CRAN Task View: Survival Analysis
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
something is inaccurate or missing.
Standard Survival Analysis
Estimation of the Survival Distribution
function from the
computes the Kaplan-Meier estimator for truncated and/or censored data.
(replacement of the Design package)
proposes a modified version of the
package implements a fast algorithm and some features
not included in
Various confidence intervals and confidence bands for the Kaplan-Meier estimator
are implemented in the
the Kaplan-Meier estimator.
package includes a function to compute the Kaplan-Meier
estimator for left-censored data.
provides a weighted
survival curve for each level of categorical variables with
missing data. The
computes the Kaplan-Meier estimator from
histogram data. The
package permits to compute a
weighted Kaplan-Meier estimate. The
plots the survival function using a
variant of the Kaplan-Meier estimator in a hospitalisation risk
presmoothed estimates of the main quantities used for
right-censored data, i.e., survival, hazard and density functions.
package permits to compute the Kaplan-Meier
estimator following Pollock et al. (1998). The
package provides several functions for computing confidence
intervals of the survival distribution (e.g., beta product
confidence procedure). The
various length-bias corrections to survival curve
estimation. Non-Parametric confidance bands for the Kaplan-Meier
estimator can be computed using the
package implements the Kaplan-Meier estimator
with constraints. The
package allows landmark
estimation and testing of survival
package computes the
original and modified jackknife estimates of Kaplan-Meier
package permits to estimate a
survival distribution in the presence of dependent left-truncation
and right-censoring. The
methods for estimating the conditional survival function for
ordered multivariate failure time data. The
implements the generalised Turnbull estimator proposed by Dehghan
and Duchesne for estimating the conditional survival function with
Non-Parametric maximum likelihood estimation (NPMLE):
package provides several ways to compute the NPMLE
of the survival distribution for various censoring and truncation
can also be used to compute the MLE for interval-censored data.
permits to compute the NPMLE of the cumulative
distribution function for left- and right-censored data.
function in package
computes the NPMLE for interval-censored data.
package implements several algorithms
permitting to analyse possibly doubly truncated survival
computes the NPMLE of a survival function
for general interval-censored data.
permits to fit an univariate distribution by maximum
likelihood. Data can be interval censored.
package provides routines for fitting
models in the vitality family of mortality models.
to estimate the hazard function through kernel methods for right-censored data.
the instantaneous hazard from the Kaplan-Meier estimator.
to estimate the hazard function using splines.
package aims at estimating the hazard function for interval
package provides non-parametric smoothing
of the hazard through B-splines.
compares survival curves using the Fleming-Harrington G-rho family of test.
implements this class of tests for left-censored
implements a permutation version of the
logrank test and a version of the logrank that adjusts for
implements the shift-algorithm by Streitberg and Roehmel for
computing exact conditional p-values and quantiles, possibly for censored data.
the logrank test reformulated as a linear rank test.
package performs tests using maximally selected
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
compares 2 hazard ratios.
implements a two stage procedure for comparing
package proposes to test the equality of
two survival distributions based on the Gini index.
package offers several tests based on the
Fleming-Harrington class for comparing surival curves with right-
and interval-censored data.
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
implements a nonparametric two-sample
procedure for comparing the median survival time.
package performs two-sample comparison
of the restricted mean survival time
package permits to compare two samples
with censored data using empirical likelihood ratio tests.
package fits the Cox model.
package propose some extensions to the
function. The package
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
package proposes a robust implementation
of the Cox model.
fits Cox models
with possibly time-varying effects. The
permits to fit Cox models with multiple fractional
fits Cox models for
covariates with missing data. A Cox model model can be fitted to
data from complex survey design using the
fits Cox models using a weighted partial likelihood for nested
case-control studies. The
Pan's (2000) multiple imputation approach to Cox models for
interval censored data. The
package fits Cox
models for interval-censored data through an EM algorithm.
