Statistical Methods in Clinical Trials

Designing robust N-of-1 studies for precision medicine

The paper develops a stochastic time series model to optimize N-of-1 study designs for precision medicine, showing how design parameters affect treatment efficacy detection.

fragilityindex: An R Package for Statistical Fragility Estimates in Biomedicine

The paper introduces an R package for calculating fragility index in clinical trials and extends the method to survival analysis and logistic regression.

Statistical test with sample size and power calculation for paired repeated measures designs of method comparison studies

The paper introduces a statistical test with sample size and power calculation for paired repeated measures designs in method comparison studies, showing good finite sample performance.

The harmonic mean p-value for combining dependent tests

The paper introduces the harmonic mean p-value (HMP) to combine dependent tests, improving statistical power while controlling the family-wise error rate in large-scale hypothesis testing.

AdaFDR: a Fast, Powerful and Covariate-Adaptive Approach to Multiple Hypothesis Testing

AdaFDR improves multiple hypothesis testing by using covariates to increase discovery power while controlling false discovery rate, outperforming BH and IHW in eQTL analysis.

A direct approach to estimating false discovery rates conditional on covariates

The paper proposes a regression framework to estimate false discovery rates conditional on covariates and applies it to genome-wide association studies.

Evaluating Clinical Benefit in I-SPY 2

The paper introduces a clinical benefit index (CBI) to quantify and integrate drug efficacy and toxicity using patient-reported outcomes for adaptive randomization and regulatory evaluation.

A machine learning method for subgroup analysis of randomized controlled trials

The paper develops a machine learning method for subgroup analysis in RCTs, applying it to the SPRINT trial to identify patients who may benefit from the intervention.

Designing fecal microbiota transplant trials that account for differencesin donor stool efficacy

The paper develops a Bayesian allocation strategy to improve statistical power in FMT trials by accounting for donor stool efficacy differences.