Last updated on 2021-10-14
by Arthur Allignol and Aurelien Latouche
Survival 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 the
maintainers know if
something is inaccurate or missing. The Task View is also on
github. Feel free to open
or submit a pull request.
Standard Survival Analysis
Estimation of the Survival Distribution
- Kaplan-Meier: The
from the survival package computes the Kaplan-Meier
estimator for truncated and/or censored data. rms
(replacement of the Design package) proposes a modified version of
survfit function. The prodlim package
implements a fast algorithm and some features not included in
survival. Various confidence intervals and confidence
bands for the Kaplan-Meier estimator are implemented in the
plot.Surv of package
eha plots the Kaplan-Meier estimator. The
NADA package includes a function to compute the
Kaplan-Meier estimator for left-censored data.
in survey provides a weighted Kaplan-Meier estimator.
kaplan-meier function in spatstat
computes the Kaplan-Meier estimator from histogram data.
KM function in package rhosp
plots the survival function using a variant of the Kaplan-Meier
estimator in a hospitalisation risk context. The
survPresmooth package computes presmoothed estimates of
the main quantities used for right-censored data, i.e., survival,
hazard and density functions. The asbio package permits
to compute the Kaplan-Meier estimator following Pollock et
al. (1998). The bpcp package provides several functions
for computing confidence intervals of the survival distribution
(e.g., beta product confidence procedure). The lbiassurv
package offers various length-bias corrections to survival curve
estimation. The kmc package implements the Kaplan-Meier
estimator with constraints. The landest package allows
landmark estimation and testing of survival probabilities. The
jackknifeKME package computes the original and modified
jackknife estimates of Kaplan-Meier estimators. The
tranSurv package permits to estimate a survival
distribution in the presence of dependent left-truncation and
right-censoring. The condSURV package provides methods
for estimating the conditional survival function for ordered
multivariate failure time data. The gte package
implements the generalised Turnbull estimator proposed by Dehghan
and Duchesne for estimating the conditional survival function with
Non-Parametric maximum likelihood estimation (NPMLE):
The Icens package provides several ways to compute the NPMLE
of the survival distribution for various censoring and truncation
MLEcens can also be used to compute the MLE for interval-censored data.
dblcens permits to compute the NPMLE of the cumulative
distribution function for left- and right-censored data.
icfit function in package interval
computes the NPMLE for interval-censored data.
The DTDA package implements several algorithms
permitting to analyse possibly doubly truncated survival
data. npsurv computes the NPMLE of a survival function
for general interval-censored data.
Parametric: The fitdistrplus package
permits to fit an univariate distribution by maximum
likelihood. Data can be interval censored.
The vitality package provides routines for fitting
models in the vitality family of mortality models.
The muhaz package permits
to estimate the hazard function through kernel methods for right-censored data.
epi.insthaz function from epiR computes
the instantaneous hazard from the Kaplan-Meier estimator.
polspline, gss and logspline allow
to estimate the hazard function using splines.
The ICE package aims at estimating the hazard function for interval
The bshazard package provides non-parametric smoothing
of the hazard through B-splines.
survdiff function in survival
compares survival curves using the Fleming-Harrington G-rho family of test.
NADA implements this class of tests for left-censored
clinfun implements a permutation version of the
logrank test and a version of the logrank that adjusts for
The exactRankTests implements the shift-algorithm by Streitberg and Roehmel for
computing exact conditional p-values and quantiles, possibly for censored data.
SurvTest in the coin package implements
the logrank test reformulated as a linear rank test.
The maxstat package performs tests using maximally selected
The interval package implements logrank and Wilcoxon type tests
for interval-censored data.
Three generalised logrank tests and a score test for interval-censored data
are implemented in the glrt package.
survcomp compares 2 hazard ratios.
The TSHRC implements a two stage procedure for comparing
The Survgini package proposes to test the equality of
two survival distributions based on the Gini index.
The FHtest package offers several tests based on the
Fleming-Harrington class for comparing surival curves with right-
and interval-censored data.
The LogrankA package provides a logrank test for which
aggregated data can be used as input.
The short term and long term hazard ratio model for two samples
survival data can be found in the YPmodel package.
- The controlTest implements a nonparametric two-sample
procedure for comparing the median survival time.
