Jillian Strayhorn
Jillian Strayhorn
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Assistant Professor of Social and Behavioral Sciences
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Professional overview
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Jillian C. Strayhorn, PhD is an Assistant Professor in the Department of Social and Behavioral Sciences at GPH and Associate Director of its Center for the Advancement and Dissemination of Intervention Optimization (cadio). She is a quantitative methodologist and decision scientist whose research focuses on the complex multi-criteria decision-making that goes into optimizing multicomponent interventions to achieve public health impact.
Dr. Strayhorn is an expert on the multiphase optimization strategy (MOST), a framework for optimizing behavioral, biobehavioral, and social-structural interventions. Her work in intervention optimization is highly interdisciplinary, bringing together ideas and methods from Bayesian statistics, health economics and multi-criteria decision analysis. The driving mission of this work is to enable more successful identification and advancement of high-value interventions capable of accomplishing complex objectives, including objectives that involve multiple outcomes, efficiency of resource use, or health equity. Dr. Strayhorn collaborates on applications of MOST across various areas of public health, including cancer risk reduction, smoking cessation, HIV, substance misuse, and mental health, among others.
Dr. Strayhorn earned her BA in Psychology, summa cum laude with distinction in all subjects, at Cornell University, and her PhD in Human Development and Family Studies at Pennsylvania State University, where she was the recipient of a Ruth L. Kirschstein NRSA predoctoral award (F31) from the National Institute on Drug Abuse . Her latest work has been published in Psychological Methods, Health Psychology, and Translational Behavioral Medicine.
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Education
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BA, Psychology, Cornell University, Ithaca, NYMS, Human Development and Family Studies, Pennsylvania State University, University Park, PAPhD, Human Development and Family Studies, Pennsylvania State University, University Park, PA
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Honors and awards
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Alumni Association Dissertation Award, Pennsylvania State University (2022)Student Optimization of Behavioral and Biobehavioral Interventions Research Award, Society of Behavioral Medicine (2021)Merrill Presidential Scholar Award, Cornell University (2014)Phi Beta Kappa Junior Inductee, Cornell University (2013)Robinson-Appel Humanitarian Award, Cornell University (2013)
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Publications
Publications
A posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science
AbstractStrayhorn, J., Strayhorn, J. C., Collins, L. M., & Vanness, D. J. (n.d.).Publication year
2024Journal title
Psychological methodsAbstractIn current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2 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 new approach 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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).An Optimization Randomized Clinical Trial to Identify an Effective, Efficient Smoking Cessation Intervention in the Context of Lung Cancer Screening: Cessation and Screening to Save Lives (CASTL).
AbstractOstroff, J., Shelley, D., Chichester, L., Schofield, E., Li, Y., Collins, L., Elkin, E., Strayhorn, J., Ciupek, A., King, J., & Barry, A. (n.d.).Publication year
2025Journal title
CHESTAbstract~Applying a decision-priority perspective in optimizing adaptive interventions.
AbstractStrayhorn, J. (n.d.).Abstract~Bayesian Multicriteria Decision Analysis Methods for Optimizing Multicomponent Interventions: The Effect of Value Function Misspecification
AbstractStrayhorn, J., & Vanness, D. (n.d.).Publication year
2025Abstract~Decision-making in factorial optimization trials with multiple outcomes: A posterior expected value approach.
AbstractStrayhorn, J., Collins, L., & Vanness, D. (n.d.).Publication year
2024Journal title
Medical Decision MakingAbstract~Decision-making in the multiphase optimization strategy (MOST): Applying decision analysis for intervention value efficiency (DAIVE) to optimize an information leaflet to support medication adherence.
AbstractGreen, S. M., Smith, S. G., Collins, L., & Strayhorn, J. (n.d.).Publication year
2024Journal title
Translational Behavioral MedicineAbstract~Decision-making in the multiphase optimization strategy: application of a posterior expected value approach.
