Package {survobj}

Title:	Simulate Parametric and Semi-Parametric Survival Times with Object-Oriented Design
Version:	3.2.0
Description:	Simulate parametric and semi-parametric survival times through a consistent, reusable interface for each distribution, using an object-oriented design. Supported distributions include Exponential, Weibull, Gompertz, Log-Logistic, Log-Normal, and Piecewise Exponential. Random variates can be generated under Proportional Hazards, Accelerated Failure Time, and Extended Hazards models, as well as under renewal and non-homogeneous Poisson recurrent event processes, following the methods described by Bender (2003) <doi:10.5282/UBM/EPUB.1716> and Leemis (1987) in Operations Research, 35(6), 892-894.
License:	GPL (≥ 3)
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
Suggests:	knitr, rmarkdown, spelling, testthat (≥ 3.0.0)
Language:	en-US
Config/testthat/edition:	3
Imports:	dplyr, tidyr, ggplot2, survival
VignetteBuilder:	knitr
URL:	https://johnaponte.github.io/survobj/, https://github.com/johnaponte/survobj
BugReports:	https://github.com/johnaponte/survobj/issues
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-09-20 16:20:02 UTC; master
Author:	Aponte John [aut, cre, cph]
Maintainer:	Aponte John <john.j.aponte@gmail.com>
Repository:	CRAN
Date/Publication:	2026-09-20 18:30:10 UTC

survobj: Simulate Parametric and Semi-Parametric Survival Times with Object-Oriented Design

Description

Simulate parametric and semi-parametric survival times through a consistent, reusable interface for each distribution, using an object-oriented design. Supported distributions include Exponential, Weibull, Gompertz, Log-Logistic, Log-Normal, and Piecewise Exponential. Random variates can be generated under Proportional Hazards, Accelerated Failure Time, and Extended Hazards models, as well as under renewal and non-homogeneous Poisson recurrent event processes, following the methods described by Bender (2003) doi:10.5282/UBM/EPUB.1716 and Leemis (1987) in Operations Research, 35(6), 892-894.

Author(s)

Maintainer: Aponte John john.j.aponte@gmail.com (ORCID) [copyright holder]

Authors:

• Aponte John john.j.aponte@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://johnaponte.github.io/survobj/

• https://github.com/johnaponte/survobj

• Report bugs at https://github.com/johnaponte/survobj/issues

Censor of events

Description

if censor_time < time, event is changed to 0, otherwise not changed

Usage

censor_event(censor_time, time, event)

censor_time(censor_time, time)

Arguments

Details

if censor_time < time, time is changed to censor_time, otherwise no change

Be careful and do not overwrite the time variable with the censor time variable to not lose track of the events

Value

censored time or event

Functions

• censor_time(): Censor time

Examples

# Typical workflow in a simulation of survival time.
# Simulate time to event (sim_t_event)
# and simulates the time to lost to follow up (tim_t_ltof)
# the simulation time frame is 1, so everything after 1 is censored

require(dplyr)
data.frame(
  sim_t_event = c(0.5,0.6,1,10,20),
  sim_t_ltof = c(2,0.5,2,2,0.8)
 ) |>
 mutate(sevent = censor_event(1,sim_t_event,event=1)) |>
 mutate(stime = censor_time(1,sim_t_event)) |>
 mutate(event = censor_event(sim_t_ltof, stime, sevent)) |>
 mutate(timeto = censor_time(sim_t_ltof, stime))

Functions to help in time conversion

Description

This set of functions helps in the time conversion, taking into account generic times and not specific times. The conversions are based on the assumption that 1 year is 365.25 days and is 12 months. There is no adjustment for leap days or hours or difference of days between months

Usage

dtom(x)

mtod(x)

dtoy(x)

ytod(x)

mtoy(x)

ytom(x)

Arguments

Value

the converted time

Functions

• dtom(): convert days to months

• mtod(): convert months to days

• dtoy(): convert days to years

• ytod(): convert years to days

• mtoy(): convert months to year

• ytom(): convert years to months

Examples

dtom(365.25)
mtod(12)
dtoy(165.25)
ytod(1)
mtoy(12)
ytom(365.25)

Confirm is a single number

Description

Evaluates if the argument is a single number

Usage

is_single_number(x)

Arguments

Value

TRUE if it is a single number, FALSE otherwise

Examples

is_single_number(3)  #TRUE
is_single_number(c(3,3,3)) #FALSE
is_single_number(list(a=3)) #FALSE
is_single_number("3") #FALSE

