Showing 6 of total 6 results (show query)
kkholst
mets:Analysis of Multivariate Event Times
Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.
Maintained by Klaus K. Holst. Last updated 2 days ago.
multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp
14 stars 13.44 score 236 scripts 42 dependents
modeloriented
survex:Explainable Machine Learning in Survival Analysis
Survival analysis models are commonly used in medicine and other areas. Many of them are too complex to be interpreted by human. Exploration and explanation is needed, but standard methods do not give a broad enough picture. 'survex' provides easy-to-apply methods for explaining survival models, both complex black-boxes and simpler statistical models. They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023) <doi:10.1016/j.knosys.2022.110234>, SurvLIME described in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.
Maintained by Mikołaj Spytek. Last updated 10 months ago.
biostatisticsbrier-scorescensored-datacox-modelcox-regressionexplainable-aiexplainable-machine-learningexplainable-mlexplanatory-model-analysisinterpretable-machine-learninginterpretable-mlmachine-learningprobabilistic-machine-learningshapsurvival-analysistime-to-eventvariable-importancexai
110 stars 8.40 score 114 scripts
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
17 stars 6.85 score 69 scripts
onofriandreapg
drcte:Statistical Approaches for Time-to-Event Data in Agriculture
A specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 <doi:10.1017/wsc.2022.8>.
Maintained by Andrea Onofri. Last updated 11 days ago.
non-linear-regressionseed-germinationtime-to-event
4.07 score 39 scripts 2 dependentsonofriandreapg
drcSeedGerm:Utilities for Data Analyses in Seed Germination/Emergence Assays
Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 <doi:10.1016/j.eja.2018.08.011>. Contains several datasets for practicing.
Maintained by Andrea Onofri. Last updated 3 months ago.
nonlinear-regressionseed-germination-assaystime-to-event
5 stars 3.97 score 37 scripts
boehringer-ingelheim
BPrinStratTTE:Causal Effects in Principal Strata Defined by Antidrug Antibodies
Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.
Maintained by Christian Stock. Last updated 12 months ago.
bayesian-methodscausal-inferenceclinical-trialestimandmcmc-methodspharmaceutical-developmentprincipal-stratificationsimulationstantime-to-eventcpp
3.18 score