Package {survrec}

Type:	Package
Version:	2.0.0
Date:	2026-09-12
Title:	Survival Analysis for Recurrent Event Data
Description:	Estimation of the survival function of inter-occurrence times for recurrent event data, using the estimators of Peña, Strawderman and Hollander (2001) <doi:10.1198/016214501753381922> and Wang and Chang (1999) <doi:10.1080/01621459.1999.10473831>, and maximum likelihood estimation under a gamma frailty model. Includes bootstrap comparison of survival quantiles between groups.
Depends:	R (≥ 3.5.0)
Imports:	Rcpp, boot, ggplot2, graphics, grDevices, rlang, stats, utils
LinkingTo:	Rcpp
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, BiocStyle
VignetteBuilder:	knitr
Config/testthat/edition:	3
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/isglobal-brge/survrec
BugReports:	https://github.com/isglobal-brge/survrec/issues
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
NeedsCompilation:	yes
Packaged:	2026-09-12 16:08:23 UTC; mailos
Author:	Dolors Pelegri-Siso [aut, cre], Juan R. González [aut], Edsel A. Peña [aut], Robert L. Strawderman [aut]
Maintainer:	Dolors Pelegri-Siso <dolors.pelegri@isglobal.org>
Repository:	CRAN
Date/Publication:	2026-09-22 07:00:02 UTC

survrec: Survival Analysis for Recurrent Event Data

Description

Estimation of the survival function of inter-occurrence times for recurrent event data. Three estimators are available: the generalized product-limit estimator of Peña, Strawderman and Hollander (2001), the estimator of Wang and Chang (1999) for correlated inter-occurrence times, and maximum likelihood estimation under a gamma frailty model. Survival quantiles can be compared between groups through several bootstrap schemes.

Details

The main entry points are Survr() to build the response object, survfitr() to estimate survival curves (optionally by group) and survdiffr() to bootstrap survival quantiles.

Author(s)

Maintainer: Dolors Pelegri-Siso dolors.pelegri@isglobal.org (ORCID)

Authors:

• Juan R. González juanr.gonzalez@isglobal.org

• Edsel A. Peña

• Robert L. Strawderman

References

Peña, E.A., Strawderman, R. and Hollander, M. (2001). Nonparametric Estimation with Recurrent Event Data. Journal of the American Statistical Association 96, 1299–1315.

Wang, M.-C. and Chang, S.-H. (1999). Nonparametric Estimation of a Recurrent Survival Function. Journal of the American Statistical Association 94, 146–153.

See Also

Useful links:

• https://github.com/isglobal-brge/survrec

• Report bugs at https://github.com/isglobal-brge/survrec/issues

Migratory Motor Complex

Description

Times of the Migratory Motor Complex (MMC) of 19 healthy individuals, measured through small bowel manometry.

Usage

MMC

Format

A data frame with 99 rows and 4 columns:

id

subject identifier, repeated for each recurrence

time

recurrence or censoring gap time

event

censoring status: 1 for every recurrence, 0 for the last (censored) time of each subject

group

a factor with levels Males and Females. Note: the groups were created at random to illustrate a group comparison

Source

Husebye, E., Skar, V., Aalen, O.O. and Osnes, M. (1990). Digital ambulatory manometry of the small intestine in healthy adults. Digestive Diseases and Sciences 35, 1057–1065.

Create a survival recurrent object

Description

Creates a survival recurrent object, usually used as the response variable in a model formula.

Usage

Survr(id, time, event)

is.Survr(x)

Arguments

Details

Each subject must contribute one censored time (its last, incomplete gap time), so id must have exactly as many distinct values as there are zeros in event. Rows are assumed to be grouped by subject and in chronological order within subject.

Value

An object of class Survr, implemented as a matrix with columns id, time and event.

is.Survr() returns TRUE if x inherits from class Survr and FALSE otherwise.