AbstractGreen, S., Smith, S., Collins, L., & Strayhorn, J. (n.d.).Publication year
2024AbstractBackground: Intervention optimization using the multiphase optimization strategy (MOST) involves strategic decision-making about the composition of the optimized intervention, based on empirical results from an optimization trial. The initial component screening approach (CSA) to optimization decision-making had limitations, including reliance on arbitrary significance thresholds and inability to incorporate multiple outcome variables. Recent advances in MOST have suggested an alternative posterior expected value (PEV) approach, which does not have the limitations of a CSA and showedsuperior decision-making performance, relative to a CSA, in simulation. We previously used a CSA to select, based on a single primary outcome, an optimized version of an information leaflet designed to support medication beliefs in women with breast cancer. Here we apply the innovative PEV approach to select an optimized leaflet based on multiple valued outcomes.Methods: We used data from a 2 5 factorial trial (n=1604) involving five candidate intervention components: 1) diagrams about the medication, 2) medication benefits, 3) side-effect information, 4) answers to common concerns, and 5) quotes from breast cancer survivors. Components were hypothesized to contribute to three outcomes: beliefs about medication (BMQ; primary), satisfaction with information about medication (secondary), and knowledge about the medication (secondary). To apply a PEV approach, we used Bayesian factorial analysis of variance to estimate expected outcomesfor each unique intervention (32 total) on each of the three valued outcomes. We put outcomes on the same 0-1 scale and combined them using a linear value function, which gave more importance to the primary outcome than the secondary outcomes, yielding an expected value for each intervention. We identified the intervention with the largest expected value as the optimized leaflet.Results: When performance on all three outcomes was considered, the optimized leaflet contained two components: side-effect information and quotes from breast cancer survivors. The remaining three candidate components (diagrams, benefits and common concerns) were not included. When the CSA was used with a single primary outcome (BMQ), the optimized leaflet contained four components; diagrams, benefits, concerns and quotes.Conclusions: Using a PEV approach and empirical information about component performance on multiple valued outcomes led to a different choice of optimized leaflet than was previously made using a CSA with a single primary outcome. This highlights the importance of specifying priority outcomes a priori, as well as points of contrast between a PEV approach and CSA.Effects of behavioral intervention components to increase COVID-19 testing for African American/Black and Latine frontline essential workers not up-to-date on COVID-19 vaccination: Results of an optimization randomized controlled trial
AbstractStrayhorn, J., Gwadz, M., Heng, S., Cleland, C. M., Strayhorn, J., Robinson, J. A., Serrano, F. G. B., Wang, P., Parameswaran, L., & Chero, R. (n.d.).Publication year
2025Journal title
Journal of behavioral medicineAbstractRacial/ethnic disparities in COVID-19, including incidence, hospitalization, and death rates, are serious and persistent. Among those at highest risk for COVID-19 and its adverse effects are African American/Black and Latine (AABL) frontline essential workers in public-facing occupations (e.g., food services, retail). Testing for COVID-19 in various scenarios (when exposed or symptomatic, regular screening testing) is an essential component of the COVID-19 control strategy in the United States. However, AABL frontline workers have serious barriers to COVID-19 testing at the individual (insufficient knowledge, distrust, cognitive biases), social (norms), and structural levels of influence (access). Thus, testing rates are insufficient and interventions are needed. The present study is grounded in the multiphase optimization strategy (MOST) framework. It tests the main and interaction effects of a set of candidate behavioral intervention components to increase COVID-19 testing rates in this population. The study enrolled adult AABL frontline essential workers who were not up-to-date on COVID-19 vaccination nor recently tested for COVID-19. It used a factorial design to examine the effects of candidate behavioral intervention components, where each component was designed to address a specific barrier to COVID-19 testing. All participants received a core intervention comprised of health education. The candidate components were motivational interviewing counseling (MIC), a behavioral economics intervention (BEI), peer education (PE), and access to testing (either self-test kits [SK] or a navigation meeting [NM]). The primary outcome was COVID-19 testing in the follow-up period. Participants were assessed at baseline, randomly assigned to one of 16 experimental conditions, and assessed six- and 12-weeks later. The study was carried out in English and Spanish. We used a logistic regression model and multiple imputation to examine the main and interaction effects of the four factors (representing components): MIC, BEI, PE, and Access. We also conducted a sensitivity analysis using the complete case analysis. Participants (N = 438) were 35 years old on average (SD = 10). Half identified as men/male (52%), and 48% as women/female/other. Almost half (49%) were African American/Black, and 51% were Latine/Hispanic (12% participated in Spanish). A total of 32% worked in food services. Attendance in components was very high (~ 99%). BEI had positive effect on the outcome (OR = 1.543; 95% CI: [0.977, 2.438]; p-value = 0.063) as did Access, in favor of SK (OR = 1.351; 95% CI: [0.859, 2.125]; p-value = 0.193). We found a three-way interaction among MIC*PE*Access (OR: 0.576; 95% CI: [0.367, 0.903]; p-value = 0.016): when MIC was present, SK tended to increase COVID testing when PE was not present. The study advances intervention science and takes the first step toward creating an efficient and effective multi-component intervention to increase COVID-19 testing rates in AABL frontline workers.Effects of Chatbot Components to Facilitate Mental Health Services Use in Individuals withEating Disorders Following Online Screening: An Optimization Randomized Controlled Trial
AbstractFitzsimmons-Craft, E., Rackoff, G., Shah, J., Strayhorn, J., D’Adamo, L., Howe, C., DiPietro, B., Firebaugh, M.-L., Newman, M., Collins, L., Barr Taylor, C., & Wilfley, D. (n.d.).Publication year
2024Journal title
International Journal of Eating DisordersVolume
57Issue
11Page(s)
2204-2216Abstract~Explaining the Effects of Treatment Components in Internet-Based Cognitive-Behavioral Treatment for Social Anxiety Disorder: A Factorial Mediation Analysis
AbstractLopes, R., Sipka, D., Strayhorn, J., Fernandez-Alvarez, J., Krieger, T., Klein, J., & Berger, T. (n.d.).Publication year
2024Journal title
Behaviour Research and TherapyAbstract~Intervention optimization as an opportunity to move toward implementability and equitability.