Generate random recurrent episodes under a non-homogeneous Poisson process

Description

Generate random draws from the distribution of a recurrent set of survival times under a non-homogeneous Poisson process following Leemis (1987)

Usage

nhpphr(SURVIVAL, hr, prevtime)

nhppaft(SURVIVAL, aft, prevtime)

Arguments

Value

Vector of survival times

Functions

• nhpphr(): Recurrent episodes under a proportional hazard model

• nhppaft(): Recurrent episodes under an accelerated failure time model

Examples

s_obj <- s_exponential(fail = 0.4, t = 1)
hr <- c(1,1,0.5,0.5)
time1 <- rsurvhr(s_obj, hr)
time2 <- nhpphr(s_obj,hr, time1)

s_obj <- s_exponential(fail = 0.4, t = 1)
aft <- c(1,1,0.5,0.5)
timea <- rsurvaft(s_obj, aft)
timeb <- nhppaft(s_obj, aft, timea)

Generate random recurrent episodes under a renewal Poisson process

Description

Generate random draws from the distribution of a recurrent set of survival times under a renewal Poisson process following Leemis (1987)

Usage

renewhr(SURVIVAL, hr, prevtime)

renewaft(SURVIVAL, aft, prevtime)

Arguments

Value

Vector of survival times

Vector of survival times

Functions

• renewhr(): Recurrent episodes under a proportional hazard model

• renewaft(): Recurrent episodes under an accelerated failure time model

Examples

s_obj <- s_exponential(fail = 0.4, t = 1)
hr <- c(1,1,0.5,0.5)
time1 <- rsurvhr(s_obj, hr)
time2 <- renewhr(s_obj, hr, time1)

s_obj2 <- s_exponential(fail = 0.4, t = 1)
aft <- c(1,1,0.5,0.5)
timea <- rsurvaft(s_obj2, aft)
timeb <- renewaft(s_obj2, aft, timea)

Factory of SURVIVAL objects with Exponential distributions

Description

Creates a SURVIVAL object with an Exponential distribution.

Usage

s_exponential(...)

Arguments

Value

a SURVIVAL object of the exponential distribution family. See the documentation of s_factory for the methods available for SURVIVAL objects

Parameters

To create an exponential survival object the following options are available:

lambda to specify the canonical parameter of the distribution, or

surv and t for the proportion surviving (no events) at time t, or

fail and t for the proportion failing (events) at time t

lambda = -log(surv)/t

lambda = -log(1-fail)/t

The parameters should be spelled correctly as partial matching is not available

Examples

s_exponential(lambda = 3)
s_exponential(surv = 0.4, t = 2)
s_exponential(fail = 0.6, t = 2)

Factory of objects of class SURVIVAL

Description

Create objects of the class SURVIVAL

Usage

s_factory(s_family, ...)

Arguments

Details

The objects of the class SURVIVAL define different distributions of survival times. Each class has its own set of parameters but once the SURVIVAL object is defined, they have access to the same functions to calculate:

• survival function: sfx(),

• hazard function: hfx(),

• cumulative hazard function: Cum_Hfx()

• the inverse of the cumulative hazard function: invCum_Hfx().

• generate random survival times: rsurv()

• generate random survival times under proportional hazard ratio: rsurvhr().

There are several functions to plot the distributions

• generic S3: plot.SURVIVAL()

• plot_survival(): to plot the functions

• ggplot_survival_random(): to ggplot random draws from the distribution

• compare_survival(): to compare the functions of two SURVIVAL objects

Value

a SURVIVAL object

Distributions

The following factories are implemented:

• s_exponential(): for Exponential distributions

• s_weibull(): for Weibull distributions

• s_gompertz(): for Gompertz distributions

• s_piecewise(): for Piecewise exponential distributions

• s_loglogistic(): for Log-Logistic distributions

• s_lognormal(): for Log-Normal distributions

Examples

# Define a SURVIVAL object
obj <- s_factory(s_exponential, lambda = 2)

# Survival, Hazard and Cumulative hazard at time 0.4
sfx(SURVIVAL = obj, t= 0.4)
hfx(SURVIVAL = obj, t = 0.4)
Cum_Hfx(SURVIVAL = obj, t = 0.4)

# Time when the Cumulative hazard is 0.8
invCum_Hfx(SURVIVAL = obj, H = 0.8)

# Draw one random survival time from the distribution
rsurv(SURVIVAL = obj, n = 1)

# Draw one random survival time from the distribution, with hazard ratio 0.5
rsurvhr(SURVIVAL = obj, hr = 0.5)

# Plot the survival functions
plot(obj)

Factory of SURVIVAL objects with Gompertz distributions

Description

Creates a SURVIVAL object with a Gompertz distribution.