See Also

survfitr(), psh_fit(), wc_fit(), mlefrailty_fit()

Examples

data(MMC)
x <- Survr(MMC$id, MMC$time, MMC$event)
is.Survr(x)

Coerce a survfitr object to a data frame

Description

Returns the estimated curves in long ("tidy") format, one row per distinct event time (and group), ready for further processing or plotting.

Usage

## S3 method for class 'survfitr'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

A data frame with columns group (only when the fit has strata), time, n.event, n.risk, surv and std.error (NA for estimators without standard errors).

Examples

data(MMC)
fit <- survfitr(Survr(id, time, event) ~ group,
  data = MMC,
  type = "wang-chang"
)
head(as.data.frame(fit))

Plot a mean cumulative function

Description

Draws the mean cumulative function estimated by mcf() as a step function, one colour per group.

Usage

## S3 method for class 'mcf'
autoplot(object, ...)

Arguments

Value

A ggplot object.

See Also

mcf(), theme_survrec()

Examples

data(colon)
autoplot(mcf(Survr(hc, time, event) ~ as.factor(dukes), data = colon))

Plot the bootstrap distributions of survival quantiles

Description

Draws the density of the bootstrap replicates of each group, with a vertical line at the observed quantile. Infinite replicates (survival estimate never below the requested level) are excluded from the densities and reported in the caption.

Usage

## S3 method for class 'survdiffr'
autoplot(object, ...)

Arguments

Value

A ggplot object.

See Also

survdiffr(), summary.survdiffr(), theme_survrec()

Examples

data(colon)
fit <- survdiffr(Survr(hc, time, event) ~ as.factor(dukes),
  data = colon, q = 0.5, seed = 1
)
autoplot(fit)

Plot survival curves with ggplot2

Description

Draws the estimated survival function of a survfitr object as a step function, one colour per group, with an optional pointwise confidence band. The cumulative distribution (fun = "event") and the cumulative hazard (fun = "cumhaz") transformations are also available.

Usage

## S3 method for class 'survfitr'
autoplot(
  object,
  fun = c("surv", "event", "cumhaz"),
  conf.int = TRUE,
  level = 0.95,
  conf.type = c("log-log", "plain"),
  ...
)

Arguments

Details

The confidence band is computed from the standard errors of the fit when the estimator provides them (Peña-Strawderman-Hollander and Wang-Chang). With conf.type = "log-log" (default) the limits are obtained on the \log(-\log S) scale, so they always stay inside [0, 1]; "plain" gives the symmetric limits of the base plot.survfitr() method.

Value

A ggplot object, which can be further styled with the usual ggplot2 syntax.

See Also

plot.survfitr() for the base-graphics version, plotEstimators() to compare the three estimators, theme_survrec()

Examples

data(MMC)
fit <- survfitr(Survr(id, time, event) ~ group,
  data = MMC,
  type = "wang-chang"
)
autoplot(fit)
autoplot(fit, fun = "cumhaz", conf.int = FALSE)

# it is a ggplot, so it can be restyled
autoplot(fit) + ggplot2::labs(title = "Migratory Motor Complex")

Rehospitalization in colorectal cancer

Description

Rehospitalization times after surgery in patients diagnosed with colorectal cancer.

Usage

colon

Format

A data frame with 861 rows and 6 columns:

hc

subject identifier, repeated for each recurrence

time

rehospitalization or censoring gap time

event

censoring status: 1 for every rehospitalization, 0 for the last (censored) time of each subject

chemoter

did the patient receive chemotherapy? 1: no, 2: yes

dukes

Dukes' tumoral stage: 1: A-B, 2: C, 3: D

distance

distance from residence to hospital: 1: <= 30 km, 2: > 30 km

Source

González, J.R., Fernandez, E., Moreno, V. et al. (2005). Sex differences in hospital readmission among colorectal cancer patients. Journal of Epidemiology and Community Health 59, 506–511.

Mean cumulative function of recurrent event data

Description

Nonparametric (Nelson-Aalen type) estimate of the mean cumulative function, i.e. the expected number of events experienced by a subject up to each calendar time, optionally by group.