AbstractStrayhorn, J., & Collins, L. (n.d.).Abstract~Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology
AbstractStrayhorn, J., Collins, L. M., Nahum-Shani, I., Guastaferro, K., Strayhorn, J. C., Vanness, D. J., & Murphy, S. A. (n.d.).Publication year
2024Journal title
Annual review of clinical psychologyAbstractTo build a coherent knowledge base about what psychological intervention strategies work, develop interventions that have positive societal impact, and maintain and increase this impact over time, it is necessary to replace the classical treatment package research paradigm. The multiphase optimization strategy (MOST) is an alternative paradigm that integrates ideas from behavioral science, engineering, implementation science, economics, and decision science. MOST enables optimization of interventions to strategically balance effectiveness, affordability, scalability, and efficiency. In this review we provide an overview of MOST, discuss several experimental designs that can be used in intervention optimization, consider how the investigator can use experimental results to select components for inclusion in the optimized intervention, discuss the application of MOST in implementation science, and list future issues in this rapidly evolving field. We highlight the feasibility of adopting this new research paradigm as well as its potential to hasten the progress of psychological intervention science. Expected final online publication date for the , Volume 20 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.Justifying the sample size for a factorial trial
AbstractStrayhorn, J. (n.d.).Publication year
2025Abstract~Lead exposure and the 2010 achievement test scores of children in New York counties
AbstractStrayhorn, J., Strayhorn, J. C., & Strayhorn, J. M. (n.d.).Publication year
2012Journal title
Child and adolescent psychiatry and mental healthVolume
6Issue
1Page(s)
4AbstractLead is toxic to cognitive and behavioral functioning in children even at levels well below those producing physical symptoms. Continuing efforts in the U.S. since about the 1970s to reduce lead exposure in children have dramatically reduced the incidence of elevated blood lead levels (with elevated levels defined by the current U.S. Centers for Disease Control threshold of 10 μg/dl). The current study examines how much lead toxicity continues to impair the academic achievement of children of New York State, using 2010 test data.Martial arts as a mental health intervention for children? Evidence from the ECLS-K
AbstractStrayhorn, J., Strayhorn, J. M., & Strayhorn, J. C. (n.d.).Publication year
2009Journal title
Child and adolescent psychiatry and mental healthVolume
3Issue
1Page(s)
32AbstractMartial arts studios for children market their services as providing mental health outcomes such as self-esteem, self-confidence, concentration, and self-discipline. It appears that many parents enroll their children in martial arts in hopes of obtaining such outcomes. The current study used the data from the Early Childhood Longitudinal Study, Kindergarten class of 1998-1999, to assess the effects of martial arts upon such outcomes as rated by classroom teachers.Martial arts research: prudent skepticism
AbstractStrayhorn, J., Strayhorn, J. M., & Strayhorn, J. C. (n.d.).Publication year
2011Journal title
Science (New York, N.Y.)Volume
334Issue
6054Page(s)
310; author reply 311Abstract~Multiphase optimization strategy: How to build more effective, affordable, scalable and efficient social and behavioural oral health interventions
AbstractStrayhorn, J., Guastaferro, K., & Strayhorn, J. C. (n.d.).Publication year
2023Journal title
Community dentistry and oral epidemiologyVolume
51Issue
1Page(s)
103-107AbstractThis commentary introduces the field of social behavioural oral health interventions to the multiphase optimization strategy (MOST). MOST is a principled framework for the development, optimization and evaluation of multicomponent interventions. Drawing from the fields of engineering, behavioural science, economics, decision science and public health, intervention optimization requires a strategic balance of effectiveness with affordability, scalability and efficiency. We argue that interventions developed using MOST are more likely to maximize the public health impact of social behavioural oral health interventions.New advances in optimizing interventions for equitability.
