Sample Size for SMART Designs in Non-Surgical Periodontal Trials
Overview
Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) arXiv:1902.09386.
- Adding the optimal design
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Maintainer: ‘Dipankar Bandyopadhyay <bandyopd@gmail.com>’
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family = "Xu",
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email = "bandyopd@gmail.com"),
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<arXiv:1902.09386>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
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Documentation
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Maintainer since 0.1.1 (2019)
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.1.1Latest2019-05-17 · current release
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2019-05-17
- Total releases
- 1 / 7 yrs
- License
- LGPL (>= 2) OSI
- Minimum R
- ≥ 3.5
- Download size
- 13 KB
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