Usage

mcf(formula, data)

## S3 method for class 'mcf'
print(x, ...)

Arguments

Details

Calendar times are reconstructed as the within-subject cumulative sums of the gap times of the Survr() response. At each distinct event calendar time t the estimate increases by dN(t)/Y(t), where dN(t) is the number of events observed at t and Y(t) the number of subjects still under observation (total observation time \ge t).

Value

An object of class mcf: a data frame with columns group, time, n.event, n.risk and mcf, with print and autoplot methods.

References

Lawless, J.F. and Nadeau, C. (1995). Some Simple Robust Methods for the Analysis of Recurrent Events. Technometrics 37, 158–168.

See Also

autoplot.mcf(), survfitr()

Examples

data(MMC)
m <- mcf(Survr(id, time, event) ~ group, data = MMC)
m
autoplot(m)

Survival estimator under a gamma frailty model

Description

Maximum likelihood estimation of the survival function of correlated inter-occurrence times of recurrent event data under a gamma frailty model.

Usage

mlefrailty_fit(
  x,
  tvals,
  lambda = NULL,
  alpha = NULL,
  alpha.min,
  alpha.max,
  tol = 1e-07,
  maxiter = 500,
  alpha.console = TRUE
)

Arguments

Details

The product-limit estimator of Peña, Strawderman and Hollander (2001) is valid when the inter-occurrence times are an IID sample from some underlying distribution F. This assumption is clearly restrictive in biomedical applications, and one obvious generalization that allows association between inter-occurrence times is a frailty model.

A common and convenient choice of frailty distribution is a gamma with shape and scale parameters set equal to an unknown parameter \alpha. The common marginal survival function can be written as

\bar{F}(t) = \left[\frac{\alpha}{\alpha + \Lambda_0(t)} \right]^{\alpha}

The parameter \alpha controls the degree of association between inter-occurrence times within a unit. Peña, Strawderman and Hollander (2001) showed that \alpha and \Lambda_0 can be estimated by maximising the marginal likelihood function with an expectation-maximisation (EM) algorithm.

To achieve good convergence, a seed value for \alpha is estimated first by maximising the profile likelihood for \alpha over the interval (alpha.min, alpha.max) with Brent's method. If the EM algorithm does not converge from that seed, it is restarted from up to six additional seeds around it. If convergence still fails, the initial value of alpha can be used as the alpha.min argument and the fit recomputed.

Value

A list with class survfitr containing:

n

number of units or subjects observed.

m

number of events of each subject.

failed

uncensored gap times, ordered by subject.

censored

censored gap time of each subject.

time

ordered distinct event times.

n.event

number of events at each distinct time.

AtRisk

matrix of units at risk at each distinct time (rows are subjects).

status

1 if the EM algorithm converged, -1 if it reached maxiter without converging (in that case no estimates are provided).

alpha

estimate of the gamma frailty parameter.

lambda

estimated hazard probabilities at the distinct event times.

frailties

posterior frailty estimate of each subject; values spread away from 1 indicate heterogeneity between subjects.

survfunc

survival estimate at each distinct time.

tvals

copy of the tvals argument.

MLEAttvals

survival estimate at the tvals times.

References

Peña, E.A., Strawderman, R. and Hollander, M. (2001). Nonparametric Estimation with Recurrent Event Data. Journal of the American Statistical Association 96, 1299–1315.

See Also

survfitr(), Survr()

Examples

data(MMC)
fit <- mlefrailty_fit(Survr(MMC$id, MMC$time, MMC$event))
fit
plot(fit)

# compare with Peña-Strawderman-Hollander
fit <- psh_fit(Survr(MMC$id, MMC$time, MMC$event))
lines(fit, lty = 2)

# and with Wang-Chang
fit <- wc_fit(Survr(MMC$id, MMC$time, MMC$event))
lines(fit, lty = 3)

Plot survival curves of recurrent event data

Description

Plots the estimated survival (or probability) function from a survfitr object, one curve per group when the fit has strata. Additional fits can be added to the same axes with the lines method.

