Showing 3 of total 3 results (show query)
drizopoulos
JMbayes2:Extended Joint Models for Longitudinal and Time-to-Event Data
Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).
Maintained by Dimitris Rizopoulos. Last updated 24 days ago.
competing-riskslongitudinal-analysismixed-modelsmulti-statepersonalized-medicineprecision-medicineprediction-modelsurvival-modelsopenblascppopenmp
84 stars 8.27 score 264 scripts 2 dependents
brian-j-smith
MachineShop:Machine Learning Models and Tools
Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.
Maintained by Brian J Smith. Last updated 7 months ago.
classification-modelsmachine-learningpredictive-modelingregression-modelssurvival-models
62 stars 7.95 score 121 scripts
giabaio
survHE:Survival Analysis in Health Economic Evaluation
Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 'remotes::install_github' from their GitHub repositories: (<https://github.com/giabaio/survHEhmc> and <https://github.com/giabaio/survHEinla/> respectively). 'survHEinla' is based on the package INLA, which is available for download at <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). <doi:10.18637/jss.v095.i14>.
Maintained by Gianluca Baio. Last updated 22 days ago.
frequentisthamiltonian-monte-carlohealth-economic-evaluationinlaplotting-survival-curvesrstansurvival-analysissurvival-modelsuncertaintyopenjdk
42 stars 6.88 score 2 dependents