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
8.83 score 34 stars 2 dependents 156 scripts 7.1k 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 am 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
7.56 score 18 stars 76 scripts 4.4k 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
5.19 score 26 scripts 408 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 5 scripts 319 downloadsgoldilocks - Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints
Implements the Goldilocks adaptive trial design for a time to event outcome using a piecewise exponential model and conjugate Gamma prior distributions. The method closely follows the article by Broglio and colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore the operating characteristics of different trial designs.
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adaptivebayesianbayesian-statisticsclinical-trialsstatisticscpp
4.54 score 7 stars 4 scripts 155 downloads