Sample Size for SMART Designs in Non-Surgical Periodontal Trials

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+) .

SMARTp

Overview

A SMART design for non-surgical treatments of chronic periodontitis with spatially-referenced and non-randomly missing skewed outcomes.

Installation

# Install from CRAN (when available)
install.packages("SMARTp")
# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("bandyopd/SMARTp")

Usage

library(SMARTp) will load the following functions:

• CAR_cov_teeth, for generating the CAR structure.
• MC_var_yibar_mis, for estimating mean and variance of the average change in CAL for each patient by Monte Carlo method.
• SampleSize_SMARTp, for sample size calculation under a clustered SMART design for chronic periodontitis.

See ?SampleSize_SMARTp for a complete example of how to use this package.

Contact

Dipankar Bandyopadhyay, PhD, Professor: [email protected]