Usage

## S3 method for class 'survfitr'
plot(x, conf.int = TRUE, prob = FALSE, ...)

## S3 method for class 'survfitr'
lines(x, prob = FALSE, ...)

Arguments

Value

No return value; called for its side effect.

See Also

psh_fit(), wc_fit(), mlefrailty_fit()

Examples

data(MMC)
fit <- survfitr(Survr(id, time, event) ~ group,
  data = MMC,
  type = "wang-chang"
)
plot(fit)

Compare the three survival estimators on the same data

Description

Fits the Peña-Strawderman-Hollander, the Wang-Chang and the gamma frailty MLE estimators to the same sample and draws the three curves on the same axes. Agreement between the PSH and the frailty estimates suggests independent inter-occurrence times, while a Wang-Chang curve separated from the PSH one points to within-subject correlation.

Usage

plotEstimators(x, conf.int = FALSE, level = 0.95, alpha.console = FALSE, ...)

Arguments

Value

A ggplot object.

See Also

autoplot.survfitr(), psh_fit(), wc_fit(), mlefrailty_fit()

Examples

data(MMC)
plotEstimators(Survr(MMC$id, MMC$time, MMC$event))

Print a short summary of survival curves for recurrent event data

Description

Prints, for each curve, the number of subjects, the number of events, the restricted mean survival and its standard error, the median survival and the minimum, maximum and median number of recurrences per subject.

Usage

## S3 method for class 'survfitr'
print(x, scale = 1, digits = max(options()$digits - 4, 3), ...)

Arguments

Details

The restricted mean and its standard error are based on a truncated estimator: if the survival curve does not reach zero, the reported quantity is the mean survival restricted to the time before the last censoring. The median is the time at which the survival curve crosses 0.5.

Value

x, invisibly.

See Also

summary.survfitr(), survfitr()

Examples

data(MMC)
fit <- survfitr(Survr(id, time, event) ~ group, data = MMC)
print(fit)

Peña-Strawderman-Hollander survival estimator

Description

Estimates the survival function of the inter-occurrence times of recurrent event data with the generalized product-limit estimator (PLE) of Peña, Strawderman and Hollander (2001).

Usage

psh_fit(x, tvals)

Arguments

Details

The estimator computed by this function is the nonparametric estimator of the inter-event time survivor function under the assumption of a renewal or IID model. It generalizes the product-limit estimator to the situation where the event is recurrent. For details and the theory behind the estimator, please refer to Peña, Strawderman and Hollander (2001).

Value

A list with class survfitr containing:

n

number of units or subjects observed.

m

number of events of each subject.

failed

uncensored gap times, ordered by subject.

censored

censored gap time of each subject.

time

ordered distinct event times.

n.event

number of events at each distinct time.

AtRisk

matrix of units at risk at each distinct time (rows are subjects).

survfunc

survival estimate at each distinct time.

std.error

standard error of the survival estimate.

tvals

copy of the tvals argument.

PSHpleAttvals

survival estimate at the tvals times.

References

Peña, E.A., Strawderman, R. and Hollander, M. (2001). Nonparametric Estimation with Recurrent Event Data. Journal of the American Statistical Association 96, 1299–1315.

See Also

survfitr(), Survr()

Examples

data(MMC)
fit <- psh_fit(Survr(MMC$id, MMC$time, MMC$event))
fit
plot(fit, conf.int = FALSE)

# compare with the gamma frailty MLE
fit <- mlefrailty_fit(Survr(MMC$id, MMC$time, MMC$event))
lines(fit, lty = 2)

# and with Wang-Chang
fit <- wc_fit(Survr(MMC$id, MMC$time, MMC$event))
lines(fit, lty = 3)

Survival time of a selected quantile

Description

Given a survfitr object, returns the first time at which the estimated survival function falls to the quantile q or below.

Usage

q_search(f, q = 0.5)

Arguments

Value

The survival time of the selected quantile, or NA (with a warning) when the survival estimates are not available.