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
the Cox proportional hazards model with shape constrained hazard
package implements the Cox
model using an active set algorithm for dummy variables of ordered
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
allows nonparametric estimation of
an isotonic covariate effect for proportional hazards
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
methods for estimation of proportional hazards models with
intermittently observed longitudinal
package provides routines to fit
the Cox model with left-truncated data using augmented information
from the marginal of the truncation times.
goodness-of-fit methods for the Cox proportional hazards model.
The proportionality assumption can be checked using
package calculates concordance probability
estimate for the Cox model, as does the
the latter package draws a quantile curve of the survival
distribution as a function of covariates. The
package computes simultaneous tests and confidence intervals for
the Cox model and other parametric survival
package permits to obtain
least-squares means (and contrasts thereof) from linear models. In
particular, it provides support for
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
permits to plot visualisation of the relative importance of
covariates in a proportional hazards
package provides hazard ratio
curves that allows for nonlinear relationship between predictor
and survival. The
package permits to compute the
unadjusted/adjusted attributable fraction function from a Cox
proportional hazards model. The
tools to check the proportional hazards assumption using a
standardised score process. The
empirical likelihood analysis for the Cox Model and Yang-Prentice
Parametric Proportional Hazards Model:
survival) fits a parametric
proportional hazards model. The
packages implement a proportional hazards
model with a parametric baseline hazard. The
translates an AFT model to a proportional
hazards form. The
function that fits a hazard regression
model, using splines to model the baseline hazard. Hazards can be,
but not necessarily, proportional. The
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
estimation of a Weibull Regression for a right-censored endpoint,
one interval-censored covariate, and an arbitrary number of
Accelerated Failure Time (AFT) Models:
function in package
fit an accelerated failure time model. A modified version of
is implemented in the
function). It permits to use some of the
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
proposes the front end of the
left-censored data. A least-square principled implementation of
the AFT model can be found in the
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
AFT models for interval censored data. The
package implements both rank-based estimates and least square
estimates (via generalised estimating equations) to the AFT
model. An alternative weighting scheme for parameter estimation in
the AFT model is proposed in the
package implements elastic net
regularisation for the AFT model.
fit the additive hazards model of Aalen in
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
package fits shape-restricted
additive hazards models. The
tools to fit additive hazards model to random sampling, two-phase
sampling and two-phase sampling with auxiliary information.
Buckley-James model, though the latter does it without
an intercept term. The
package fits the Buckley-James
model with high-dimensional covariates (L2 boosting, regression
trees and boosted MARS, elastic net).
can fit other types of models depending on the chosen
, a tobit model. The
package provides the
function, which is a
to fit the tobit model. An
implementation of the tobit model for cross-sectional data and
panel data can be found in the
package provides implementation of the
proportional odds model and of the proportional excess hazards
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
package computes the
pseudo-observation for modelling the survival function based on
the Kaplan-Meier estimator and the restricted
package dose the same for the
restricted mean survival time.
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.
function in package
a multiplicative relative risk and an additive excess risk model
for interval-censored data. The
package can fit
vector generalised linear and additive models for censored data.
package implements the generalised
additive model for location, scale and shape that can be fitted to
censored data. The
produces local regression estimates.
function included in the
package implements a conditional quantile regression model for
censored data. The
package fits shared parameter
models for the joint modelling of a longitudinal response and
event times. The temporal process regression model is implemented
package. Aster models, which combine
aspects of generalized linear models and Cox models, are
implemented in the
package implements conditional
logistic regression for survival data as an alternative to the Cox
model when hazards are non-proportional.
extension of the
package, fits latent variable models
for censored outcomes via a probit link
package implements Markov
beta and gamma processes for modelling the hazard ratio for
discrete failure time data. The
packages proposes some model-free contrast comparison measures
such as difference/ratio of cumulative hazards, quantiles and
restricted mean. The
package provides link-based
survival models that extend the Royston-Parmar models, a family of
flexible parametric models. The
implements a unified estimation procedure for the analysis of
censored data using linear transformation
package fits a flexible parametric
regression model to possibly right-censored, left-truncated
fits the generalized odds rate hazards
model to interval-censored data while
generalized odds rate mixture cure model to interval-censored
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
semiparametric promotion time cure models with possibly
mis-measured covariates. The
implements semiparametric cure rate estimators for interval
censored data. The
package permits to fit
semiparametric proportional hazards and accelerated failure time
mixture cure models.