- The survRM2 package performs two-sample comparison
of the restricted mean survival time
- The emplik2 package permits to compare two samples
with censored data using empirical likelihood ratio tests.
Cox model: The
coxph function in
the survival package fits the Cox model.
cph in the rms package and
the eha package propose some extensions to the
coxph function. The package coxphf
implements the Firth's penalised maximum likelihood bias reduction
method for the Cox model. An implementation of weighted
estimation in Cox regression can be found in coxphw.
The coxrobust package proposes a robust implementation
of the Cox model.
timecox in package timereg fits Cox models
with possibly time-varying effects. The mfp package
permits to fit Cox models with multiple fractional
polynomial. A Cox model model can be fitted to
data from complex survey design using the
function in survey. The multipleNCC package
fits Cox models using a weighted partial likelihood for nested
case-control studies. The MIICD package implements
Pan's (2000) multiple imputation approach to Cox models for
interval censored data. The ICsurv package fits Cox
models for interval-censored data through an EM algorithm.
The dynsurv package fits time-varying coefficient
models for interval censored and right censored survival data
using a Bayesian Cox model, a spline based Cox model or a
transformation model. The OrdFacReg package implements the Cox
model using an active set algorithm for dummy variables of ordered
factors. The survivalMPL package fits Cox models using
maximum penalised likelihood and provide a non parametric smooth
estimate of the baseline hazard function. A Cox model with
piecewise constant hazards can be fitted using the pch
package. The icenReg package implements several models
for interval-censored data, e.g., Cox, proportional odds, and
accelerated failure time models. A Cox type Self-Exciting
Intensity model can be fitted to right-censored data using
the coxsei package. The SurvLong contains
methods for estimation of proportional hazards models with
intermittently observed longitudinal
covariates. The plac package provides routines to fit
the Cox model with left-truncated data using augmented information
from the marginal of the truncation times.
The proportionality assumption can be checked using
cox.zph function in survival.
The CPE package calculates concordance probability
estimate for the Cox model, as does the
function in clinfun. The
the latter package draws a quantile curve of the survival
distribution as a function of covariates. The multcomp
package computes simultaneous tests and confidence intervals for
the Cox model and other parametric survival
models. The lsmeans package permits to obtain
least-squares means (and contrasts thereof) from linear models. In
particular, it provides support for
coxme functions. The multtest
package on Bioconductor proposes a resampling based multiple
hypothesis testing that can be applied to the Cox model. Testing
coefficients of Cox regression models using a Wald test with a
sandwich estimator of variance can be done using
the saws package. The rankhazard package
permits to plot visualisation of the relative importance of
covariates in a proportional hazards
model. The smoothHR package provides hazard ratio
curves that allows for nonlinear relationship between predictor
and survival. The paf package permits to compute the
unadjusted/adjusted attributable fraction function from a Cox
proportional hazards model. The PHeval package proposes
tools to check the proportional hazards assumption using a
standardised score process. The ELYP package implements
empirical likelihood analysis for the Cox Model and Yang-Prentice
Parametric Proportional Hazards Model:
survreg (from survival) fits a parametric
proportional hazards model. The eha
and mixPHM packages implement a proportional hazards
model with a parametric baseline hazard. The
in rms translates an AFT model to a proportional
hazards form. The polspline package includes
hare function that fits a hazard regression
model, using splines to model the baseline hazard. Hazards can be,
but not necessarily, proportional. The flexsurv package
implements the model of Royston and Parmar (2002). The model uses
natural cubic splines for the baseline survival function, and
proportional hazards, proportional odds or probit functions for
regression. The SurvRegCensCov package allows
estimation of a Weibull Regression for a right-censored endpoint,
one interval-censored covariate, and an arbitrary number of
Accelerated Failure Time (AFT) Models: The
survreg function in package survival can
fit an accelerated failure time model. A modified version of
survreg is implemented in the rms package
psm function). It permits to use some of the
rms functionalities. The eha package also
proposes an implementation of the AFT model (function
aftreg). An AFT model with an error distribution
assumed to be a mixture of G-splines is implemented in the
smoothSurv package. The NADA package
proposes the front end of the
survreg function for
left-censored data. The
simexaft package implements the
Simulation-Extrapolation algorithm for the AFT model, that can be
used when covariates are subject to measurement error. A robust
version of the accelerated failure time model can be found in
RobustAFT. The coarseDataTools package fits
AFT models for interval censored data.
