Package: bayesCT 0.99.3.9000

bayesCT: Simulation and Analysis of Adaptive Bayesian Clinical Trials

Simulation and analysis of Bayesian adaptive clinical trials for binomial, continuous, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors.

Authors:Thevaa Chandereng [aut, cre], Donald Musgrove [aut], Tarek Haddad [aut], Graeme L. Hickey [aut], Timothy Hanson [aut], Theodore Lystig [aut]

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bayesCT/json (API)

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

Peer review:

Bug tracker:https://github.com/thevaachandereng/bayesct/issues

Datasets:
  • binomialdata - Binomial dataset for analyzing adaptive Bayesian trials
  • normaldata - Gaussian dataset for analyzing adaptive Bayesian trials
  • survivaldata - Time-to-event dataset for analyzing adaptive Bayesian trials

On CRAN:

adaptivebayesian-methodsbayesian-trialclinical-trialsstatistical-power

29 exports 12 stars 1.71 score 42 dependencies 36 scripts 261 downloads

Last updated 4 years agofrom:e94419c3c0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 10 2024
R-4.5-winOKSep 10 2024
R-4.5-linuxOKSep 10 2024
R-4.4-winOKSep 10 2024
R-4.4-macOKSep 10 2024
R-4.3-winOKSep 10 2024
R-4.3-macOKSep 10 2024

Exports:%>%analysisbeta_priorbinomial_analysisbinomial_outcomebinomialBACTdata_binomialdata_normaldata_survivalenrollmentenrollment_rategamma_priorhistorical_binomialhistorical_normalhistorical_survivalhypothesisimputenormal_analysisnormal_outcomenormalBACTpw_exp_imputepw_exp_simrandomizationrandomizesimulatestudy_detailssurvival_analysissurvival_outcomesurvivalBACT

Dependencies:bayesDPclicodacolorspacedplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmcmcMCMCpackmgcvmunsellnlmepillarpkgconfigpurrrquantregR6RColorBrewerRcppRcppArmadillorlangscalesSparseMsurvivaltibbletidyselectutf8vctrsviridisLitewithr

Time-to-Event Outcome

Thevaa Chandereng, Donald Musgrove, Tarek Haddad, Graeme Hickey, Timothy Hanson, Theodore Lystig

Rendered fromtime-to-event.Rmdusingknitr::rmarkdownon Sep 10 2024.

Last update: 2020-07-13
Started: 2019-02-12

bayesCT: An R package for Simulation in Adaptive Bayesian Clinical Trials

Thevaa Chandereng, Donald Musgrove, Graeme L. Hickey

Rendered frombayesCT.Rmdusingknitr::rmarkdownon Sep 10 2024.

Last update: 2020-07-20
Started: 2018-08-15

Binomial Outcomes

Thevaa Chandereng, Donald Musgrove, Graeme L. Hickey

Rendered frombinomial.Rmdusingknitr::rmarkdownon Sep 10 2024.

Last update: 2020-07-20
Started: 2018-12-09

Continuous Outcomes

Thevaa Chandereng, Donald Musgrove, Graeme L. Hickey

Rendered fromnormal.Rmdusingknitr::rmarkdownon Sep 10 2024.

Last update: 2020-07-20
Started: 2018-12-11

Readme and manuals

Help Manual

Help pageTopics
Analysis wrapper functionanalysis
Beta prior for for control and treatment groupbeta_prior
Analyzing a Bayesian trial for binomial countsbinomial_analysis
Proportion of an event in control and treatmentbinomial_outcome
Binomial counts for Bayesian adaptive trialsbinomialBACT
Binomial dataset for analyzing adaptive Bayesian trialsbinomialdata
Data file for binomial analysisdata_binomial
Data file for continuous (normally distributed) data analysisdata_normal
Data file for survival analysisdata_survival
Simulating enrollment datesenrollment
Enrollment rate wrapperenrollment_rate
Gamma prior for for control and treatment groupgamma_prior
Historical data for binomial distributionhistorical_binomial
Historical data for normal distributionhistorical_normal
Historical data for survival analysishistorical_survival
Hypothesis wrapperhypothesis
Imputation wrapperimpute
Analyzing Bayesian trial for continuous (normally distributed) datanormal_analysis
Parameters for treatment and control in continuous (normally distributed) data casenormal_outcome
Normal distribution for Bayesian Adaptive trialsnormalBACT
Gaussian dataset for analyzing adaptive Bayesian trialsnormaldata
Imputes time-to-event outcomes.pw_exp_impute
Simulates time-to-event outcomes.pw_exp_sim
Randomization allocationrandomization
Randomization scheme wrapperrandomize
Simulation wrapper for binomial and normalsimulate
Details of the clinical studystudy_details
Analyzing Bayesian trial for time-to-event datasurvival_analysis
Piecewise constant hazard rates and the cutpoint for control and treatment groupsurvival_outcome
Time-to-event outcome for Bayesian Adaptive trialssurvivalBACT
Time-to-event dataset for analyzing adaptive Bayesian trialssurvivaldata