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
A Bayesian sequential analysis framework for optimization and evaluation of multicomponent interventions
AbstractVanness, D. J., Strayhorn, J. C., & Collins, L. (n.d.).Abstract~A brief engagement intervention adapted for racial and ethnic minority young adults in mental health services: Protocol for a pilot optimization trial.
AbstractMoore, K., Rodwin, A. H., Gwadz, M., Chang, D., Collins, L., & Munson, M. (n.d.).Journal title
Journal of Medical Internet ResearchAbstract~A comparison of inclusive and restrictive strategies in modern missing data procedures
AbstractCollins, L., Schafer, J. L., & Kam, C. M. (n.d.).Publication year
2001Journal title
Psychological MethodsVolume
6Issue
3Page(s)
330-351AbstractTwo classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in comparable ways. In either approach, it is possible to include auxiliary variables solely for the purpose of improving the missing data procedure. A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy, which makes liberal use of such variables. The simulation showed that the inclusive strategy is to be greatly preferred. With an inclusive strategy not only is there a reduced chance of inadvertently omitting an important cause of missingness, there is also the possibility of noticeable gains in terms of increased efficiency and reduced bias, with only minor costs. As implemented in currently available software, the ML approach tends to encourage the use of a restrictive strategy, whereas the MI approach makes it relatively simple to use an inclusive strategy.A conceptual framework for adaptive preventive interventions
AbstractCollins, L., Murphy, S. A., & Bierman, K. L. (n.d.).Publication year
2004Journal title
Prevention ScienceVolume
5Issue
3Page(s)
185-196AbstractRecently, adaptive interventions have emerged as a new perspective on prevention and treatment. Adaptive interventions resemble clinical practice in that different dosages of certain prevention or treatment components are assigned to different individuals, and/or within individuals across time, with dosage varying in response to the intervention needs of individuals. To determine intervention need and thus assign dosage, adaptive interventions use prespecified decision rules based on each participant's values on key characteristics, called tailoring variables. In this paper, we offer a conceptual framework for adaptive interventions, discuss principles underlying the design and evaluation of such interventions, and review some areas where additional research is needed.A dynamical model for describing behavioural interventions for weight loss and body composition change
AbstractNavarro-Barrientos, J. E., Rivera, D. E., & Collins, L. (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 dynamical systems approach to understanding self-regulation in smoking cessation behavior change
AbstractTimms, K. P., Rivera, D. E., Collins, L., & Piper, M. E. (n.d.).Publication year
2014Journal title
Nicotine and Tobacco ResearchVolume
16Issue
SUPPL2Page(s)
S159-S168AbstractIntroduction: Self-regulation, a key component of the addiction process, has been challenging to model precisely in smoking cessation settings, largely due to the limitations of traditional methodological approaches in measuring behavior over time. However, increased availability of intensive longitudinal data (ILD) measured through ecological momentary assessment facilitates the novel use of an engineering modeling approach to better understand self-regulation. Methods: Dynamical systems modeling is a mature engineering methodology that can represent smoking cessation as a selfregulation process. This article shows how a dynamical systems approach effectively captures the reciprocal relationship between day-to-day changes in craving and smoking. Models are estimated using ILD from a smoking cessation randomized clinical trial. Results: A system of low-order differential equations is presented that models cessation as a self-regulatory process. It explains 87.32% and 89.16% of the variance observed in craving and smoking levels, respectively, for an active treatment group and 62.25% and 84.12% of the variance in a control group. The models quantify the initial increase and subsequent gradual decrease in craving occurring postquit as well as the dramatic quit-induced smoking reduction and postquit smoking resumption observed in both groups. Comparing the estimated parameters for the group models suggests that active treatment facilitates craving reduction and slows postquit smoking resumption. Conclusions: This article illustrates that dynamical systems modeling can effectively leverage ILD in order to understand selfregulation within smoking cessation. Such models quantify group-level dynamic responses in smoking cessation and can inform the development of more effective interventions in the future.A dynamical systems model for improving gestational weight gain behavioral interventions
AbstractDong, Y., Rivera, D. E., Thomas, D. M., Navarro-Barrientos, J. E., Downs, D. S., Savage, J. S., & Collins, L. (n.d.).Publication year
2012Page(s)