Usage

s_gompertz(...)

Arguments

Value

a SURVIVAL object of the Gompertz distribution family. See the documentation of s_factory for the methods available for SURVIVAL objects

Parameters

To create a Gompertz survival object the following options are available:

scale and shape to specify the canonical parameters of the distribution, or

surv, t and shape for the proportion surviving (no events) at time t and shape, or

fail and t and shape for the proportion failing (events) at time t and shape.

scale = -log(surv)·shape/(exp(shape·t))

scale = -log(1-fail)·shape/(exp(shape·t))

The parameters should be spelled correctly as partial matching is not available

Negative shape

A negative shape produces a decreasing hazard whose cumulative hazard is bounded above by scale / abs(shape) (following Bender, Augustin & Blettner 2005). This makes the distribution improper: a cure fraction of exp(-scale / abs(shape)) of the subjects never experience the event, so their simulated survival time (rsurv, rsurvhr, rsurvaft, rsurveh) and invCum_Hfx() are returned as Inf.

Examples

s_gompertz(scale = 1, shape = 1.5)
s_gompertz(surv = 0.4, t = 2, shape = 1.5)
s_gompertz(fail = 0.6, t = 2, shape = 1.5)

Factory of SURVIVAL objects with Log Logistic distributions

Description

Creates a SURVIVAL object with a Log Logistic distribution.

Usage

s_loglogistic(...)

Arguments

Value

a SURVIVAL object of the log-logistic distribution family. See the documentation of s_factory for the methods available for SURVIVAL objects

Parameters

To create a Log Logistic survival object the following options are available:

scale and shape to specify the canonical parameters of the distribution, or

surv, t and shape for the proportion surviving (no events) at time t and the shape parameter, or

fail, t and shape for the proportion failing (events) at time t and the shape parameter, or

intercept and scale for the parameters returned by survreg(.., dist = "loglogistic") models.

The parameters should be spelled correctly as partial matching is not available

Examples

s_loglogistic(scale = 2,shape = 2)
s_loglogistic(surv = 0.6, t= 12, shape = 0.5)
s_loglogistic(fail = 0.4, t = 12, shape =0.5)
s_loglogistic(intercept = 0.4, scale = 0.5)

Factory of SURVIVAL objects with Log Normal distributions

Description

Creates a SURVIVAL object with a Log Normal distribution.

Usage

s_lognormal(...)

Arguments

Value

a SURVIVAL object of the log-normal distribution family. See the documentation of s_factory for the methods available for SURVIVAL objects

Parameters

To create a Log Normal survival object the following options are available:

scale and shape to specify the canonical parameters of the distribution, or

surv, t and shape for the proportion surviving (no events) at time t and the shape parameter, or

fail, t and shape for the proportion failing (events) at time t and the shape parameter, or

intercept and scale for the parameters returned by survreg(.., dist = "lognormal") models.

The scale parameter is the median value of the distribution, and the shape is the log standard deviation

The parameters should be spelled correctly as partial matching is not available

Examples

s_lognormal(scale = 2,shape = 2)
s_lognormal(surv = 0.6, t= 12, shape = 0.5)
s_lognormal(fail = 0.4, t = 12, shape =0.5)
s_lognormal(intercept = 0.4, scale = 0.5)

Factory of SURVIVAL objects with Piecewise Exponential distributions

Description

Creates a SURVIVAL object with a Piecewise Exponential distribution.

Usage

s_piecewise(...)

Arguments

Value

a SURVIVAL object of the piecewise exponential distribution family. See the documentation of s_factory for the methods available for SURVIVAL objects

Parameters

To create a piecewise exponential survival object the following options are available:

breaks and hazards to specify the exponential (constant) hazard until each break, or

surv, breaks and segments for the proportion surviving (no events) at the end of the last segment, or

fail, breaks and segments for the proportion failing (events) at the end of the last segment

If surv or fail parameters are indicated, the segments are scaled to hazards in order to match the surviving or failing proportion at the end of the last segment.

Define the last break point as Inf to fully define the distribution, otherwise an error will be produced if the function after the last break is requested

The parameters should be spelled correctly as partial matching is not available

Examples

s_piecewise(breaks = c(1,2,3,Inf), hazards = c(0.5,0.6,0.5,0.1))
s_piecewise(surv = 0.4, breaks = c(1,2,3,Inf), segments = c(1,2,1,2))
s_piecewise(fail = 0.6, breaks = c(1,2,3,Inf), segments = c(1,2,1,2))

Factory of SURVIVAL objects with Weibull distributions

Description

Creates a SURVIVAL object with a Weibull distribution.