An alternative weighting scheme for parameter estimation in
the AFT model is proposed in the imputeYn package. The
AdapEnetClass package implements elastic net
regularisation for the AFT model.
Additive Models: Both survival
and timereg fit the additive hazards model of Aalen in
respectively. timereg also proposes an implementation
of the Cox-Aalen model (that can also be used to perform the Lin,
Wei and Ying (1994) goodness-of-fit for Cox regression models) and
the partly parametric additive risk model of McKeague and
Sasieni. A version of the Cox-Aalen model for interval censored
data is available in the coxinterval
package. The uniah package fits shape-restricted
additive hazards models. The addhazard package contains
tools to fit additive hazards model to random sampling, two-phase
sampling and two-phase sampling with auxiliary information.
bj function in rms and
BJnoint in emplik compute the
Buckley-James model, though the latter does it without
an intercept term. The bujar package fits the Buckley-James
model with high-dimensional covariates (L2 boosting, regression
trees and boosted MARS, elastic net).
Other models: Functions like
can fit other types of models depending on the chosen
distribution, e.g., a tobit model. The AER
package provides the
tobit function, which is a
survreg to fit the tobit model. An
implementation of the tobit model for cross-sectional data and
panel data can be found in the censReg package. The
timereg package provides implementation of the
proportional odds model and of the proportional excess hazards
model. The invGauss package fits the inverse Gaussian
distribution to survival data. The model is based on describing
time to event as the barrier hitting time of a Wiener process,
where drift towards the barrier has been randomized with a
Gaussian distribution. The pseudo package computes the
pseudo-observation for modelling the survival function based on
the Kaplan-Meier estimator and the restricted mean. The
fastpseudo package dose the same for the restricted
mean survival time. flexsurv fits parametric
time-to-event models, in which any parametric distribution can be
used to model the survival probability, and where one of the
parameters is a linear function of covariates. The
Icens function in package Epi provides a
multiplicative relative risk and an additive excess risk model for
interval-censored data. The VGAM package can fit
vector generalised linear and additive models for censored data.
The gamlss.cens package implements the generalised
additive model for location, scale and shape that can be fitted to
censored data. The
locfit.censor function in
locfit produces local regression estimates. The
crq function included in the quantreg
package implements a conditional quantile regression model for
censored data. The JM package fits shared parameter
models for the joint modelling of a longitudinal response and
event times. The temporal process regression model is implemented
in the tpr package. Aster models, which combine
aspects of generalized linear models and Cox models, are
implemented in the aster and aster2
packages. The concreg package implements conditional
logistic regression for survival data as an alternative to the Cox
model when hazards are non-proportional. The
surv2sampleComp packages proposes some model-free
contrast comparison measures such as difference/ratio of
cumulative hazards, quantiles and restricted mean. The
rstpm2 package provides link-based survival models that
extend the Royston-Parmar models, a family of flexible parametric
models. The TransModel package implements a unified
estimation procedure for the analysis of censored data using
linear transformation models. The ICGOR fits the
generalized odds rate hazards model to interval-censored data
while GORCure generalized odds rate mixture cure model
to interval-censored data. The thregI package permits
to fit a threshold regression model for interval-censored data
based on the first-hitting-time of a boundary by the sample path
of a Wiener diffusion process. The miCoPTCM package
fits semiparametric promotion time cure models with possibly
mis-measured covariates. The smcure package permits to fit
semiparametric proportional hazards and accelerated failure time
mixture cure models. The case-base sampling
approach for fitting flexible hazard regression models to survival
data with single event type or multiple competing causes via
logistic and multinomial regression can be found in package
General Multistate Models: The
function from package survival can be fitted for any
transition of a multistate model. It can also be used for
comparing two transition hazards, using correspondence between
multistate models and time-dependent covariates. Besides, all the
regression methods presented above can be used for multistate
models as long as they allow for left-truncation.
