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Peer-reviewedLicence not statedThe American Statistician

Automated, Efficient, and Model-Free Inference for Randomized Clinical Trials via Data-Driven Covariate Adjustment

  • Kelly Van Lancker1,2
  • Ivan Matos Diaz3
  • Stijn Vansteelandt2
  1. 1Department of Mathematics and Data Science, Vrije Universiteit Brussel
  2. 2Department of Mathematics, Computer Science and Statistics, Ghent University
  3. 3Division of Biostatistics, Department of Population Health, New York University School of Medicine
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Cite

BibTeXRIS
BibTeX
@article{lancker2026automated,
  title = {{Automated, Efficient, and Model-Free Inference for Randomized Clinical Trials via Data-Driven Covariate Adjustment}},
  author = {Kelly Van Lancker and Ivan Matos Diaz and Stijn Vansteelandt},
  journal = {The American Statistician},
  year = {2026},
  month = jul,
  doi = {10.1080/00031305.2026.2700237},
  url = {https://arxiv.org/abs/2404.11150},
}
RIS
TY  - JOUR
TI  - Automated, Efficient, and Model-Free Inference for Randomized Clinical Trials via Data-Driven Covariate Adjustment
AU  - Kelly Van Lancker
AU  - Ivan Matos Diaz
AU  - Stijn Vansteelandt
PY  - 2026
DA  - 2026/07/08
JO  - The American Statistician
DO  - 10.1080/00031305.2026.2700237
UR  - https://arxiv.org/abs/2404.11150
SN  - 0003-1305
SN  - 1537-2731
ER  -