4059-4064AbstractExcessive gestational weight gain (GWG) represents a major public health concern. In this paper, we present a dynamical systems model that describes how a behavioral intervention can influence weight gain during pregnancy. The model relies on the integration of a mechanistic energy balance with a dynamical behavioral model. The behavioral model incorporates some well-accepted concepts from psychology: the Theory of Planned Behavior (TPB) and the principle of self-regulation which describes how internal processes within the individual can serve to reinforce the positive outcomes of an intervention. A hypothetical case study is presented to illustrate the basic workings of the model and demonstrate how the proper design of the intervention can counteract natural trends towards declines in healthy eating and reduced physical activity during the course of pregnancy. The model can be used by behavioral scientists to evaluate decision rules for adaptive time-varying behavioral interventions, or as the open-loop model for hybrid model predictive control algorithms acting as decision frameworks for such interventions.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.A Factorial Experiment to Optimize Remotely Delivered Behavioral Treatment for Obesity : Results of the Opt-IN Study
AbstractSpring, B., Pfammatter, A. F., Marchese, S. H., Stump, T., Pellegrini, C., McFadden, H. G., Hedeker, D., Siddique, J., Jordan, N., & Collins, L. (n.d.).Publication year
2020Journal title
ObesityVolume
28Issue
9Page(s)
1652-1662AbstractObjective: Intensive behavioral obesity treatments face scalability challenges, but evidence is lacking about which treatment components could be cut back without reducing weight loss. The Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity (Opt-IN) study applied the Multiphase Optimization Strategy to develop an entirely remotely delivered, technology-supported weight-loss package to maximize the amount of weight loss attainable for ≤$500. Methods: Six-month weight loss was examined among adults (N = 562) with BMI ≥ 25 who were randomly assigned to conditions in a factorial experiment crossing five dichotomous treatment components set to either low/high (12 vs. 24 coaching calls) or off/on (primary care provider reports, text messaging, meal replacements, and buddy training). Results: About 84.3% of participants completed the final assessment. The treatment package yielding maximum weight loss for ≤$500 included 12 coaching calls, buddy training, and primary care provider progress reports; produced average weight loss of 6.1 kg, with 57.1% losing ≥5% and 51.8% losing ≥7%; and cost $427 per person. The most expensive candidate-treatment component (24 vs. 12 coaching calls) was screened out of the optimized treatment package because it did not increase weight loss. Conclusions: Systematically testing each treatment component’s effect on weight loss made it possible to eliminate more expensive but less impactful components, yielding an optimized, resource-efficient obesity treatment for evaluation in a randomized controlled trial.A Hybrid Evaluation-Optimization Trial to Evaluate an Intervention Targeting the Intersection of Alcohol and Sex in College Students and Simultaneously Test an Additional Component Aimed at Preventing Sexual Violence
AbstractTanner, A. E., Guastaferro, K. M., Rulison, K. L., Wyrick, D. L., Milroy, J. J., Bhandari, S., Thorpe, S., Ware, S., Miller, A. M., & Collins, L. (n.d.).Publication year
2021Journal title
Annals of Behavioral MedicineVolume
55Issue
12Page(s)
1184-1187AbstractBackground: Using the multiphase optimization strategy (MOST), we previously developed and optimized an online behavioral intervention, itMatters, aimed at reducing the risk of sexually transmitted infections (STI) among first-year college students by targeting the intersection of alcohol use and sexual behaviors. Purpose: We had two goals: (a) to evaluate the optimized itMatters intervention and (b) to determine whether the candidate sexual violence prevention (SVP) component (included at the request of participating universities) had a detectable effect and therefore should be added to create a new version of itMatters. We also describe the hybrid evaluation-optimization trial we conducted to accomplish these two goals in a single experiment. Methods: First year college students (N = 3,098) at four universities in the USA were individually randomized in a hybrid evaluation-optimization 2 × 2 factorial trial. Data were analyzed using regression models, with pre-test outcome variables included as covariates in the models. Analyses were conducted separately with (a) immediate post-test scores and (b) 60-day follow-up scores as outcome variables. Results: Experimental results indicated a significant effect of itMatters on targeted proximal outcomes (norms) and on one distal behavioral outcome (binge drinking). There were no significant effects on other behavioral outcomes, including the intersection of alcohol and sexual behaviors. In addition, there were mixed results (positive short-term effect; no effect at 60-day follow-up) of the SVP component on targeted proximal outcomes (students' self-efficacy to reduce/prevent sexual violence and perceived effectiveness of protective behavioral strategies). Conclusions: The hybrid evaluation-optimization trial enabled us to evaluate the individual and combined effectiveness of the optimized itMatters intervention and the SVP component in a single experiment, conserving resources and providing greatly improved efficiency. Trial Registration: NCT04095065.A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention
AbstractTimms, K. P., Rivera, D. E., Piper, M. E., & Collins, L. (n.d.).Publication year
2014Page(s)
2389-2394AbstractThe chronic, relapsing nature of tobacco use represents a major challenge in smoking cessation treatment. Recently, novel intervention paradigms have emerged that seek to adjust treatments over time in order to meet a patient's changing needs. This article demonstrates that Hybrid Model Predictive Control (HMPC) offers an appealing framework for designing these optimized, time-varying smoking cessation interventions. HMPC is a particularly appropriate approach as it recognizes that intervention doses must be assigned in predetermined, discrete units while retaining receding-horizon, constraint-handling, and combined feedback and feedforward capabilities. Specifically, an intervention algorithm is developed here in which counseling and two pharmacotherapies are manipulated to reduce daily smoking and craving levels. The potential usefulness of such an intervention is illustrated through simulated treatment of a quit attempt in a hypothetical patient, which highlights that prioritizing reduction in craving over total daily smoking levels significantly reduces craving levels, suppresses relapse, and successfully rejects time-varying disturbances such as stress, all while adhering to several practical operational constraints and resource use considerations.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.A multidimensional developmental model of alcohol use during emerging adulthood
AbstractAuerbach, K. J., & Collins, L. (n.d.).Publication year
2006Journal title
Journal of Studies on AlcoholVolume
67Issue
6Page(s)
917-925AbstractObjective: Longitudinal analyses identified unique multidimensional classes of alcohol use and examined individuals' movement among these classes during emerging adulthood. Method: Latent transition analysis was used to identify a developmental model of alcohol use incorporating four aspects of use: use in the past year, frequency of use, quantity of use, and heavy episodic drinking. Participants were drawn from the Reducing Risk in Young Adult Transitions study (N = 1,143). Participants' alcohol use was assessed at mean ages of 18.5, 20.5, and 22.5 years. Results: Through exploratory analysis, a five-class developmental model was identified as the best description of participants' alcohol use between ages 18.5 and 22.5 years. This model consisted of five multidimensional alcohol-use latent variables: no use, occasional low use, occasional high use, frequent high use, and frequent high use with heavy episodic drinking. Analyses provided information regarding the proportion of participants in each latent class in the model at each measurement occasion and patterns of participants' movement among latent classes during the observed age period. Conclusions: Although alcohol use increased overall for study participants between ages 18.5 and 22.5, participants in lower-level alcohol-use latent classes were more likely to remain in low-level latent classes over time, and participants in moderate- and high-level latent classes were more likely to be in the frequent high use with heavy episodic drinking latent class over time. Implications for the prevention of heavy episodic drinking are discussed.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.A note on the unbiased estimation of the intraclass correlation
AbstractDonoghue, J. R., & Collins, L. (n.d.).Publication year
1990Journal title
PsychometrikaVolume
55Issue
1Page(s)
159-164AbstractThe intraclass correlation, ρ, is a parameter featured in much psychological research. Two commonly used estimators of ρ, the maximum likelihood and least squares estimators, are known to be negatively biased. Olkin and Pratt (1958) derived the minimum variance unbiased estimator of the intraclass correlation, but use of this estimator has apparently been impeded by the lack of a closed form solution. This note briefly reviews the unbiased estimator and gives a FORTRAN 77 subroutine to calculate it.A Posterior Expected Value Approach to Decision-Making in the Multiphase Optimization Strategy for Intervention Science
AbstractStrayhorn, J. C., Collins, L., & Vanness, D. J. (n.d.).Publication year
2023Journal title
