Showing 4 of total 4 results (show query)
graemeleehickey
joineR: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).
Maintained by Graeme L. Hickey. Last updated 3 months ago.
biostatisticscompeting-riskscoxjoinerlongitudinal-datarepeated-measurementsrepeated-measuresstatisicsstatistical-methodssurvivalsurvival-analysistime-to-event
74.8 match 18 stars 6.87 score 69 scripts
mwhalen18
sewage:A Light-Weight Data Pipelining Tool
Provides a simple interface to developing complex data pipelines which can be executed in a single call. 'sewage' makes it easy to test, debug, and share data pipelines through it's interface and visualizations.
Maintained by Matthew Whalen. Last updated 2 years ago.
5.3 match 5 stars 3.40 score 3 scripts
canmod
iidda.analysis:Tools for Analyzing IIDDA Datasets
This package contains tools for working with data obtained from the International Infectious Disease Data Archive.
Maintained by Steven Walker. Last updated 4 months ago.
2.3 match 5.65 score 23 scriptsgraemeleehickey
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).
Maintained by Graeme L. Hickey. Last updated 1 months ago.
armadillobiostatisticsclinical-trialscoxdynamicjoint-modelslongitudinal-datamultivariate-analysismultivariate-datamultivariate-longitudinal-datapredictionrcppregression-modelsstatisticssurvivalopenblascppopenmp
1.2 match 30 stars 8.93 score 146 scripts 1 dependents