Examples

data(MMC)
fit <- survfitr(Survr(id, time, event) ~ 1, data = MMC)

# 75th percentile of the survival function
q_search(fit, q = 0.75)

Quantiles of the survival time

Description

Returns, for each requested probability p, the first time at which the estimated survival function falls to 1 - p or below (so probs = 0.5 is the median survival time). NA is returned when the curve does not reach the requested level.

Usage

## S3 method for class 'survfitr'
quantile(x, probs = c(0.25, 0.5, 0.75), ...)

Arguments

Value

A named vector of survival times, or a matrix with one row per group when the fit has strata.

See Also

q_search(), survfitr()

Examples

data(MMC)
fit <- survfitr(Survr(id, time, event) ~ group,
  data = MMC,
  type = "wang-chang"
)
quantile(fit, probs = c(0.25, 0.5, 0.75))

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

ggplot2

autoplot

Summarize a grouped bootstrap of survival quantiles

Description

Computes, for each group, the observed quantile and a percentile bootstrap confidence interval, and for each pair of groups the difference of quantiles with its percentile interval and a two-sided bootstrap p-value for the null hypothesis of no difference.

Usage

## S3 method for class 'survdiffr'
summary(object, level = 0.95, ...)

## S3 method for class 'summary.survdiffr'
print(x, digits = max(options()$digits - 4, 3), ...)

Arguments

Details

Replicates in which the survival estimate never fell below the requested level are treated as +Inf (the quantile exceeds the observed follow-up); pairs where both replicates are infinite are dropped from the difference. The p-value is 2 \min(P(D \le 0), P(D \ge 0)) over the replicated differences D.

Value

A list of class summary.survdiffr with components groups (data frame with the observed quantile and its interval per group) and contrasts (data frame with the pairwise differences, intervals and p-values).

See Also

survdiffr(), boot::boot.ci()

Examples

data(colon)
fit <- survdiffr(Survr(hc, time, event) ~ as.factor(dukes),
  data = colon, q = 0.5, seed = 1
)
summary(fit)

Summary of survival curves for recurrent event data

Description

Returns a matrix with the distinct event times and, at each of them, the number of events, the number of subjects at risk, the survival estimate and (when available) its standard error. If the fit has multiple curves, a list with one matrix per curve is returned.

Usage

## S3 method for class 'survfitr'
summary(object, ...)

## S3 method for class 'summary.survfitr'
print(x, scale = 1, digits = max(options()$digits - 4, 3), ...)

Arguments

Value

For a single curve, a matrix; for multiple curves, a list of class summary.survfitr with one matrix per curve.

See Also

survfitr()

Examples

data(MMC)
summary(survfitr(Survr(id, time, event) ~ group, data = MMC))

Survival estimate at selected times

Description

Evaluates a step survival function at arbitrary times. The estimate is a decreasing piecewise-constant function with jumps at the event times, so the value at any time is the one of the previous event time.

Usage

surv_search(tvals, time, surv)

Arguments

Value

The survival estimate at each of the tvals times.

Examples

time <- c(4, 7, 9, 15, 21, 67)
surv <- c(0.8, 0.7, 0.65, 0.55, 0.43, 0.22)

# survival at times 1, 10, 32 and 74
surv_search(c(1, 10, 32, 74), time, surv)

Bootstrap survival quantiles for recurrent event data

Description

Obtains bootstrap replicates of a quantile (e.g. the median) of the survival time for each group of subjects. Confidence intervals of the replicates can be computed with boot::boot.ci().

Usage

survdiffr(
  formula,
  data,
  q,
  B = 500,
  boot.F = "WC",
  boot.G = "none",
  seed = NULL,
  ...
)

Arguments

Details

Three resampling schemes are available through boot.F: nonparametric resampling of gap times from the Peña-Strawderman-Hollander ("PSH") or the Wang-Chang ("WC") estimates of the inter-occurrence time distribution, and semiparametric resampling under the fitted gamma frailty model ("semiparametric"). With boot.G = "empirical" the per-subject censoring times are additionally resampled from their empirical distribution.

