youth and AdolescenceVolume
42Issue
11Page(s)
1732-1744AbstractMost studies isolate the effects of one knowledge-related behavior on youth outcomes. This study explores the relationship between subgroups of mother-youth dyads that use specific combinations of parental knowledge-related behaviors and youth risky behavior. Using a sample of 796 rural 6th graders (53 % female), we assessed mother and youth reports of maternal knowledge, active parent monitoring efforts, youth disclosure, parental supervision, and the amount of parent-youth communication to identify five knowledge latent classes: High-Monitors, Maternal Over-Estimators, Low-Monitors, Communication-Focused, and Supervision-Focused. Delinquency, antisocial peers, and substance use were associated with increased odds of membership in the Supervision-Focused class, relative to the High Monitors. Membership in the Low Monitors and Maternal Over-Estimators classes was associated with unhealthy attitudes towards substances and for Low Monitors, substance use. The discussion focuses on the value of using a person-oriented approach to understand parental knowledge and risky behavior during early adolescence and intervention implications.Recruiting and engaging smokers in treatment in a primary care setting: Developing a chronic care model implemented through a modified electronic health record
AbstractPiper, M. E., Baker, T. B., Mermelstein, R., Collins, L. M., Fraser, D. L., Jorenby, D. E., Smith, S. S., Christiansen, B. A., Schlam, T. R., Cook, J. W., Oguss, M., & Fiore, M. C. (n.d.).Publication year
2013Journal title
Translational Behavioral MedicineVolume
3Issue
3Page(s)
253-263AbstractAlmost 35 million U.S. smokers visit primary care clinics annually, creating a need and opportunity to identify such smokers and engage them in evidence-based smoking treatment. The purpose of this study is to examine the feasibility and effectiveness of a chronic care model of treating tobacco dependence when it is integrated into primary care systems using electronic health records (EHRs). The EHR prompted primary care clinic staff to invite patients who smoked to participate in a tobacco treatment program. Patients who accepted and were eligible were offered smoking reduction or cessation treatment. More than 65 % of smokers were invited to participate, and 12.4 % of all smokers enrolled in treatment-30 % in smoking reduction and 70 % in cessation treatment. The chronic care model developed for treating tobacco dependence, integrated into the primary care system through the EHR, has the potential to engage up to 4.3 million smokers in treatment a year.Some methodological considerations in theory-based health behavior research
AbstractCollins, L. M., MacKinnon, D. P., & Reeve, B. B. (n.d.).Publication year
2013Journal title
Health PsychologyVolume
32Issue
5Page(s)
586-591AbstractAs this special issue shows, much research in social and personality psychology is directly relevant to health psychology. In this brief commentary, we discuss three topics in research methodology that may be of interest to investigators involved in health-related psychological research. The first topic is statistical analysis of mediated and moderated effects. The second is measurement of latent constructs. The third is the Multiphase Optimization Strategy, a framework for translation of innovations from social and personality psychology into behavioral interventions.Dynamic energy-balance model predicting gestational weight gain
AbstractThomas, D. M., Navarro-Barrientos, J. E., Rivera, D. E., Heymsfield, S. B., Bredlau, C., Redman, L. M., Martin, C. K., Lederman, S. A., Collins, L. M., & Butte, N. F. (n.d.).Publication year
2012Journal title
American Journal of Clinical NutritionVolume
95Issue
1Page(s)
115-122AbstractBackground: Gestational weight gains (GWGs) that exceed the 2009 Institute of Medicine recommended ranges increase risk of long-term postpartum weight retention; conversely, GWGs within the recommended ranges are more likely to result in positive maternal and fetal outcomes. Despite this evidence, recent epidemiologic studies have shown that the majority of pregnant women gain outside the target GWG ranges. A mathematical model that predicts GWG and energy intake could provide a clinical tool for setting precise goals during early pregnancy and continuous objective feedback throughout pregnancy. Objective: The purpose of this study was to develop and validate a differential equation model for energy balance during pregnancy that predicts GWG that results from changes in energy intakes. Design: A set of prepregnancy BMI - dependent mathematical models that predict GWG were developed by using data from a longitudinal study that measured gestational-changes in fat-free mass, fat mass, total body water, and total energy expenditure in 63 subjects. Results: Mathematical models developed for women with low, normal, and high prepregnancy BMI were shown to fit the original data. In 2 independent studies used for validation, model predictions of fat-free mass, fat mass, and total body water matched actual measurements within 1 kg. Conclusions: Our energy-balance model provides plausible predictions of GWG that results from changes in energy intakes. Because the model was implemented as a Web-based applet, it can be widely used by pregnant women and their health care providers.Multilevel factorial experiments for developing behavioral interventions: Power, sample size, and resource considerations
AbstractDziak, J. J., Nahum-Shani, I., & Collins, L. M. (n.d.).Publication year
2012Journal title
Psychological MethodsVolume
17Issue
2Page(s)