mvna package provides convenient functions for
estimating and plotting the cumulative transition hazards in any
multistate model, possibly subject to right-censoring and
etm package estimates and plots transition
probabilities for any multistate models. It can also estimate the
variance of the Aalen-Johansen estimator, and handles
left-truncated data. The
msSurv package provides non-parametric estimation for
multistate models subject to right-censoring (possibly
state-dependent) and left-truncation. The mstate
package permits to estimate hazards and probabilities, possibly
depending on covariates, and to obtain prediction probabilities in
the context of competing risks and multistate models. The
msm package contains functions for fitting general
continuous-time Markov and hidden Markov multistate models to
longitudinal data. Transition rates and output processes can be
modelled in terms of covariates. The msmtools package
provides utilities to facilitate the modelling of longitudinal
data under a multistate framework using the msm
package.The SemiMarkov package can be used to fit
semi-Markov multistate models in continuous time. The
distribution of the waiting times can be chosen between the
exponential, the Weibull and exponentiated Weibull distributions.
Non-parametric estimates in illness-death models and other three
state models can be obtained with package
p3state.msm. The TPmsm package permits to
estimate transition probabilities of an illness-death model or
three-state progressive model. The gamboostMSM package
extends the mboost package to estimation in the
mulstistate model framework, while the penMSM package
proposes L1 penalised estimation. The coxinterval
package permits to fit Cox models to the progressive illness-death
model observed under right-censored survival times and interval-
or right-censored progression times. The SmoothHazard
package fits proportional hazards models for the illness-death model
with possibly interval-censored data for transition toward the
transient state. Left-truncated and right-censored data are also
allowed. The model is either parametric (Weibull) or
semi-parametric with M-splines approximation of the baseline
intensities. The TP.idm package implement the estimator
of Una-Alvarez and Meira-Machado (2015) for non-Markov
The Epi package implements Lexis objects as a way to
represent, manipulate and summarise data from multistate models.
The LexisPlotR package, based on ggplot2,
permits to draw Lexis diagrams. The TraMineR package is
intended for analysing state or event sequences that describe life
courses. asbio computes the expected numbers of
individuals in specified age classes or life stages given
survivorship probabilities from a transition matrix.
Competing risks: The package cmprsk
estimates the cumulative incidence functions, but they can be
compared in more than two samples. The package also implements
the Fine and Gray model for regressing the subdistribution hazard
of a competing risk.
crrSC extends the cmprsk package to
stratified and clustered data. The kmi package
performs a Kaplan-Meier multiple imputation to recover missing
potential censoring information from competing risks events,
permitting to use standard right-censored methods to analyse
cumulative incidence functions. The crrstep package
implements stepwise covariate selection for the Fine and Gray
model. Package pseudo computes pseudo observations for
modelling competing risks based on the cumulative incidence
timereg does flexible regression modelling for
competing risks data based on the on the
inverse-probability-censoring-weights and direct binomial
riskRegression implements risk regression for competing
risks data, along with other extensions of existing packages
useful for survival analysis and competing risks data.
The Cprob package estimates the conditional probability
of a competing event, aka., the conditional cumulative
incidence. It also implements a proportional-odds model using
either the temporal process regression or the pseudo-value
approaches. Packages survival
survfit) and prodlim can also be used
to estimate the cumulative incidence function.
package implements the semi-parametric mixture model for competing
risks data. The MIICD package implements Pan's (2000)
multiple imputation approach to the Fine and Gray model for
interval censored data. The CFC package permits to
perform Bayesian, and non-Bayesian, cause-specific competing risks
analysis for parametric and non-parametric survival
functions. The gcerisk package provides some methods
for competing risks data. Estimation, testing and regression
modeling of subdistribution functions in the competing risks
setting using quantile regressions can be had
in cmprskQR. The intccr package permits to
fit the Fine and Gray model as well other models that belong to
the class of semiparametric generalized odds rate transformation
models to interval-censored competing risks data.
Recurrent event data:
coxph from the
survival package can be used to analyse recurrent event
cph function of the rms package
fits the Anderson-Gill model for recurrent events, model that can
also be fitted with the frailtypack package. The latter
also permits to fit joint frailty models for joint modelling of
recurrent events and a terminal event. The condGEE
package implements the conditional GEE for recurrent event gap
times. The reda package provides function to fit gamma
frailty model with either a piecewise constant or a spline as the
baseline rate function for recurrent event data, as well as some
miscellaneous functions for recurrent event data. Several
regression models for recurrent event data are implemented in
the reReg package. The spef package includes
functions for fitting semiparametric regression models for panel
count survival data.