All resampling uses R's random number generator, so results are reproducible with set.seed() or the seed argument. The re-estimation of the survival curve on each replicate can run in parallel, see survrecThreads(). Some procedures (notably the semiparametric one, which refits the frailty model on every replicate) can be slow.

Value

An object of class boot (or a named list with one per group) whose t0 is the observed quantile and t the bootstrap replicates. A replicate is -1 when the survival estimate does not fall below q, and NA when the resampled data had no events. For the semiparametric procedure the component alpha contains the frailty parameter estimated on each replicate.

References

González, J.R. and Peña, E.A. (2003). Bootstrapping median survival with recurrent event data. IX Conferencia Española de Biometría.

See Also

survfitr(), boot::boot.ci(), survrecThreads()

Examples

data(colon)

# compare the median survival time between the three Dukes stages
fit <- survdiffr(Survr(hc, time, event) ~ as.factor(dukes),
  data = colon, q = 0.5, seed = 1
)
boot::boot.ci(fit$"1", type = c("norm", "basic", "perc"))
boot::boot.ci(fit$"2", type = c("norm", "basic", "perc"))
boot::boot.ci(fit$"3", type = c("norm", "basic", "perc"))

# 75th quantile with percentile confidence intervals
fit <- survdiffr(Survr(hc, time, event) ~ as.factor(dukes),
  data = colon, q = 0.75, seed = 1
)
quantile(fit$"1"$t, c(0.025, 0.975))

# resampling from the PSH estimate instead
fit <- survdiffr(Survr(hc, time, event) ~ as.factor(dukes),
  data = colon, q = 0.5, boot.F = "PSH", seed = 1
)

Survival curves for recurrent event data

Description

Computes an estimate of the survival curve of inter-occurrence times for recurrent event data, optionally by group, using the Peña-Strawderman-Hollander, the Wang-Chang or the gamma frailty MLE estimators, together with their standard errors.

Usage

survfitr(formula, data, type = "MLEfrailty", ...)

Arguments

Details

See psh_fit(), wc_fit() and mlefrailty_fit() for details on each estimator; additional arguments in ... are passed to the chosen fitting function.

Value

A survfitr object: the fit of the chosen estimator, or a named list with one fit per group when the formula defines groups. Methods are provided for print, plot, lines and summary.

References

Peña, E.A., Strawderman, R. and Hollander, M. (2001). Nonparametric Estimation with Recurrent Event Data. Journal of the American Statistical Association 96, 1299–1315.

Wang, M.-C. and Chang, S.-H. (1999). Nonparametric Estimation of a Recurrent Survival Function. Journal of the American Statistical Association 94, 146–153.

See Also

print.survfitr(), plot.survfitr(), summary.survfitr(), Survr(), psh_fit(), wc_fit(), mlefrailty_fit()

Examples

data(colon)
# fit a Peña-Strawderman-Hollander estimator by Dukes stage
fit <- survfitr(Survr(hc, time, event) ~ as.factor(dukes),
  data = colon, type = "pena"
)
plot(fit, ylim = c(0, 1), xlim = c(0, 2000))
fit
summary(fit)

Deprecated functions in survrec

Description

The dot-separated names of survrec 1.x were renamed to snake_case in version 2.0.0. The old names still work but emit a deprecation warning and will be removed in a future release.

Usage

psh.fit(x, tvals)

wc.fit(x, tvals)

mlefrailty.fit(
  x,
  tvals,
  lambda = NULL,
  alpha = NULL,
  alpha.min,
  alpha.max,
  tol = 1e-07,
  maxiter = 500,
  alpha.console = TRUE
)

surv.search(tvals, time, surv)

q.search(f, q = 0.5)

Arguments

Details

psh.fit()

use psh_fit() instead.

wc.fit()

use wc_fit() instead.

mlefrailty.fit()

use mlefrailty_fit() instead.

surv.search()

use surv_search() instead.

q.search()

use q_search() instead.