General Multistate Models:
function from package
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.
package provides convenient functions for
estimating and plotting the cumulative transition hazards in any
multistate model, possibly subject to right-censoring and
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
package provides non-parametric estimation for
multistate models subject to right-censoring (possibly
state-dependent) and left-truncation. The
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
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
provides utilities to facilitate the modelling of longitudinal
data under a multistate framework using the
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
package permits to
estimate transition probabilities of an illness-death model or
three-state progressive model. The
package to estimation in the
mulstistate model framework, while the
proposes L1 penalised estimation. The
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
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
package implement the estimator
of Una-Alvarez and Meira-Machado (2015) for non-Markov
package implements Lexis objects as a way to
represent, manipulate and summarise data from multistate models.
package, based on
permits to draw Lexis diagrams. The
intended for analysing state or event sequences that describe life
package permits to describe and
analyse life histories following a multistate perspective on the
computes the expected numbers of
individuals in specified age classes or life stages given
survivorship probabilities from a transition matrix.
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.
stratified and clustered data. The
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
implements stepwise covariate selection for the Fine and Gray
computes pseudo observations for
modelling competing risks based on the cumulative incidence
does flexible regression modelling for
competing risks data based on the on the
inverse-probability-censoring-weights and direct binomial
implements risk regression for competing
risks data, along with other extensions of existing packages
useful for survival analysis and competing risks data.
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
can also be used
to estimate the cumulative incidence function.
package estimates event-specific incidence
rates, rate ratios, event-specific incidence proportions and
cumulative incidence functions. The
package implements the semi-parametric mixture model for competing
risks data. The
package implements a
proportional subdistribution hazards model with adjustment for
covariate-dependent censoring. The
implements Pan's (2000) multiple imputation approach to the Fine
and Gray model for interval censored data. The
package provides graphical and analytical approaches for checking
the assumptions of the Fine and Gray model. The
package permits to perform Bayesian, and non-Bayesian,
cause-specific competing risks analysis for parametric and
non-parametric survival functions. The
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
Recurrent event data:
package can be used to analyse recurrent event
function of the
fits the Anderson-Gill model for recurrent events, model that can
also be fitted with the
package. The latter
also permits to fit joint frailty models for joint modelling of
recurrent events and a terminal event. The
package proposes implementations of several models for recurrent
events data, such as the Peña-Strawderman-Hollander,
Wang-Chang estimators, and MLE estimation under a Gamma Frailty
package implements the conditional
GEE for recurrent event gap times. The
package implements weighted logrank type tests for recurrent
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
package proposes several functions to deal
with relative survival data. For example,
computes a relative
fits an additive model and
fits the Cox model of Andersen et al. for relative survival, while
fits a Cox model in transformed time.
package permits to fit relative survival models like
the proportional excess and additive excess models.
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
package implements the models of Remontet
et al. (2007) and Mahboubi et al. (2011).
package implements methods for
population-based survival analysis, like the proportional hazard
relative survival model and the join point relative survival model.
package computes relative survival,
absolute excess risk and standardized mortality ratio based on
French death rates.
package permits to fit multiplicative
regression models for relative survival.
allows for estimation of EdererII and Pohar
Perme relative / net survival as well as standardized mortality
package implements time-dependent ROC curves
and extensions to relative survival.
