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research.Rmd
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---
title: "Research"
---
***
My research focuses on **evaluating** and **applying** advanced statistical methods to behavioral and health data. My research falls into two broad categories: statistical research and applied research.
[CV](files/SCoxe CV 070824.pdf) / [`r fontawesome::fa(name = "google", fill = "steelblue")` scholar](https://scholar.google.com/citations?user=fZEA0AkAAAAJ)
## Statistical research
### **Generalized linear model (GLiM)**
GLiMs are a broad family of statistical models that extend linear regression analysis to a wider range of outcome variable distributions and error structures. I use *simulations* to explore the performance of these models. I am particularly interested in the statistical characteristics of counts outcome variables, especially in statistical mediation models.
- **Coxe, S.** (2019, March). Counts and Frequencies in Psychological Research. At Blending Psychology and Methods: A Conference in Honor of Professor Leona Aiken. Tempe, AZ. March 22, 2019.
- Aiken, L. S., Mistler, S. A., **Coxe, S.**, and West, S. G. (2015). Analyzing Count Variables in Individuals and Groups: Single and Multilevel Models. *Group Processes and Intergroup Relations, 18*, 290-314. doi: 10.1177/1368430214556702
- **Coxe, S.**, Aiken, L. S., and West, S. G. (2013). Generalized linear models. In T. Little (Ed.), *Oxford Handbook of Quantitative Methods, Volume 2*. New York: Oxford University Press.
- **Coxe, S.** (2010). Mediation analysis of Poisson distributed count outcomes: Standard estimates of the mediated effect are not equivalent. Presented at the 8th Annual Society of Multivariate Experimental Psychology Graduate Student Pre-conference. Atlanta, GA. September 30, 2010.
- **Coxe, S.**, West, S. G., and Aiken, L. S. (2009). The analysis of count data: A gentle introduction to Poisson regression and its alternatives. Journal of *Personality Assessment, 91*, 121-136.
- Data from this paper: [SAS](files/jpapoisson.sas7bdat) / [SPSS](files/jpapoisson.sav) / [Text](files/jpapoisson.txt)
### **Integrative data analysis (IDA)**
I am joint co-PI on [TIDAL](https://doi.org/10.1186/s12888-020-02734-6), an integrative data analysis project funded by [NIMH](https://reporter.nih.gov/project-details/9509965) combining data from 4 separate psychosocial interventions for teens who have been diagnosed with ADHD. The combined dataset is publicly available [here](https://nda.nih.gov/data_structure.html?short_name=tidal01). Among the methodological challenges in this project were harmonization of measures of ADHD symptoms (DSM-IV versus DSM-5) and parent depression symptoms (various measures) using moderated nonlinear factor analysis.
- [**Coxe, S.** (2023, April). Integrative data analysis: Latent profile analysis of adolescents with ADHD. At FIU Statistics Department Colloquium Series. Miami, FL. April 19, 2023.](files/StatColl2023.html)
- **Coxe, S.** & Sibley, M. H. (2023). Harmonizing DSM-IV and DSM-5 versions of ADHD "A Criteria": An Item Response Theory Analysis. *Assessment, 30*, 606-617.[https://doi.org/10.1177/10731911211061299](https://doi.org/10.1177/10731911211061299)
- Zhao, X., **Coxe, S.**, Sibley, M. J., Zulauf-McCurdy, C.A., & Pettit, J. (2022). Harmonizing Depression Measures Across Studies: A Tutorial for Data Harmonization. *Prevention Science*. [https://doi.org/10.1007](https://doi.org/10.1007)
- [**Coxe, S.** (2022, April). Integrative data analysis to further replicable science in psychology. At FIU Statistics Annual Mini-Conference in Statistical Methods and Mentoring. Miami, FL. April 8, 2022.](files/Coxe_Stats_040822.html)
- Sibley, M. H., **Coxe, S.**, Stein, M.A., Meinzer, M. C., Valente, M. (2022). Predictors of Treatment Engagement and Response among Adolescents with ADHD: An Integrative Data Analysis. *Journal of the American Academy of Child and Adolescent and Psychiatry*.