Psychological MethodsAbstractIn current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2k factorial optimization trial use a component screening approach (CSA) to select intervention components for inclusion in an optimized intervention. In this approach, scientists review all estimated main effects and interactions to identify the important ones based on a fixed threshold, and then base decisions about component selection on these important effects. We propose an alternative posterior expected value approach based on Bayesian decision theory. This newapproach aims to be easier to apply and more readily extensible to a variety of intervention optimization problems.We used Monte Carlo simulation to evaluate the performance of a posterior expected value approach and CSA (automated for simulation purposes) relative to two benchmarks: random component selection, and the classical treatment package approach. We found that both the posterior expected value approach and CSA yielded substantial performance gains relative to the benchmarks. We also found that the posterior expected value approach outperformed CSA modestly but consistently in terms of overall accuracy, sensitivity, and specificity, across a wide range of realistic variations in simulated factorial optimization trials. We discuss implications for intervention optimization and promising future directions in the use of posterior expected value to make decisions in MOST.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.A randomized controlled trial of an optimized smoking treatment delivered in primary care
AbstractPiper, M. E., Cook, J. W., Schlam, T. R., Jorenby, D. E., Smith, S. S., Collins, L., Mermelstein, R., Fraser, D., Fiore, M. C., & Baker, T. B. (n.d.).Publication year
2018Journal title
Annals of Behavioral MedicineVolume
52Issue
10Page(s)
854-864AbstractBackground: The effectiveness of smoking cessation treatment is limited in real-world use, perhaps because we have not selected the components of such treatments optimally nor have treatments typically been developed for and evaluated in real-world clinical settings.Purpose: To validate an optimized smoking cessation treatment package that comprises intervention components identified as effective in factorial screening experiments conducted as per the Multiphase Optimization Strategy (MOST).Methods: Adult smokers motivated to quit were recruited from primary care clinics (N = 623). Participants were randomized to receive either recommended usual care (R-UC; 10 min of in-person counseling, 8 weeks of nicotine patch, and referral to quitline services) or abstinence-optimized treatment (A-OT; 3 weeks of prequit mini-lozenges, 26 weeks of nicotine patch + mini-lozenges, three in-person and eight phone counseling sessions, and 7-11 automated calls to prompt medication use). The key outcomes were self-reported and biochemically confirmed (carbon monoxide, COA risk-based model predictive control approach to adaptive interventions in behavioral health
AbstractZafra-Cabeza, A., Rivera, D. E., Collins, L., Ridao, M. A., & Camacho, E. F. (n.d.).Publication year
2006Page(s)
673-678AbstractThis paper demonstrates how control systems engineering and risk management can be applied to problems in behavioral health through their application to the design and implementation of adaptive interventions. Adaptive interventions represent a promising approach to prevention and treatment of chronic, relapsing disorders, such as alcoholism, cigarette smoking, and drug abuse. The benefits of the proposed approach are presented in the development of risk-based Model Predictive Control (MPC) algorithm for a hypothetical intervention inspired by two real-life programs: Fast Track, an intervention whose long-term goal is the prevention of conduct disorders in at-risk children, and Communities that Care, a risk-based prevention program for substance abuse. The tailoring or controlled variable of the adaptive intervention is a measure of parental functioning in the family of an at-risk child; 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. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper has the potential to increase intervention potency and adherence while reducing waste, resulting in more effective interventions than conventional fixed treatment. MPC is particularly meaningful for the problem given some of its favorable properties, such as ease of constraint-handling, and its ability to scale to interventions involving multiple tailoring variables. Several simulations are conducted under conditions of varying disturbance magnitude to demonstrate the effectiveness of the algorithm.A risk-based model predictive control approach to adaptive interventions in behavioral health
AbstractZafra-Cabeza, A., Rivera, D. E., Collins, L., 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.A strategy for optimizing and evaluating behavioral interventions
AbstractCollins, L., Murphy, S. A., Nair, V. N., & Strecher, V. J. (n.d.).Publication year
2005Journal title
Annals of Behavioral MedicineVolume
30Issue
1Page(s)
65-73AbstractBackground: Although the optimization of behavioral interventions offers the potential of both public health and research benefits, currently there is no widely agreed-upon principled procedure for accomplishing this. Purpose: This article suggests a multiphase optimization strategy (MOST) for achieving the dual goals of program optimization and program evaluation in the behavioral intervention field. Methods: MOST consists of the following three phases: (a) screening, in which randomized experimentation closely guided by theory is used to assess an array of program and/or delivery components and select the components that merit further investigation; (b) refining, in which interactions among the identified set of components and their interrelationships with covariates are investigated in detail, again via randomized experiments, and optimal dosage levels and combinations of components are identified; and (c) confirming, in which the resulting optimized intervention is evaluated by means of a standard randomized intervention trial. To make the best use of available resources, MOST relies on design and analysis tools that help maximize efficiency, such as fractional factorials. Results: A slightly modified version of an actual application of MOST to develop a smoking cessation intervention is used to develop and present the ideas. Conclusions: MOST has the potential to husband program development resources while increasing our understanding of the individual program and delivery components that make up interventions. Considerations, challenges, open questions, and other potential benefits are discussed.A Tribute to the Mind, Methodology and Mentoring of Wayne Velicer
