I graduated with a PhD in Statistics from the University of Michigan. Currently, I'm working as a freelance statistical consultant. I love exploring ways to use data to help improve the world. Below is a brief summary of my interests and work. See my CV for more details.
As technology develops, new types of data can be collected at a much higher frequency. Additionally, these new technologies provide a new platform for intervention delivery. My research interests involve developing experimental designs and statistical methodology for understanding these new data and new forms of intervention.
I'm currently involved in two projects. In one project, we are designing novel experiments and corresponding statistical methods to improve the mental health of medical interns using mobile technologies, such as fitbits and phones. This work is in collaboration with Srijan Sen, the Intern Health Study, and Zhenke Wu.
In a second project, we are experimenting and performing analyses to evaluate interventions aimed at improving learner engagement in a Massively Open Online Course. This work is in collaboration with Chris Brooks.
I also worked in collaboration with the D3 Lab on dynamic treatment regimes, sequential multiple assignment randomized trials (SMARTs), just-in-time adaptive interventions (JITAIs), and micro-randomized trials (MRTs). I worked with Daniel Almirall to develop estimation methodology and sample size formulae for clustered SMARTs.
T. NeCamp, S. Sen, E. Ionides, A. Tewari, Y. Fang, M. Walton, E. Frank, Z. Wu. (2020) Assessing real-time moderation for developing adaptive mobile health interventions for medical interns: Micro-randomized trial. Journal of Medical Internet Research. 22(3):e15033. DOI: 10.2196/15033. Link to Paper
T. NeCamp, J. Gardner, C. Brooks. (2019) Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments. 9th International Conference on Learning Analytics and Knowledge (LAK19). March, 2019. Tempe, AZ. doi: 10.1145/3303772.3303812. Link to Paper
Presented at the Conference on Digital Experimentation (CODE) at MIT. (2018) Sequential Randomization to Develop Personalized and Optimized Interventions in Massively Open Online Courses: A Case Study Notes from CODE talk | Slides from CODE talk
T. NeCamp, A. Kilbourne, D. Almirall. (2017) Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations. Statistical Methods in Medical Research. doi: 10.1177/0962280217708654. Link to Paper | Link to Code
Wolfe, A. D., T. NeCamp, S. Fassnacht, P. Blischak, and L. Kubatko. (2016), Population genetics of Penstemon albomarginatus (Plantaginaceae), a rare Mojave Desert species of conservation concern. Conservation Genetics. 17: 1245. doi:10.1007/s10592-016-0857-y Link
Wolfe, A. D., A. McMullen-Sibul, V. J. Tepedino, L. Kubatko, T. NeCamp , and S. Fassnacht. (2014), Conservation genetics and breeding system of Penstemon debilis (Plantaginaceae), a rare beardtongue endemic to oil shale talus in western Colorado, USA. Journal of Systematics and Evolution, 52: 598-611. doi: 10.1111/jse.12100. Link
I have worked extensively during my PhD to find opportunities to use data and my skill set to positively impact society. As a member and president of STATCOM at the University of Michigan, I have been fortunate enough to work on several projects which use data and statistics to benefit community partners.
See a recent news article I wrote (with Evan Reynolds) about these experiences. STATCOM: Revitalization of Statistical Community Service at Universities
I am extremely passionate about social justice and enacting social change. I continually seek opportunities to volunteer my time to make the world a more equal place.
tnecamp [arroba] umich [punto] edu