1.3k citations
- Vanderbilt UniversityUS67 papers
- University of PittsburghUS8 papers
- Harvard UniversityUS7 papers
- National Institutes of HealthUS7 papers
- Johns Hopkins UniversityUS6 papers
- University of California, San FranciscoUS6 papers
- University of PennsylvaniaUS6 papers
- Washington University in St. LouisUS6 papers
- Brigham and Women's HospitalUS5 papers
- Heidelberg UniversityDE5 papers
- Stanford UniversityUS5 papers
- University of WashingtonUS5 papers
9 papers · 1 filter
Unified and Simple Sample Size Calculations for Individual or Cluster Randomized Trials with Skewed or Ordinal Outcomes
Shengxin Tu, Chun Li, Caroline De Schacht +4
Sample size calculations can be challenging with skewed continuous outcomes in randomized controlled trials (RCTs). Standard t-test-based calculations may require data transformati…
CALF-SBM: A Covariate-Assisted Latent Factor Stochastic Block Model
Sydney Louit, Evan Clark, Alexander Gelbard +3
We propose a novel network generative model extended from the standard stochastic block model by concurrently utilizing observed node-level information and accounting for network-e…
Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records
Sarah C. Lotspeich, Sheetal Kedar, Rabeya Tahir +7
The allostatic load index (ALI) is a 10-component measure of whole-person health. Data from electronic health records (EHR) present a huge opportunity to operationalize the ALI in…
Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
Cathy Shyr, Boyu Ren, Prasad Patil +1
Estimating heterogeneous treatment effects (HTEs) is crucial for precision medicine. While multiple studies can improve the generalizability of results, leveraging them for estimat…
Sequential monitoring using the Second Generation P-Value with Type I error controlled by monitoring frequency
Jonathan J. Chipman, Robert A. Greevy, Lindsay Mayberry +1
Many adaptive monitoring schemes adjust the required evidence toward a hypothesis to control Type I error. This shifts focus away from determining scientific relevance with an unco…
Rematching on-the-fly: sequential matched randomization and a case for covariate-adjusted randomization
Jonathan J. Chipman, Lindsay Mayberry, Robert A. Greevy
Covariate-adjusted randomization (CAR) can reduce the risk of covariate imbalance and, when accounted for in analysis, increase the power of a trial. Despite CAR advances, stratifi…