The relsurv package proposes several functions to deal
with relative survival data. For example,
rs.surv computes a relative
rs.add fits an additive model and
fits the Cox model of Andersen et al. for relative survival, while
fits a Cox model in transformed time.
The timereg package permits to fit relative survival models like
the proportional excess and additive excess models.
The mexhaz package allows fitting an hazard regression
model using different shapes for the baseline hazard. The model
can be used in the relative survival setting (excess mortality
hazard) as well as in the overall survival setting (overall
The flexrsurv package implements the models of Remontet
et al. (2007) and Mahboubi et al. (2011).
The survexp.fr package computes relative survival,
absolute excess risk and standardized mortality ratio based on
French death rates.
The MRsurv package permits to fit multiplicative
regression models for relative survival.
Random Effect Models
Frailties: Frailty terms can be added in
survreg functions in package
survival. A mixed-effects Cox model is implemented in
the coxme package. The
in the timereg package fits the Clayton-Oakes-Glidden
model. The parfm package fits fully parametric frailty
models via maximisation of the marginal likelihood. The
frailtypack package fits proportional hazards models
with a shared Gamma frailty to right-censored and/or
left-truncated data using 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 of clustering), respectively. The
lmec package fits a linear mixed-effects model for
left-censored data. The Cox model using h-likelihood estimation
for the frailty terms can be fitted using the frailtyHL
package. The tlmec package implements a linear mixed
effects model for censored data with Student-t or normal
distributions. The frailtySurv package simulates and
fits semiparametric shared frailty models under a wide range of
frailty distributions. The parfm package implements
parametric frailty models by maximum marginal likelihood. The
PenCoxFrail package provides a regularisation approach
for Cox frailty models through penalisation. The
mexhaz enables modelling of the excess hazard
regression model with time-dependent and/or non-linear effect(s)
and a random effect defined at the cluster level. The
frailtyEM package contains functions for fitting shared
frailty models with a semi-parametric baseline hazard with the
Expectation-Maximization algorithm. Supported data formats include
clustered failures with left truncation and recurrent events in
gap-time or Andersen-Gill format
Joint modelling of time-to-event and longitudinal
data: The joineR package allows the analysis
of repeated measurements and time-to-event data via joint random
effects models. The joint.Cox package performs Cox
regression and dynamic prediction under the joint frailty-copula
model between tumour progression and death for
meta-analysis. JointModel fits semiparametric
regression model for longitudinal responses and a semiparametric
transformation model for time-to-event
data. The joineRML package fits the joint model
proposed by Henderson and colleagues
but extended to the case of multiple continuous longitudinal
measures. The rstanarm package fits joint models for
one or more longitudinal outcomes (continuous, binary or count
data) and a time-to-event, estimated under a Bayesian framework.
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
Both Icens and MLEcens can estimate bivariate
survival data subject to interval censoring.
The mets package implements various statistical models
for multivariate event history data, e.g., multivariate cumulative
incidence models, bivariate random effects probit models,
The MST package constructs trees for multivariate
survival data using marginal and frailty models.
The SurvCorr package permits to estimate correlation
coefficients with associated confidence limits for bivariate,
partially censored survival times.
The bayesSurv package proposes an implementation of a bivariate
The package BMA computes a Bayesian model averaging for
Cox proportional hazards models.
NMixMCMC in mixAK 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 using the
MCMCtobit in MCMCpack.
The BayHaz package estimates the hazard function from censored
data in a Bayesian framework.
weibullregpost function in LearnBayes computes
the log posterior density for a Weibull proportional-odds regression model.
The MCMCglmm fits generalised linear mixed models using MCMC
to right-, left- and interval censored data.
The BaSTA package aims at drawing inference on
age-specific mortality from capture-recapture/recovery data when
some or all records have missing information on times of birth
and death. Covariates can also be included in the model.
The JMbayes package performs joint modelling of
longitudinal and time-to-event data under a bayesian approach.
The rstanarm package fits a joint model for one or more
longitudinal outcomes (continuous, binary or count data) and a
time-to-event under a Bayesian framework.
Bayesian parametric and semi-parametric estimation for
semi-competing risks data is available via the SemiCompRisks
- The psbcGroup package implements penalized
semi-parametric Bayesian Cox models with elastic net, fused lasso and
group lasso priors.
The PReMiuM package implements Bayesian clustering
using a Dirichlet process mixture model to censored responses.
