R语言生存分析大全

CRAN Task View: Survival AnalysisMaintainer: Arthur Allignol and AurelienLatoucheContact:arthur.allignol at uni-ulm.deVersion:2016-01-27Survival analysis, also called event history analysis in social science, or reliability analysis in engineering, deals with time until occurrence of an event of interest. However, this failure time may not be observed within the relevant time period, producing so-called censored observations.This task view aims at presenting the useful R packages for the analysis of time to event data.Please let theStandard Survival Analysis Estimation of the Survival DistributionKaplan-Meier:The survfit function from the packagedata.(replacement of the Design package) proposes a modifiedversion of the survfit function. The package implements afast algorithm and some features not included inestimator are implemented in the package.plot.Surv ofpackage plots the Kaplan-Meier estimator. The packageleft-censored data.svykm in provides a weighted Kaplan-Meierestimator.nested.km in estimates the survival curve for each level of categorical variables with missing data.The kaplan-meier function in computes the Kaplan-Meierestimator from histogram data. The package permits tocompute a weighted Kaplan-Meier estimate. The KM function inpackage plots the survival function using a variant of theThe package computes presmoothed estimates of thedensity functions. The package permits to compute theThe package provides several functions for computingconfidence procedure). The package offers varioususing the package. The package implements the ∙(NPMLE):The package provides several ways to compute thepermits to compute the NPMLE ofThe icfit function in package computes the NPMLE forinterval-censored data. The implements several algorithms∙Parametric:The package permits to fit an univariateThe package provides routines for fitting models in the Hazard Estimation∙The package permits to estimate the hazard function through∙The epi.insthaz function from computes the instantaneous∙and allow to estimate the hazard function∙The package aims at estimating the hazard function for interval∙The package provides non-parametric smoothing of the Testing∙The survdiff function in compares survival curves using theFleming-Harrington G-rho family of test.implements this∙implements a permutation version of the logrank test and a∙The implements the shift-algorithm by Streitberg and possibly for censored data.∙SurvTest in the package implements the logrank test∙The package performs tests using maximally selected rank∙The package implements logrank and Wilcoxon type tests for ∙package.∙∙implements a two stage procedure for comparing hazard∙The package proposes to test the equality of two survival∙The package offers several tests based on the∙The package provides a logrank test for which aggregated ∙survival data can be found in the package.Regression Modelling∙Cox model:The coxph function in the package fits the Coxmodel.cph in the package and the package propose someextensions to the coxph function. The package implements the Firth's penalised maximum likelihood bias reduction method for theregression can be found in package proposesa robust implementation of the Cox model.timecox infits Cox models with possibly time-varying effects. Thefits Cox models for covariates withsurvey design using the svycoxph function inThepackageICM-algorithm. The package implements Pan's (2000)data. Thetransformation model. TheThe package implements the Cox model using an activeThe package fits Cox models using maximum penalised baseline hazard function.The cumres function in computes goodness-of-fit methods for the Cox proportional hazards model. The proportionality assumption canbe checked using the cox.zph function incalculates concordance probability estimate for the Cox model, asdoes the coxphCPE function in coxphQuantile in the latterfunction of covariates. The package computesother parametric survival models. The package permits toIn particular, it provides support forthe coxph,survreg and coxme functions. The package on Bioconductor proposes a resampling based multiple hypothesis testing that can be applied to the Cox model. Testing coefficients ofTheThe package provides hazard ratio curves that allows forThe package permits to compute the unadjusted/adjustedThe∙Parametric Proportional Hazards Model:survreg(fromThe and packages implement a proportional hazardsmodel with a parametric baseline hazard. The pphsm in translatesan AFT model to a proportional hazards form. The package includes the hare function that fits a hazard regression model, usingnecessarily, proportional. The package implements the model for the baseline survival function, and proportional hazards,proportional odds or probit functions for regression.∙Accelerated Failure Time (AFT) Models:The survreg function inpackage can fit an accelerated failure time model. Amodified version of survreg is implemented in the package(psm function). It permits to use some of the functionalities.The package also proposes an implementation of the AFT model (function aftreg). An AFT model with an error distribution assumedthe package. The package proposes the front end of the survreg function for left-censored data. A least-squarethe package. The package implements theused when covariates are subject to measurement error. A robustin package fits AFT models forestimating equations) to the AFT model.