snSMART

The aim of the snSMART R package is to consolidate data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library.

An snSMART is a multi-stage trial design where for a two-stage design, randomization in the second stage depends on the outcome to first stage treatment. snSMART designs require that the same outcome is measured at the end of the first stage and at the end of the second stage. Additionally, the length of the first stage of the trial must be the same amount of time as the length for the second stage. snSMARTs are motivated by obtaining more information from a small sample of individuals with the primary goal to identify the superior first stage treatment or dosage level using both stages of data. Data are shared across the two stages of the snSMART design to more precisely estimate the effect of the treatments given in the first stage.

Installation

You can download and install snSMART with:

# Install devtools first if you haven't done so
library(devtools)
# install snSMART
devtools::install_github("sidiwang/snSMART")
library(snSMART)

snSMART designs and functions covered in this package

• snSMART with binary outcome (3 active treatments or placebo and 2 dose level, non-responders re-randomized)
  • Bayesian Joint Stage Model (BJSM) analysis function - BJSM_binary
  • Log Poisson Joint Stage Model (JSRM) analysis function - JSRM_binary
  • sample size calculation function - sample_size
  • Group Sequential snSMART BJSM analysis function - group_seq
• snSMART with mapping function (3 active treatments, re-randomization depends on continuous outcome at stage 1; continuous outcomes)
  • BJSM analysis function - BJSM_c

References

Chao, Y.C., Trachtman, H., Gipson, D.S., Spino, C., Braun, T.M. and Kidwell, K.M., 2020. Dynamic treatment regimens in small n, sequential, multiple assignment, randomized trials: An application in focal segmental glomerulosclerosis. Contemporary clinical trials, 92, p.105989.

Chao, Y.C., Braun, T.M., Tamura, R.N. and Kidwell, K.M., 2020. A Bayesian group sequential small n sequential multiple‐assignment randomized trial. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(3), pp.663-680.

Fang, F., Hochstedler, K.A., Tamura, R.N., Braun, T.M. and Kidwell, K.M., 2021. Bayesian methods to compare dose levels with placebo in a small n, sequential, multiple assignment, randomized trial. Statistics in Medicine, 40(4), pp.963-977.

Hartman, H., Tamura, R.N., Schipper, M.J. and Kidwell, K.M., 2021. Design and analysis considerations for utilizing a mapping function in a small sample, sequential, multiple assignment, randomized trials with continuous outcomes. Statistics in Medicine, 40(2), pp.312-326.

Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K., 2020. Sample size determination for Bayesian analysis of small n sequential, multiple assignment, randomized trials (snSMARTs) with three agents. Journal of Biopharmaceutical Statistics, 30(6), pp.1109-1120.

Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M., 2018. A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs). Statistics in medicine, 37(26), pp.3723-3732.