The spBayesSurv package provides Bayesian model fitting
for several survival models including spatial copula, linear
dependent Dirichlet process mixture model, anova Dirichlet process
mixture model, proportional hazards model and marginal spatial
proportional hazards model.
The IDPSurvival package implements non-parametric
survival analysis techniques using a prior near-ignorant Dirichlet
The ICBayes packages permits to fit Bayesian
semiparametric regression survival models (proportional hazards
model, proportional odds model, and probit model) to
interval-censored time-to-event data
The BayesPiecewiseICAR package fits a piecewise
exponential hazard to survival data using a Hierarchical Bayesian
rpart implements CART-like trees that can be used with
The party package implements recursive partitioning for survival
LogicReg can perform logic regression.
kaps implements K-adaptive partitioning and recursive
partitioning algorithms for censored survival data.
The DStree package implements trees and bagged trees
for discrete-times survival data. The LTRCtrees package
provides recursive partition algorithms designed for fitting
survival tree with left-truncated and right censored data. The
package also includes an implementation of recursive partitioning
(conditional inference trees) for interval-censored
data. bnnSurvival implements a bootstrap aggregated
version of the k-nearest neighbors survival probability prediction
Random forest: Package ipred implements
bagging for survival data. The randomForestSRC package
fits random forest to survival data, while a variant of the random
forest is implemented in party. A faster implementation
can be found in package ranger. An alternative
algorithm for random forests is implemented in icRSF.
Regularised and shrinkage methods:
The glmnet package provides procedures for fitting the
entire lasso or elastic-net regularization path for Cox models.
The glmpath package implements a L1 regularised Cox
proportional hazards model. An L1 and L2 penalised Cox models are
available in penalized. The pamr package
computes a nearest shrunken centroid for survival gene expression
data. The lpc package
implements the lassoed principal components method.
The ahaz package implements the LASSO and elastic net
estimator for the additive risk model. The fastcox
package implements the Lasso and elastic-net penalized Cox's
regression using the cockail algorithm. The SGL
package permits to fit Cox models with a combination of lasso and
group lasso regularisation. The hdnom package implements 9
types of penalised Cox regression methods and provides methods for
model validation, calibration, comparison, and nomogram
visualisation. A penalised version of the Fine
and Gray model can be found
in crrp. The Cyclops package implements
cyclic coordinate descent for the Cox proportional hazards model.
Gradient boosting for the Cox model is implemented in the gbm
The mboost package includes a generic gradient boosting algorithm
for the construction of prognostic and diagnostic models for right-censored data.
Other: The superpc package implements
the supervised principal components for survival data.
The compound.Cox package fits Cox proportional hazards
model using the compound covariate method. plsRcox
provides partial least squares regression and various techniques
for fitting Cox models in high dimensionnal
settings. The mlr3proba package, part of the mlr3
ecosystem implements survival models, including classical models
(Cox, AFT) and machine learning models(random forests, SVMs).
Predictions and Prediction Performance
The pec package provides utilities to plot prediction
error curves for several survival
models. The riskRegression package now includes most of
the functionality of the pec package.
peperr implements prediction error techniques which can
be computed in a parallelised way. Useful for high-dimensional
The timeROC package permits to estimate time-dependent
ROC curves and time-dependent AUC with censored data, possibly
with competing risks.
survivalROC computes time-dependent ROC curves and time-dependent AUC from
censored data using Kaplan-Meier or Akritas's nearest neighbour estimation method
(Cumulative sensitivity and dynamic specificity).
tdROCcan be used to compute time-dependent ROC curve
from censored survival data using nonparametric weight
risksetROC implements time-dependent ROC curves,
AUC and integrated AUC of Heagerty and Zheng (Biometrics, 2005).
Various time-dependent true/false positive rates and
Cumulative/Dynamic AUC are implemented in the survAUC package.
The survcomp package provides several functions to
assess and compare the performance of survival models.
C-statistics for risk prediction models with censored survival
data can be computed via the survC1 package.
The survIDINRI package implements the integrated
discrimination improvement index and the category-less net
reclassification index for comparing competing risks prediction
- The compareC package permits to compare C indices
with right-censored survival outcomes
The APtools package provide tools to estimate the
average positive predictive values and the AUC for risk scores or
The NPHMC permits to calculate sample size based on
proportional hazards mixture cure models.