Value

Each deprecated function emits a deprecation warning and then returns exactly what its replacement returns: psh.fit(), wc.fit() and mlefrailty.fit() return a survfitr object, surv.search() returns the interpolated survival probabilities as a numeric vector, and q.search() returns the estimated quantile as a single number. See the replacement functions for the full description of the value.

Number of threads used by survrec

Description

Gets or sets the maximum number of threads used by the parallel parts of the package, currently the re-estimation phase of the bootstrap in survdiffr(), which refits the survival curve once per bootstrap replicate.

Usage

survrecThreads(n)

Arguments

Details

By default the package lets OpenMP decide, which means it honours the OMP_NUM_THREADS environment variable. Set this option to cap the number of threads regardless of that variable, for instance when running inside a shared machine or a job scheduler.

The bootstrap resampling itself always runs serially because it draws from R's random number generator (which is not thread-safe); results are therefore reproducible with set.seed() independently of the number of threads.

If the package was built without OpenMP support (which is the case with the default compiler on macOS), the computation runs sequentially and this setting has no effect.

Value

The current setting, invisibly when it is being changed. NULL means "let OpenMP decide".

Examples

survrecThreads() # current setting

old <- survrecThreads(2)
survrecThreads(old) # restore

Plot theme used by the survrec graphics

Description

A clean ggplot2 theme shared by all the plotting functions of the package: light horizontal grid, no panel border, muted axis text and room for the legend at the bottom. Exported so that the figures of a report can be restyled consistently. It matches the theme of the companion package gcmrec.

Usage

theme_survrec(base_size = 12)

Arguments

Value

A ggplot2 theme object, to be added to any plot.

Examples

data(MMC)
fit <- survfitr(Survr(id, time, event) ~ group,
  data = MMC,
  type = "wang-chang"
)
autoplot(fit) + theme_survrec(base_size = 14)

Wang-Chang survival estimator

Description

Estimates the survival function of correlated or i.i.d. inter-occurrence times of recurrent event data with the product-limit estimator of Wang and Chang (1999).

Usage

wc_fit(x, tvals)

Arguments

Details

Wang and Chang (1999) proposed an estimator of the common marginal survivor function for the case where within-unit inter-occurrence times are correlated. The correlation structure they considered is quite general and contains both the i.i.d. and the multiplicative (hence gamma) frailty model as special cases.

This estimator removes the bias noted for the product-limit estimator of Peña, Strawderman and Hollander (2001) when inter-occurrence times are correlated within units. However, when applied to i.i.d. inter-occurrence times it is not expected to perform as well as the PSH estimator, especially with regard to efficiency.

Value

A list with class survfitr containing:

n

number of units or subjects observed.

m

number of events of each subject.

failed

uncensored gap times, ordered by subject.

censored

censored gap time of each subject.

time

ordered distinct event times.

n.event

weighted number of events at each distinct time.

AtRisk

weighted number of units at risk at each distinct time.

survfunc

survival estimate at each distinct time.

std.error

standard error of the survival estimate.

tvals

copy of the tvals argument.

WCpleAttvals

survival estimate at the tvals times.

Note

The maintainers wish to thank Professors Chiung-Yu Huang and Shu-Hui Chang for providing the original Fortran code computing the standard errors of Wang and Chang's estimator.

References

Wang, M.-C. and Chang, S.-H. (1999). Nonparametric Estimation of a Recurrent Survival Function. Journal of the American Statistical Association 94, 146–153.

See Also

survfitr(), Survr()

Examples

data(MMC)
fit <- wc_fit(Survr(MMC$id, MMC$time, MMC$event))
fit
plot(fit, conf.int = FALSE)

# compare with Peña-Strawderman-Hollander
fit <- psh_fit(Survr(MMC$id, MMC$time, MMC$event))
lines(fit, lty = 2)

# and with the gamma frailty MLE
fit <- mlefrailty_fit(Survr(MMC$id, MMC$time, MMC$event))
lines(fit, lty = 3)