Public Health and Health Professions
Shan Lab
Led by Guogen Shan · Biostatistics
What we study
The Shan Lab develops statistical methods for clinical trials and survival data analysis, focusing on exact tests and software tools. They work with deep learning and survival models for Alzheimer's research.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Projects
- 2
- Papers
- 5
Current projects
Application of deep learning and novel survival models to predict MCI-to-AD dementia progression
Using deep learning to predict Alzheimer's disease progression from mild cognitive impairment.
Alzheimer's Disease: New Trial Designs for Emerging Challenges
Developing new trial designs for Alzheimer's disease research.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- A Research Protocol to Study the Dose- and Age-Dependent Effects of Breathing Low Oxygen on Enhancing Transcutaneous Spinal Stimulation-Induced Walking Function for People with Spinal Cord Injury: The BO 2 ST-II Trial
2026 · Neurotrauma Reports
Planned a trial to test low oxygen breathing and transcutaneous spinal stimulation for improving walking in older spinal cord injury patients.
- Statistical inference for saved time based on disease progression curves in Alzheimer’s disease research
2026 · Contemporary Clinical Trials Communications
Developed a method to estimate saved time in Alzheimer’s disease using disease progression curves and statistical inference.
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- Crestal bone changes in tissue‐level implants according to implant placement depth: A retrospective cohort study
2026 · Journal of Periodontology
Examined how implant placement depth affects crestal bone changes in tissue-level implants, using patient and implant data.
- Comparison of statistical methods for sequential parallel comparison designs with time-to-event endpoints under non-proportional hazards
2026 · BMC Medical Research Methodology
Evaluated statistical methods for sequential parallel comparison designs with time-to-event endpoints under non-proportional hazards.
- Adaptive sample size re-estimation designs for a two-stage randomized trial with binary outcome
2025 · Statistical Methods in Medical Research
Proposed adaptive sample size re-estimation designs for two-stage randomized trials with binary outcomes.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Guogen Shan hasn't set up this page yet. Their UF Scholars profile lists their publications.