Showing 4 of total 4 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
sahirbhatnagar
casebase:Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression
Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.
Maintained by Sahir Bhatnagar. Last updated 7 months ago.
competing-riskscox-regressionregression-modelssurvival-analysis
9 stars 7.16 score 94 scripts
mskcc-epi-bio
tidycmprsk:Competing Risks Estimation
Provides an intuitive interface for working with the competing risk endpoints. The package wraps the 'cmprsk' package, and exports functions for univariate cumulative incidence estimates and competing risk regression. Methods follow those introduced in Fine and Gray (1999) <doi:10.1002/sim.7501>.
Maintained by Daniel D. Sjoberg. Last updated 8 months ago.
23 stars 7.06 score 157 scripts 1 dependents
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
18 stars 6.87 score 69 scripts