Random Effect Models
Frailty terms can be added in
functions in package
survival. A mixed-effects Cox model is implemented in
package fits the Clayton-Oakes-Glidden
package fits fully parametric frailty
models via maximisation of the marginal likelihood. The
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. A
proportional hazards model with mixed effects can be fitted using
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
package implements a linear mixed effects model for censored data
with Student-t or normal distributions. The
package simulates and fits semiparametric shared frailty models
under a wide range of frailty distributions. The
package implements parametric frailty models by maximum marginal
package provides a
regularisation approach for Cox frailty models through
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
Joint modelling of time-to-event and longitudinal
package allows the analysis
of repeated measurements and time-to-event data via joint random
effects models. The
package performs Cox
regression and dynamic prediction under the joint frailty-copula
model between tumour progression and death for
regression model for longitudinal responses and a semiparametric
transformation model for time-to-event data.
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
can estimate bivariate
survival data subject to interval censoring.
package implements various statistical models
for multivariate event history data, e.g., multivariate cumulative
incidence models, bivariate random effects probit models,
package constructs trees for multivariate
survival data using marginal and frailty models.
package permits to estimate correlation
coefficients with associated confidence limits for bivariate,
partially censored survival times.
package proposes an implementation of a bivariate
computes a Bayesian model averaging for
Cox proportional hazards models.
fits a Bayesian
semi-parametric AFT model.
in the same package
fits a Linear Dependent Dirichlet Process Mixture of survival models.
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
package estimates the hazard function from censored
data in a Bayesian framework.
the log posterior density for a Weibull proportional-odds regression model.
fits generalised linear mixed models using MCMC
to right-, left- and interval censored data.
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.
package performs joint modelling of
longitudinal and time-to-event data under a bayesian approach.
Bayesian parametric and semi-parametric estimation for
semi-competing risks data is available via the
package implements penalized
semi-parametric Bayesian Cox models with elastic net, fused lasso and
group lasso priors.
package fits a Bayesian parametric
proportional hazards model for which events have been geo-located.
package implements Bayesian clustering
using a Dirichlet process mixture model to censored responses.
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.
package implements non-parametric
survival analysis techniques using a prior near-ignorant Dirichlet
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
package fits a piecewise
exponential hazard to survival data using a Hierarchical Bayesian
implements CART-like trees that can be used with
package implements recursive partitioning for survival
can perform logic regression.
implements K-adaptive partitioning and recursive
partitioning algorithms for censored survival data.
package implements trees and bagged trees
for discrete-times survival data. The
provides recursive partition algorithms designed for fitting
survival tree with left-truncated and right censored
implements a bootstrap aggregated
version of the k-nearest neighbors survival probability prediction
bagging for survival data. The
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
Regularised and shrinkage methods:
package provides procedures for fitting the
entire lasso or elastic-net regularization path for Cox models.
package implements a L1 regularised Cox
proportional hazards model. An L1 and L2 penalised Cox models are
computes a nearest shrunken centroid for survival gene expression
data. A high dimensional Cox model using univariate shrinkage is
implements the lassoed principal components method.
package implements the LASSO and elastic net
estimator for the additive risk model. The
package implements the Lasso and elastic-net penalized Cox's
regression using the cockail algorithm. The
package permits to fit Cox models with a combination of lasso and
group lasso regularisation.
fits Cox models
with penalized ridge-type (ridge, dynamic and weighted dynamic)
partial likelihood. The
package implements 9
types of penalised Cox regression methods and provides methods for
model validation, calibration, comparison, and nomogram
visualisation. Another implementation of regularised Cox models
can be found in
Coxnet. A penalised version of the Fine
and Gray model can be found
cyclic coordinate descent for the Cox proportional hazards model.
Gradient boosting for the Cox model is implemented in the
package includes a generic gradient boosting algorithm
for the construction of prognostic and diagnostic models for right-censored data.
implements permutation-based testing procedure to test
the additional predictive value of high-dimensional data. It is based on
provides routines for fitting the Cox proportional hazards model
and the Fine and Gray model by likelihood based boosting.
the supervised principal components for survival data.
package can construct index models for survival
outcomes, that is, construct scores based on a training dataset.
package fits Cox proportional hazards
model using the compound covariate method.
provides partial least squares regression and various techniques
for fitting Cox models in high dimensionnal
package implements feature selection
algorithms based on subsampling and averaging linear models
obtained from the Lasso algorithm for predicting survival risk.