∙Additive Models:Both and fit the additive hazards model of Aalen in functions aareg and aalen,respectively.also proposes an implementation of theYing (1994) goodness-of-fit for Cox regression models) and thepartly parametric additive risk model of McKeague and Sasieni. Aavailable in the package.∙Buckley-James Models:The bj functionin and BJnoint in compute the Buckley-James model,though the latter does it without an intercept term. The packageboosting, regression trees and boosted MARS, elastic net).∙Other models:Functions like survreg can fit other types of models depending on the chosen distribution,e.g., a tobit model.The package provides the tobit function, which is a wrapperof survreg to fit the tobit model. An implementation of the tobit modelthe package. The package provides implementationmodel. The package fits the inverse Gaussian distribution tobarrier hitting time of a Wiener process, where drift towards theThe package computes the pseudo-observation for modellingrestricted mean.fits parametric time-to-event models, inprobability, and where one of the parameters is a linear function ofcovariates. The Icens function in package provides ainterval-censored data. The package can fit vector generalisedThe package implements the generalised additive modelThe locfit.censor function in produces local regressionestimates. The crq function included in the packageTheregression model is implemented in the package. Aster models,and packages.Theva package, fitsformulation. Thetime data. The packages proposes somecumulative hazards, quantiles and restricted mean.Multistate ModelsGeneral Multistate Models:The coxph function frompackage can be fitted for any transition of a multistate model.correspondence between multistate models and time-dependentcovariates. Besides, all the regression methods presented above can be used for multistate models as long as they allow forThe package provides convenient functions for estimating andTheThe package provides non-parametric estimation forand left-truncation. The package permits to estimate hazardsmultistate models. The package contains functions for fittingbe modelled in terms of covariates. The package can beused to fit semi-Markov multistate models in continuous time. The distribution of the waiting times can be chosen between theexponential, the Weibull and exponentiated Weibull distributions.TheThe package extends the package tothepackage permits to fit Cox models to the progressiveThe package fits proportional hazards models for the toward the transient state. Left-truncated and right-censored data are also allowed. The model is either parametric (Weibull) orsemi-parametric with M-splines approximation of the baselineThe package implements Lexis objects as a way to represent,Thecomputes the expectedCompeting risks:The package estimates the cumulative samples. The package also implements the Fine and Gray model forextends the package to stratified and clustered data.Thecumulative incidence functions. The package implementsPackage computes pseudo observations for modellingfunctions.does flexible regression modelling for competingand direct binomial regression approach.implementsdata. The package estimates the conditional probability of aimplements a proportional-odds model using either the temporalprocess regression or the pseudo-value approaches.Packages(via survfit) and can also be used toestimate the cumulative incidence function. The packagepackage implements acovariate-dependent censoring. The package implementsmodel for interval censored data.∙Recurrent event data:coxph from the package can be used toanalyse recurrent event data. The cph function of the package fitsfitted with the package. The latter also permits to fit jointevent. The package proposes implementations of severalPeñestimation under a Gamma Frailty model. The packageThe package implements weighted logrank type tests for Relative Survival∙The package proposes several functions to deal with relative survival data. For example,rs.surv computes a relative survivalcurve.rs.add fits an additive model and rsmul fits the Cox model ofAndersen et al. for relative survival, while rstrans fits a Cox model∙The package permits to fit relative survival models like the∙The package implements methods for population-based and the join point relative survival model.∙The package computes relative survival, absolute excess∙The package permits to fit multiplicative regression models∙The package implements time-dependent ROC curves and Random Effect Models ∙Frailties:Frailty terms can be added in coxph and survreg functionsin packagethe package. The two.stage function in the packagefits the Clayton-Oakes-Glidden model. The package fits fullyThe package fits proportional hazards models with ausing a penalised likelihood on the hazard function. The package also fits additive and nested frailty models that can be used for, e.g.,meta-analysis and for hierarchically clustered data (with 2 levels ofeffects can be fitted using the package. The package fits athe package. The package implements a linear mixeddistributions.