Liberal Arts and Sciences
Daniels Lab
Led by Michael Daniels · Statistics
What we study
The lab studies biostatistics, focusing on Bayesian methods and causal inference for incomplete data. They use statistical modeling and data analysis.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Papers
- 5
Recent papers, explained
- A sensitivity analysis approach to principal stratification with a continuous longitudinal intermediate outcome: applications to a cohort stepped wedge trial
2026 · Biostatistics
Develops a method to estimate causal effects in studies with continuous intermediate outcomes.
- Long-term memory effects of an incremental blood pressure intervention in a mortal cohort
2026 · Biometrics
Tests whether reducing blood pressure over 15 years improves memory in people with hypertension.
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- Bayesian feature selection in joint models with application to a cardiovascular disease cohort study
2026 · Statistical Methods in Medical Research
Uses Bayesian methods to identify important features in cardiovascular disease studies, improving models for risk factor analysis.
- A New Algorithm for Sampling Parameters in a Structured Correlation Matrix With Application to Estimating Optimal Combinations of Muscles to Quantify Progression in Duchenne Muscular Dystrophy
2025 · Statistics in Medicine
Creates a new algorithm to sample parameters in a structured correlation matrix for tracking muscle changes in Duchenne muscular dystrophy.
- Risk of Seizure Associated With Concomitant Use of Tramadol and Antidepressants in Older Nursing Home Residents
2025 · Neurology
Finds if combining tramadol with certain antidepressants increases seizure risk in older nursing home residents.
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How to reach out
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