AbstractStrayhorn, J. (n.d.).Abstract~One view of the next decade of research on behavioral and biobehavioral approaches to cancer prevention and control: intervention optimization
AbstractStrayhorn, J., Collins, L. M., Strayhorn, J. C., & Vanness, D. J. (n.d.).Publication year
2021Journal title
Translational behavioral medicineVolume
11Issue
11Page(s)
1998-2008AbstractAs a new decade begins, we propose that the time is right to reexamine current methods and procedures and look for opportunities to accelerate progress in cancer prevention and control. In this article we offer our view of the next decade of research on behavioral and biobehavioral interventions for cancer prevention and control. We begin by discussing and questioning several implicit conventions. We then briefly introduce an alternative research framework: the multiphase optimization strategy (MOST). MOST, a principled framework for intervention development, optimization, and evaluation, stresses not only intervention effectiveness, but also intervention affordability, scalability, and efficiency. We review some current limitations of MOST along with future directions for methodological work in this area, and suggest some changes in the scientific environment we believe would permit wider adoption of intervention optimization. We propose that wider adoption of intervention optimization would have a positive impact on development and successful implementation of interventions for cancer prevention and control and on intervention science more broadly, including accumulation of a coherent base of knowledge about what works and what does not; establishment of an empirical basis for adaptation of interventions to different settings with different levels and types of resources; and, in the long run, acceleration of progress from Stage 0 to Stage V in the National Institutes of Health Model of Stages of Intervention Development.Operationalizing primary outcomes to achieve reach, effectiveness, and equity in multilevel interventions
AbstractGuastaferro, K., Sheldrick, R. C., Strayhorn, J., & Feinberg, E. (n.d.).Publication year
2023Journal title
Prevention ScienceAbstractWhen intervention scientists plan a clinical trial of an intervention, they select an outcome metric that operationalizes their definition of intervention success. The outcome metric that is selected has important implications for which interventions are eventually supported for implementation at scale and, therefore, what health benefits (including how much benefit and for whom) are experienced in a population. Particularly when an intervention is to be implemented in a population that experiences a health disparity, the outcome metric that is selected can also have implications for equity. Some outcome metrics risk exacerbating an existing health disparity, while others may decrease disparities for some but have less effect for the larger population. In this study, we use a computer to simulate implementation of a hypothetical multilevel, multicomponent intervention to highlight the tradeoffs that can occur between outcome metrics that reflect different operationalizations of intervention success. In particular, we highlight tradeoffs between overall mean population benefit and the distribution of health benefits in the population, which has direct implications for equity. We suggest that simulations like the one we present can be useful in the planning of a clinical trial for a multilevel and/or multicomponent intervention, since simulated implementation at scale can illustrate potential consequences of candidate operationalization of intervention success, such that unintended consequences for equity can be avoided.Optimization of Smoking Cessation Interventions via Multiphase Optimization STrategy (MOST): Basic Concepts, Practical Considerations and New Developments
AbstractCollins, L., Guastaferro, K., Strayhorn, J., Cantrell, J., Kimber, C., & Piper, M. (n.d.).Publication year
2024Abstract~Optimizing educational interventions in crisis contexts through the multiphase optimization strategy (MOST).
AbstractCollins, L., Guastaferro, K., & Strayhorn, J. (n.d.).Publication year
2022Abstract~Optimizing home visiting programs to improve reach: A case study in strategically balancing intervention effectiveness with provider time
AbstractGuastaferro, K., & Strayhorn, J. (n.d.).Publication year
2024Journal title
Child Protection and PracticeAbstract~Optimizing Interventions for Equitability: Some Initial Ideas
AbstractStrayhorn, J., Strayhorn, J. C., Vanness, D. J., & Collins, L. M. (n.d.).Publication year
2024Journal title
Prevention science : the official journal of the Society for Prevention ResearchAbstractInterventions (including behavioral, biobehavioral, biomedical, and social-structural interventions) hold tremendous potential not only to improve public health overall but also to reduce health disparities and promote health equity. In this study, we introduce one way in which interventions can be optimized for health equity in a principled fashion using the multiphase optimization strategy (MOST). Specifically, we define intervention equitability as the extent to which the health benefits provided by an intervention are distributed evenly versus concentrated among those who are already advantaged, and we suggest that, if intervention equitability is acknowledged to be a priority, then equitability should be a key criterion that is balanced with other criteria (effectiveness overall, as well as affordability, scalability, and/or efficiency) in intervention optimization. Using a hypothetical case study and simulated data, we show how MOST can be applied to achieve a strategic balance that incorporates equitability. We also show how the composition of an optimized intervention can differ when equitability is considered versus when it is not. We conclude with a vision for next steps to build on this initial foray into optimizing interventions for equitability.Optimizing Interventions for Equitability: Some Initial Ideas.
AbstractStrayhorn, J., & Collins, L. (n.d.).Publication year
2025Abstract~