6 papers
Wilcoxon Random Forests for Robust Distributional Prediction
Danni Shi, Bryan E Shepherd, Chun Li
Random forests (RF) are tree-based models that capture complex, high-order predictor interactions. In biomedical studies, ordered outcomes often have outliers, skewness, or mixture…
Doubly Robust Estimators of Quantile Treatment Effects With Semiparametric Cumulative Probability Models
Hao Wu, Chun Li, Bryan E. Shepherd
The causal inference literature has traditionally focused on estimating the mean of the potential outcome, whereas evaluating how a treatment affects the entire outcome distributio…
Estimating treatment effects with a unified semi-parametric difference-in-differences approach
Julia C. Thome, Andrew J. Spieker, Peter F. Rebeiro +3
Difference-in-differences (DID) approaches estimate causal effects with observational data before and after an intervention. DID traditionally estimates the average treatment effec…
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…
Probability-scale residuals for event-time data
Eric S. Kawaguchi, Bryan E. Shepherd, Chun Li
The probability-scale residual (PSR) is defined as , where is the observed outcome and is a random variable from the fitted distribution. The PSR is pa…
Between- and Within-Cluster Spearman Rank Correlations
Shengxin Tu, Chun Li, Bryan E. Shepherd
Clustered data are common in practice. Clustering arises when subjects are measured repeatedly, or subjects are nested in groups (e.g., households, schools). It is often of interes…