153-175AbstractFactorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions by helping investigators to screen several candidate intervention components simultaneously and to decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the population of interest is multilevel (e.g., when students are nested within schools, or when employees are nested within organizations). In this article, we examine the feasibility of factorial experimental designs with multiple factors in a multilevel, clustered setting (i.e., of multilevel, multifactor experiments). We conduct Monte Carlo simulations to demonstrate how design elements-such as the number of clusters, the number of lower-level units, and the intraclass correlation-affect power. Our results suggest that multilevel, multifactor experiments are feasible for factor-screening purposes because of the economical properties of complete and fractional factorial experimental designs. We also discuss resources for sample size planning and power estimation for multilevel factorial experiments. These results are discussed from a resource management perspective, in which the goal is to choose a design that maximizes the scientific benefit using the resources available for an investigation.Translational Research in South Africa: Evaluating Implementation Quality Using a Factorial Design
AbstractCaldwell, L. L., Smith, E. A., Collins, L. M., Graham, J. W., Lai, M., Wegner, L., Vergnani, T., Matthews, C., & Jacobs, J. (n.d.).Publication year
2012Journal title
Child and Youth Care ForumVolume
41Issue
2Page(s)
119-136AbstractBackground: HealthWise South Africa: Life Skills for Adolescents (HW) is an evidence-based substance use and sexual risk prevention program that emphasizes the positive use of leisure time. Since 2000, this program has evolved from pilot testing through an efficacy trial involving over 7,000 youth in the Cape Town area. Beginning in 2011, through 2015, we are undertaking a new study that expands HW to all schools in the Metro South Education District. Objective: This paper describes a research study designed in partnership with our South African collaborators that examines three factors hypothesized to affect the quality and fidelity of HW implementation: enhanced teacher training; teacher support, structure and supervision; and enhanced school environment. Methods: Teachers and students from 56 schools in the Cape Town area will participate in this study. Teacher observations are the primary means of collecting data on factors affecting implementation quality. These factors address the practical concerns of teachers and schools related to likelihood of use and cost-effectiveness, and are hypothesized to be "active ingredients" related to high-quality program implementation in real-world settings. An innovative factorial experimental design was chosen to enable estimation of the individual effect of each of the three factors. Results: Because this paper describes the conceptualization of our study, results are not yet available. Conclusions: The results of this study may have both substantive and methodological implications for advancing Type 2 translational research.A dynamical model for describing behavioural interventions for weight loss and body composition change
AbstractNavarro-Barrientos, J. E., Rivera, D. E., & Collins, L. M. (n.d.).Publication year
2011Journal title
Mathematical and Computer Modelling of Dynamical SystemsVolume
17Issue
2Page(s)
183-203AbstractWe present a dynamical model incorporating both physiological and psychological factors that predict changes in body mass and composition during the course of a behavioural intervention for weight loss. The model consists of a three-compartment energy balance integrated with a mechanistic psychological model inspired by the Theory of Planned Behaviour. This describes how important variables in a behavioural intervention can influence healthy eating habits and increased physical activity over time. The novelty of the approach lies in representing the behavioural intervention as a dynamical system and the integration of the psychological and energy balance models. Two simulation scenarios are presented that illustrate how the model can improve the understanding of how changes in intervention components and participant differences affect outcomes. Consequently, the model can be used to inform behavioural scientists in the design of optimized interventions for weight loss and body composition change.A risk-based model predictive control approach to adaptive interventions in behavioral health
AbstractZafra-Cabeza, A., Rivera, D. E., Collins, L. M., Ridao, M. A., & Camacho, E. F. (n.d.).Publication year
2011Journal title
IEEE Transactions on Control Systems TechnologyVolume
19Issue
4Page(s)
891-901AbstractThis brief examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based model predictive control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this brief can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm.Commentaries on Replication in Prevention Science: A Rejoinder
Valentine, J. C., Biglan, A., Boruch, R. F., Castro, F. G., Collins, L. M., Flay, B. R., Kellam, S., Mościcki, E. K., & Schinke, S. P. (n.d.).Publication year
2011Journal title
Prevention ScienceVolume
12Issue
2Page(s)
123-125Modeling multidimensional sexual risk behavior using latent class analysis
AbstractLanza, S. T., & Collins, L. M. (n.d.). In Sexual Risk Behaviors (1–).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. M., 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. M., 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. M., 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.Does individual risk moderate the effect of contextual-level protective factors? a latent class analysis of substance use
AbstractCleveland, M. J., Collins, L. M., 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. M., 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. M., 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. M., 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. M., 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.A New SAS Procedure for Latent Transition Analysis: Transitions in Dating and Sexual Risk Behavior
AbstractLanza, S. T., & Collins, L. M. (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.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., Nichter, M., Bailey, S., Clayton, R., Abrams, D., Balster, R., Dahl, R., Giovino, G., Henningfield, J., Koob, G., McMahon, R., Merikangas, K., Shiffman, S., … 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. M., 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. M., 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]
Collins, L. (n.d.). In Behavioral Healthcare (1–).Publication year
2007Volume
27Issue
9Page(s)
10