Jack M. Wolf

A permutation procedure to detect heterogeneous treatment effects in randomized clinical trials while controlling the type I error rate

Jack M Wolf, J. Koopmeiners, D. Vock, 2022, Clinical trials.

Practical guidance on modeling choices for the virtual twins method

Ning Leng, Jeffrey A. Boatman, Joseph S. Koopmeiners, 2021, Journal of biopharmaceutical statistics.

Glucagon‐like peptide‐1 receptor agonist prescribing patterns in adolescents with type 2 diabetes

Jack M Wolf, K. Rudser, A. Kelly, 2022, Diabetes, obesity & metabolism.

tehtuner: An R package to fit and tune models for the conditional average treatment effect

Jack M. Wolf, 2023, Journal of Open Source Software.

Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks

Jack M Wolf, N. Tintle, J. Westra, 2023, Annals of human genetics.

Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks

Jason Westra, Martha Barnard, Nathan Tintle, 2019, PSB.

Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates

Jack M Wolf, N. Tintle, J. Westra, 2021, bioRxiv.