- **Coxe, S.**, Sibley, M.H., & Becker, S.P. (2021). Presenting Problem Profiles for Adolescents with ADHD: Differences by Sex, Age, Race, and Family Adversity. *Child and Adolescent Mental Health, 26*, 228-237.
- Sibley, M.H. & **Coxe, S.** (2020). The ADHD Teen Integrative Data Analysis Longitudinal (TIDAL) Dataset: Background, Methodology, and Aims. *BMC Psychiatry, 20*, 1-12.
### **Statistical graphics and R shiny**
I am interested in graphics as an aid to interpreting and communicating statistical models. To this end, I have developed `r fontawesome::fa(name = "r-project", fill = "steelblue")` packages and `r fontawesome::fa(name = "r-project", fill = "steelblue")` **shiny** applications to calculate effect size for count regression models and to estimate confidence intervals for mediation models. `r fontawesome::fa(name = "r-project", fill = "steelblue")` **shiny** applications are **interactive, web-based** tools for statistical analysis and graphics.
- [countES](https://github.com/stefanycoxe/countES): An `r fontawesome::fa(name = "r-project", fill = "steelblue")` package to estimate effect sizes for regression models for count models (Poisson regression and negative binomial regression). Standard multiplicative effect size (rate ratio) and standardized mean difference (Cohen's *d*) effect sizes are provided. Monte Carlo confidence intervals for each effect size are also given.
- [RcountD](https://stefany.shinyapps.io/RcountD/): An `r fontawesome::fa(name = "r-project", fill = "steelblue")`**shiny** app that implements the same functions
- [**Coxe, S.** (2018, February). Effect size measures for nonlinear count regression models. Poster presented at the American Statistical Association Conference on Statistical Practice. Portland, OR. February 15 – 17, 2018.](files/Coxe ASA CSP 020618.pdf)
- [SimpleMediation](https://stefany.shinyapps.io/SimpleMediation/): Estimate indirect (mediated) effect with Monte Carlo simulated confidence intervals. This app was developed for pedagogical purposes.
- I taught a course on **Statistical Graphics and Communication** in the Psychology department at FIU. This course covered a variety of topics, including **R**, **ggplot2**, **markdown**, and **shiny**. Most of the materials for the *first version* of the course from 2019 are available here: [https://stefanycoxe.github.io/graphics_2019/](https://stefanycoxe.github.io/graphics_2019/)
## Applied research
My applied research takes place largely within the [Cancer Institute](https://www.cedars-sinai.org/programs/cancer.html) at Cedars-Sinai Medical Center, focusing on behavioral interventions within the Cancer Prevention and Control and the Health Equity groups. I also have 20 years of experience with clinical and psychosocial interventions in the Psychology departments at Arizona State University and Florida International University. I have also collaborated with faculty in the Center for Children and Families and the departments of Epidemiology, Social Work, and Biomedical Engineering at Florida International University. These studies often include complex longitudinal designs, non-normally distributed outcome variables, and extensive missing data.
- Zhao, X., **Coxe, S.**, Timmons, A., & Frazier, S. L. (2022). Mental Health Information-Seeking Online: A Google Trends Analysis of ADHD. *Administration and Policy in Mental Health and Mental Health Services Research, 49*, 357 - 373. [https://doi.org/10.1007/s10488-021-01168-w](https://doi.org/10.1007/s10488-021-01168-w)
- Sibley, M.H., **Coxe, S.J.**, Page, T.P., Pelham, W.E., Yeguez, C.E., LaCount, P.A., & Barney, S. (2020). Four-Year Follow-up of High versus Low Intensity Summer Treatment for Adolescents with ADHD. *Journal of Clinical Child and Adolescent Psychology*.
- Mukherjee, S., **Coxe, S.**, Fennie, K., Madhivanan, P., Trepka, M. J. (2017). Stressful life event experiences of pregnant women in the United States: A latent class analysis. *Women’s Health Issues, 27*, 83 – 92.
- Bagner, D. M., **Coxe, S.**, Hungerford, G. M., Linares, D., Barroso, N., Hernandez, J., Rosa-Olivares, J. (2016). Behavioral parent training in infancy: A window of opportunity for high-risk families. *Journal of Abnormal Child Psychology, 44*, 901-912. doi: 10.1007/s10802-015-0089-5