A5051702169- Advanced Causal Inference Techniques
- Statistical Methods and Bayesian Inference
- Statistical Methods in Clinical Trials
- Microscopic Colitis
- Meta-analysis and systematic reviews
- Inflammatory Bowel Disease
- Statistical Methods and Inference
- Data Analysis with R
- Reliability and Agreement in Measurement
- Psychometric Methodologies and Testing
- Acute Ischemic Stroke Management
- Appendicitis Diagnosis and Management
- Biosimilars and Bioanalytical Methods
University of Waterloo
2022-2024
A nonparametric method proposed by DeLong et al in 1988 for comparing areas under correlated receiver operating characteristic curves is used widely practice. However, the as implemented popular software quietly deletes individuals with any missing values, yielding potentially invalid and/or inefficient results. We simplify algorithm using ranks and extend it to accommodate data a mixed model approach multivariate data. Simulation results demonstrate validity efficiency of our procedure at...
Abstract Data on the Likert scale are ubiquitous in medical research, including randomized trials. Statistical analysis of such data may be conducted using means raw scores or rank information scores. In context parallel‐group trials, we quantify treatment effects by probability that a subject group has better score than (or win over) control group. Asymptotic parametric and nonparametric confidence intervals for this associated sample size formulas derived studies with only follow‐up...
ABSTRACT Multiple primary endpoints are commonly used in randomized controlled trials to assess treatment effects. When the measured on different scales, O'Brien rank‐sum test or Wei–Lachin for stochastic ordering may be hypothesis testing. However, O'Brien–Wei–Lachin (OWL) approach is unable handle missing data and adjust baseline measurements. We present a nonparametric analysis that encompasses OWL as special case. Our based quantifying an endpoint‐specific effect using probability...