Package: snSMART 0.2.4

Michael Kleinsasser

snSMART: Small N Sequential Multiple Assignment Randomized Trial Methods

Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 <doi:10.1002/sim.7900>.

Authors:Sidi Wang [aut], Kelley Kidwell [aut], Michael Kleinsasser [cre]

snSMART_0.2.4.tar.gz
snSMART_0.2.4.tar.gz(r-4.7-any)snSMART_0.2.4.tar.gz(r-4.6-any)
snSMART_0.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
snSMART/json (API)

# Install 'snSMART' in R:
install.packages('snSMART', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sidiwang/snsmart/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

jagscpp

1.00 score 3 scripts 196 downloads 17 exports 49 dependencies

Last updated from:991b523ebb. Checks:4 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK150
source / vignettesOK201
linux-release-x86_64OK140
wasm-releaseOK115

Exports:BJSM_binaryBJSM_cgroup_seqLPJSM_binaryprint.BJSM_binaryprint.BJSM_cprint.BJSM_dose_binaryprint.group_seqprint.LPJSM_binaryprint.sample_sizeprint.summary.BJSM_binaryprint.summary.BJSM_cprint.summary.BJSM_dose_binaryprint.summary.group_seqprint.summary.LPJSM_binaryprint.summary.sample_sizesample_size

Dependencies:backportsbayestestRbroomclicodacondMVNormcpp11cubaturedatawizarddplyrEnvStatsevdfarvergeepackgenericsggplot2gluegtableHDIntervalinsightisobandlabelinglatticelifecyclemagrittrMASSmvtnormnortestpillarpkgconfigpracmapurrrR6RColorBrewerRcpprjagsrlangS7scalesstringistringrtibbletidyrtidyselecttruncdistutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
BJSM for snSMART (3 active treatments/placebo and 2 dose level) with binary outcomeBJSM_binary print.BJSM_binary print.BJSM_dose_binary print.summary.BJSM_binary print.summary.BJSM_dose_binary
BJSM continuous (snSMART with three active treatments and a continuous outcome design)BJSM_c print.BJSM_c print.summary.BJSM_c summary.BJSM_c
Dataset with binary outcomesdata_binary
Dose Level dataset with binary outcomesdata_dose
BJSM method for interim analysis and final analysis of group sequential trial designgroup_seq print.group_seq print.summary.group_seq
Group sequential full datagroupseqDATA_full
Group sequential data look 1groupseqDATA_look1
LPJSM for snSMART with binary outcomes (3 active treatments or placebo and two dose level)LPJSM_binary print.LPJSM_binary print.summary.LPJSM_binary summary.LPJSM_binary
Sample size calculation for snSMART with 3 active treatments and a binary outcomeprint.sample_size print.summary.sample_size sample_size summary.sample_size
Summarizing BJSM fitssummary.BJSM_binary
Summarizing BJSM fitssummary.BJSM_dose_binary
Summarizing BJSM fitssummary.group_seq
Dataset with continuous outcomestrialDataMF