AbstractHarlow, L. L., Aiken, L., Blankson, A. N., Boodoo, G. M., Brick, L. A., Collins, L., Cumming, G., Fava, J. L., Goodwin, M. S., Hoeppner, B. B., MacKinnon, D. P., Molenaar, P. C., Rodgers, J. L., Rossi, J. S., Scott, A., Steiger, J. H., & West, S. G. (n.d.).Publication year
2021Journal title
Multivariate Behavioral ResearchVolume
56Issue
3Page(s)
377-389AbstractWayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us.Achieving the goals of translational science in public health intervention research : The multiphase optimization strategy (MOST)
AbstractGuastaferro, K., & Collins, L. (n.d.).Publication year
2019Journal title
American journal of public healthVolume
109Issue
S2Page(s)
S128-S129Abstract~Adaptive sampling in research on risk-related behaviors
AbstractThompson, S. K., & Collins, L. (n.d.).Publication year
2002Journal title
Drug and alcohol dependenceVolume
68Issue
SUPPL.Page(s)
57-67AbstractThis article introduces adaptive sampling designs to substance use researchers. Adaptive sampling is particularly useful when the population of interest is rare, unevenly distributed, hidden, or hard to reach. Examples of such populations are injection drug users, individuals at high risk for HIV/AIDS, and young adolescents who are nicotine dependent. In conventional sampling, the sampling design is based entirely on a priori information, and is fixed before the study begins. By contrast, in adaptive sampling, the sampling design adapts based on observations made during the survey; for example, drug users may be asked to refer other drug users to the researcher. In the present article several adaptive sampling designs are discussed. Link-tracing designs such as snowball sampling, random walk methods, and network sampling are described, along with adaptive allocation and adaptive cluster sampling. It is stressed that special estimation procedures taking the sampling design into account are needed when adaptive sampling has been used. These procedures yield estimates that are considerably better than conventional estimates. For rare and clustered populations adaptive designs can give substantial gains in efficiency over conventional designs, and for hidden populations link-tracing and other adaptive procedures may provide the only practical way to obtain a sample large enough for the study objectives.Advances in clinical trials methodology : Intervention optimization approaches in emergency medicine
AbstractBernstein, S. L., Carter, P. M., Meurer, W., Walton, M. A., Kidwell, K. M., Cunningham, R. M., Dziura, J., & Collins, L. (n.d.).Publication year
2022Journal title
American Journal of Emergency MedicineVolume
53Page(s)
6-11AbstractThe classical two-arm randomized clinical trial (RCT) is designed to test the efficacy or effectiveness of an intervention, which may consist of one or more components. However, this approach does not enable the investigator to obtain information that is important in intervention development, such as which individual components of the intervention are efficacious, which are not and possibly should be removed, and whether any components interact. The Multiphase Optimization Strategy (MOST) is a new framework for development, optimization, and evaluation of interventions. MOST includes the RCT for purposes of evaluation, but inserts a phase of research before the RCT aimed at intervention optimization. The optimization phase requires one or more separate trials similar in scope to an RCT, but employing a different experimental design. The design of the optimization trial is selected strategically so as to maximize the amount of scientific information gained using the available resources. One consideration in selecting this experimental design is the type of intervention to be optimized. If a fixed intervention, i.e. one in which the same intervention content and intensity is provided to all participants, is to be optimized, a factorial experiment is often appropriate. If an adaptive intervention, i.e. one in which intervention content or intensity is varied in a principled manner, is to be optimized, a sequential multiple-assignment randomized trial (SMART) is often a good choice. The objective of this article is to describe MOST and the scientific rationale for its use; describe two current applications of MOST in emergency medicine research, one using a factorial experiment and the other using a SMART; and discuss funding strategies and potential future applications in studying the care of individuals with acute illness, injury, or behavioral disorders.