joineRML - Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).
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armadillobiostatisticsclinical-trialscoxdynamicjoint-modelslongitudinal-datamultivariate-analysismultivariate-datamultivariate-longitudinal-datapredictionrcppregression-modelsstatisticssurvivalopenblascppopenmp
8.76 score 34 stars 2 dependents 170 scripts 5.6k downloadsgoldilocks - Adaptive Trial Designs for Survival and Binary Endpoints
Implements Goldilocks adaptive trial designs for time-to-event and fixed-time binary endpoints. Outcomes are generated with a piecewise exponential model, with conjugate Gamma priors used for predictive imputation. Final analyses may use log-rank, Cox, or restricted mean survival time tests, Bayesian piecewise-exponential inference, frequentist risk differences, or Bayesian beta-binomial inference. The method closely follows Broglio et al. (2014) <doi:10.1080/10543406.2014.888569> and supports simulation of design operating characteristics.
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adaptivebayesianbayesian-statisticsclinical-trialsstatisticscpp
7.39 score 7 stars 42 scripts 523 downloadsjoineR - Joint Modelling of Repeated Measurements and Time-to-Event Data
Analysis of repeated measurements and time-to-event data via random effects joint models. Fits the joint models proposed by Henderson and colleagues <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a single continuous repeated measure. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-varying covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by a latent Gaussian process. The model is estimated using an Expectation Maximization algorithm. Some plotting functions and the variogram are also included. This project is funded by the Medical Research Council (Grant numbers G0400615 and MR/M013227/1).
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biostatisticscompeting-riskscoxjoinerlongitudinal-datarepeated-measurementsrepeated-measuresstatisicsstatistical-methodssurvivalsurvival-analysistime-to-event
7.16 score 18 stars 92 scripts 1.4k downloadsbayesDP - Implementation of the Bayesian Discount Prior Approach for Clinical Trials
Functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior methodology was developed in collaboration with the The Medical Device Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.
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bayesianbayesian-inferencebayesian-statisticsclinical-trialsmdicposterior-predictiveposterior-probabilityprior-distributionopenblascpp
5.62 score 70 scripts 636 downloadsadaptDiag - Bayesian Adaptive Designs for Diagnostic Trials
Simulate clinical trials for diagnostic test devices and evaluate the operating characteristics under an adaptive design with futility assessment determined via the posterior predictive probabilities.
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adaptivebayesianbayesian-statisticsclinical-trialsdiagnostic-testsdiagnosticsstatistics
4.70 score 5 stars 7 scripts 640 downloads