Usage

s_weibull(...)

Arguments

Value

a SURVIVAL object of the Weibull distribution family. See the documentation of s_factory for the methods available for SURVIVAL objects

Parameters

To create a Weibull survival object the following options are available:

scale and shape to specify the canonical parameters of the distribution, or

surv, t and shape for the proportion surviving (no events) at time t and the shape parameter, or

fail, t and shape for the proportion failing (events) at time t and the shape parameter, or

intercept and scale for the parameters returned by survreg(.., dist = "weibull") models.

scale = -log(surv)/(t^shape)

scale = -log(1-fail)/(t^shape)

The parameters should be spelled correctly as partial matching is not available

Examples

s_weibull(scale = 2,shape = 2)
s_weibull(surv = 0.6, t= 12, shape = 0.5)
s_weibull(fail = 0.4, t = 12, shape =0.5)
s_weibull(intercept = 0.4, scale = 0.5)

Functions for SURVIVAL objects

Description

All the SURVIVAL objects have access to the functions described here

Usage

sfx(SURVIVAL, t)

hfx(SURVIVAL, t)

Cum_Hfx(SURVIVAL, t)

invCum_Hfx(SURVIVAL, H)

rsurv(SURVIVAL, n)

rsurvhr(SURVIVAL, hr)

rsurvaft(SURVIVAL, aft)

rsurveh(SURVIVAL, aft, hr)

plot_survival(SURVIVAL, timeto, main)

ggplot_survival_random(SURVIVAL, timeto, subjects, nsim, alpha = 0.1)

compare_survival(SURVIVAL1, SURVIVAL2, timeto, main)

ggplot_survival_hr(SURVIVAL, hr, timeto, subjects, nsim, alpha = 0.1)

ggplot_survival_aft(SURVIVAL, aft, timeto, subjects, nsim, alpha = 0.1)

ggplot_survival_eh(SURVIVAL, aft, hr, timeto, subjects, nsim, alpha = 0.1)

Arguments

Details

rsurveh() generates random values under the Extended Hazards model of Chen & Jewell (2001), which combines a proportional-hazards effect and an accelerated-failure-time effect: h*(t) = hr * aft * h0(aft * t). It nests the proportional hazards model (aft = 1) and the accelerated failure time model (hr = 1) as special cases. Note this differs from the Accelerated Hazards model of Chen & Wang (2000), h*(t) = h0(theta * t), which is the special case hr = 1 / aft.

Value

Depending on the function a proportion surviving, hazard, cumulative hazard, inverse of the cumulative hazard, a random draw or a plot

Functions

• sfx(): Survival function

• hfx(): Hazard function

• Cum_Hfx(): Cumulative Hazard function

• invCum_Hfx(): Inverse of the Cumulative Hazard function

• rsurv(): Generate random values from the distribution

• rsurvhr(): Generate random values from the distribution under proportional hazard ratios

• rsurvaft(): Generate random values from the distribution under accelerated failure time ratios

• rsurveh(): Generate random values from the distribution under the Extended Hazards model (combined proportional hazards and accelerated failure time)

• plot_survival(): Plot of the survival functions

• ggplot_survival_random(): ggplot of the simulation of survival times

• compare_survival(): Compare graphically two survival distributions

• ggplot_survival_hr(): ggplot of the simulation of survival times with hazard ratios

• ggplot_survival_aft(): ggplot of the simulation of survival times with accelerated failure times

• ggplot_survival_eh(): ggplot of the simulation of survival times with the Extended Hazards model

Examples

#' # Define a SURVIVAL object
obj <- s_factory(s_weibull, surv = 0.8, t = 2, shape = 1.2)

# Survival, Hazard and Cumulative hazard at time 0.4
sfx(SURVIVAL = obj, t= 0.4)
hfx(SURVIVAL = obj, t = 0.4)
Cum_Hfx(SURVIVAL = obj, t = 0.4)

# Time when the Cumulative hazard is 0.8
invCum_Hfx(SURVIVAL = obj, H = 0.8)

# Draw one random survival time from the distribution
rsurv(SURVIVAL = obj, n = 1)

# Draw one random survival time from the distribution under Proportional
# hazard, Accelerated failure time or the combined Extended Hazards model.
rsurvhr(SURVIVAL = obj, hr = 0.5)
rsurvaft(SURVIVAL = obj, aft = 2)
rsurveh(SURVIVAL = obj, aft = 2, hr = 0.5)

# Plot the survival functions
plot_survival(SURVIVAL = obj, timeto = 2, main = "Example of Weibull distribution" )