Predictions and Prediction Performance
package provides utilities to plot prediction error
curves for several survival models
implements prediction error techniques which can
be computed in a parallelised way. Useful for high-dimensional
package permits to estimate time-dependent
ROC curves and time-dependent AUC with censored data, possibly
with competing risks.
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).
can be used to compute time-dependent ROC curve
from censored survival data using nonparametric weight
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
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
package implements the integrated
discrimination improvement index and the category-less net
reclassification index for comparing competing risks prediction
package provides functions for
estimating the AUC, TPR(c), FPR(c), PPV(c), and NPV(c) for
package provides functions for the
estimation of the prediction accuracy in a unified survival AUC
package permits to compare C indices
with right-censored survival outcomes
package provide tools to estimate the
average positive predictive values and the AUC for risk scores or
package proposes power calculation for weighted
Log-Rank tests in cure rate models.
permits to calculate sample size based on
proportional hazards mixture cure models.
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
package permits to generate data wih one
binary time-dependent covariate and data stemming from a
progressive illness-death model.
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)
package proposes some functions for
simulating complex event history data.
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.
package provides functions for simulating
complex multistate models data with possibly nonlinear baseline
hazards and nonlinear covariate effects.
package implements tools for simulating and
plotting quantities of interest estimated from proportional
package permits to simulate simple and
complex survival data such as recurrent event data and competing
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.
package permits to simulate data with a
dichotomous time-dependent exposure.
package can be used to simulate
univariate and semi-competing risks data given covariates and
piecewise exponential baseline hazards.
This section tries to list some specialised plot functions that might be
useful in the context of event history analysis.
functions for plotting survival curves with the at risk table aligned to
the x axis.
extends this to the competing risks
to draw the states and transitions that characterize a multistate
package provides many plot functions for
representing multistate data, in particular Lexis diagrams.
package provide multistate-type graphics
for competing risks, in which the thickness of the transition
arrows from the initial event to each competing event describes
the particular amount of every incidence rate.
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.
package contains the
for drawing survival curves with
the 'number at risk' table. Other functions are also available for
visual examinations of cox model assumptions.
package multiple imputation
methods for dealing with informative censoring.
provides data transformations, estimation
utilities, predictive evaluation measures and simulation functions for
discrete time survival analysis.
is the companion package to "Dynamic Prediction
in Clinical Survival Analysis".
implements several types of bootstrap techniques for right-censored data.
package estimates the current
cumulative incidence and the current leukaemia free survival function.
package provides functions for performing meta-analyses
of gene expression data and to predict patients' survival and risk assessment.
provides tools for individual patient data meta-analysis, mixed-level meta-analysis with patient
level data and mulivariate survival estimates for aggregate studies.
package includes the data sets from Klein
and Moeschberger (1997). Some supplementary data sets and
functions can be found in the
that accompanies Aitkin et al. (2009),
that accompanies Davidson (2003)
that accompanies Maindonald, J.H. and Braun,
W.J. (2003, 2007) also contain survival data sets.
package permits to construct, validate and
calibrate nomograms stemming from complex right-censored survey
package compute the MLE of a density
(log-concave) possibly for interval censored data.
package fits parametric
Transform-both-sides models used in reliability analysis
package implements algorithms to detect outliers
based on quantile regression for censored data.
package implements an EM algorithm
to estimate the relative case fatality ratio between two groups.
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
package allows for the estimation of
multivariate average hazard ratios as defined by Kalbfleisch and
provides miscellaneous routines to help in
the analysis of right-censored survival data.
Accompanying data sets to the book
Applied Survival Analysis
can be found in package