∙data:The package allows the analysis of repeatedmodels.Multivariate Survival Multivariate survival refers to the analysis of unit, e.g., the survival of twins or a family. To analyse such data, we can estimate the joint distribution of the survival times∙Joint modelling:Both and can estimate bivariate∙The package implements various statistical models forincidence models, bivariate random effects probit models,∙The package constructs trees for multivariate survival data using Bayesian Models∙The package proposes an implementation of a bivariate∙The package computes a Bayesian model averaging for Cox∙The DPsurvint function in fits a Bayesian semi-parametricAFT model.LDDPsurvival in the same package fits a LinearDependent Dirichlet Process Mixture of survival models.∙NMixMCMC in performs an MCMC estimation of normal mixtures for censored data.∙ A MCMC for Gaussian linear regression with left-, right- or interval-censored data can be fitted usingthe MCMCtobit in∙The package estimates the hazard function from censored∙The weibullregpost function in computes the log∙The fits generalised linear mixed models using MCMC∙The package aims at drawing inference on age-specificrecords have missing information on times of birth and death.∙The package performs joint modelling of longitudinal and ∙Bayesian parametric and semi-parametric estimation forthe package.∙priors.∙The package fits a Bayesian parametric proportional hazards∙The package implements Bayesian clustering using a∙The package provides Bayesian model fitting for several process mixture model, anovaDirichlet process mixture model,proportional hazards model and marginal spatial proportional hazards∙The package implements non-parametric survival High-Dimensional Data∙Recursive partitioning:can perform logicregression.The package implements trees and bagged trees for∙implements bagging for survival data.The∙Regularised and shrinkage methods:TheTheshrinkage is available inpackage implementsThepackageregularisation.fits Cox models with penalized ridge-type ∙the package. The package includes a generic gradientmodels for right-censored data.implementsvalue of high-dimensional data. It is basedon provides routines for fitting the Cox∙Other:Thepackage can construct indextraining dataset. The package fits Cox proportionalmethod.provides partial least squares regression and variousThe package implements feature selection algorithms based on algorithm for predicting survival risk.Predictions and PredictionPerformance∙The package provides utilities to plot prediction error curves for∙implements prediction error techniques which can be∙The package permits to estimate time-dependent ROC∙computes time-dependent ROC curves and Akritas's nearest neighbour estimation method (Cumulative∙implements time-dependent ROC curves, AUC and∙Various time-dependent true/false positive rates andthe∙package provides several functions to assess and∙package.∙Thefor comparing competing risks prediction models.∙The package provides functions forsurvival data.Power Analysis∙The package proposes power calculation for weighted Log-Rank∙The permits to calculate sample size based on proportional∙The package provides power and sample sizeepidemiological studies).∙Power analysis and sample size calculation for SNP associationthe package.Simulation∙The package permits to generate data wih one binary∙The package permits the user to simulate complex survival∙The package proposes some functions for simulating∙The package also permits to simulate and analyse multistate transition hazard functions, for non-Markov models and for∙The package provides functions for simulating complex∙The package implements tools for simulating and plotting∙The package permits to simulate simple and complex∙The package provides routines for performing for the microsimulation are a multistate model, Markov ornon-Markov, for which the transition intensities are specified, as well as an initial cohort.GraphicsThis section tries to list some specialised plot functions that might be useful in the context of event history analysis.∙Theextends this to the competing risks model.∙The plot.Hist function in permits to draw the states and∙The package provides many plot functions for representing∙The package provide multistate-type graphics for competing event to each competing event describes the particular amount ofevery incidence rate.Miscellaneous∙is the companion package to "Dynamic Prediction in Clinical∙Package proposes the censboot function that implements several∙The package estimates the current cumulative∙The package provides functions for performing∙provides tools for individual patient data meta-analysis,∙The package includes the data sets from Klein andcan be found in the thatDavidson (2003) and∙The package permits to construct, validate and calibrate∙The package compute the MLE of a density (log-concave)∙The package fits parametric Transform-both-sides∙The package implements algorithms to detect outliers∙The package implements an EM algorithm to CRAN packages:∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙Related links:∙CRAN Task View:∙CRAN Task View:∙CRAN Task View:∙∙∙∙∙∙∙∙∙∙。