The powerSurvEpi package provides power and sample size
calculation for survival analysis (with a focus towards
Power analysis and sample size calculation for SNP association
studies with time-to-event outcomes can be done using
the survSNP package.
The genSurv package permits to generate data wih one
binary time-dependent covariate and data stemming from a
progressive illness-death model.
The PermAlgo package permits the user to simulate
complex survival data, in which event and censoring times could be
conditional on an user-specified list of (possibly time-dependent)
The prodlim package proposes some functions for
simulating complex event history data.
The gems package also permits to simulate and analyse
multistate models. The package allows for a general specification
of the transition hazard functions, for non-Markov models and
for dependencies on the history.
The simMSM package provides functions for simulating
complex multistate models data with possibly nonlinear baseline
hazards and nonlinear covariate effects.
The simPH package implements tools for simulating and
plotting quantities of interest estimated from proportional
The survsim package permits to simulate simple and
complex survival data such as recurrent event data and competing
- The simsurv package enables the user to simulate
survival times from standard parametric survival distributions
(exponential, Weibull, Gompertz), 2-component mixture distributions,
or a user-defined hazard or log hazard function. Time dependent
effects (i.e. non-proportional hazards) can be included by
interacting covariates with linear time or some transformation of
The MicSim package provides routines for performing
continuous-time microsimulation for population projection. The
basis for the microsimulation are a multistate model, Markov or
non-Markov, for which the transition intensities are specified, as
well as an initial cohort.
- The SimHaz package permits to simulate data with a
dichotomous time-dependent exposure.
- The SimSCRPiecewise package can be used to simulate
univariate and semi-competing risks data given covariates and
piecewise exponential baseline hazards.
- The SimSurvNMarker package provides functions to
simulate from joint survival and potentially multivariate marker
models. User-defined basis expansions in time can be passed
which effect the log hazard, the markers, and the association
between the two.
This section tries to list some specialised plot functions that might be
useful in the context of event history analysis.
The rms package proposes
functions for plotting survival curves with the at risk table aligned to
the x axis. prodlim extends this to the competing risks
plot.Hist function in prodlim permits
to draw the states and transitions that characterize a multistate
The Epi package provides many plot functions for
representing multistate data, in particular Lexis diagrams.
The FamEvent generates time-to-event outcomes for
families that habour genetic mutation under different sampling
designs and estimates the penetrance functions for family data
with ascertainment correction.
The survminer package contains the
ggsurvplot for drawing survival curves with
the 'number at risk' table. Other functions are also available for
visual examinations of cox model assumptions.
The InformativeCensoring package multiple imputation
methods for dealing with informative censoring.
The discSurv provides data transformations, estimation
utilities, predictive evaluation measures and simulation functions for
discrete time survival analysis.
dynpred is the companion package to "Dynamic Prediction
in Clinical Survival Analysis".
Package boot proposes the
censboot function that
implements several types of bootstrap techniques for right-censored data.
The currentSurvival package estimates the current
cumulative incidence and the current leukaemia free survival function.
The survJamda package provides functions for performing meta-analyses
of gene expression data and to predict patients' survival and risk assessment.
The KMsurv package includes the data sets from Klein
and Moeschberger (1997). The package
SMPracticals that accompanies Davidson (2003)
and DAAG that accompanies Maindonald, J.H. and Braun,
W.J. (2003, 2007) also contain survival data sets.
The SvyNom package permits to construct, validate and
calibrate nomograms stemming from complex right-censored survey
The logconcens package compute the MLE of a density
(log-concave) possibly for interval censored data.
The TBSSurvival package fits parametric
Transform-both-sides models used in reliability analysis
- The OutlierDC package implements algorithms to detect outliers
based on quantile regression for censored data.
The coarseDataTools package implements an EM algorithm
to estimate the relative case fatality ratio between two groups.
- The GSSE package proposes a fully efficient sieve
maximum likelihood method to estimate genotype-specific distribution
of time-to-event outcomes under a nonparametric model
- power and sample size calculation based on the difference in
restricted mean survival times can be performed using
the SSRMST package.
The survMisc provides miscellaneous routines to help in
the analysis of right-censored survival data.
Accompanying data sets to the book Applied Survival Analysis
Using R can be found in package asaur.