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Type:	Package
Title:	Simplify Survival Data Analysis and Model Fitting
Version:	2.0.2
Description:	Inspect survival data, plot Kaplan-Meier curves, assess the proportional hazards assumption, fit parametric survival models, predict and plot survival and hazards, and export the outputs to 'Excel'. A simple interface for fitting survival models using flexsurv::flexsurvreg(), flexsurv::flexsurvspline(), flexsurvcure::flexsurvcure(), and survival::survreg().
License:	MIT + file LICENSE
URL:	https://github.com/Maple-Health-Group/easysurv,https://maple-health-group.github.io/easysurv/
BugReports:	https://github.com/Maple-Health-Group/easysurv/issues
Depends:	R (≥ 4.1.0)
Imports:	bshazard, censored, cli, data.table, dplyr, flexsurv,flexsurvcure, ggplot2, ggsurvfit (≥ 1.2.0), openxlsx, parsnip,plotly, purrr, rlang, scales, stats, survival, tibble, tidyr,usethis, utils
Suggests:	knitr, rmarkdown, spelling, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/Needs/website:	r-lib/asciicast
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-US
LazyData:	true
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-10-08 10:34:50 UTC; NiallDavison
Author:	Niall Davison [aut, cre], Brad Kievit [aut], Maple Health Group, LLC [cph, fnd]
Maintainer:	Niall Davison <niall.davison@maplehealthgroup.com>
Repository:	CRAN
Date/Publication:	2025-10-08 11:30:02 UTC

easysurv: Simplify Survival Data Analysis and Model Fitting

Description

Inspect survival data, plot Kaplan-Meier curves, assess the proportional hazards assumption, fit parametric survival models, predict and plot survival and hazards, and export the outputs to 'Excel'. A simple interface for fitting survival models using flexsurv::flexsurvreg(), flexsurv::flexsurvspline(), flexsurvcure::flexsurvcure(), and survival::survreg().

Author(s)

Maintainer: Niall Davisonniall.davison@maplehealthgroup.com (ORCID)

Authors:

• Brad Kievitbrad.kievit@maplehealthgroup.com

Other contributors:

• Maple Health Group, LLC [copyright holder, funder]

See Also

Useful links:

• https://github.com/Maple-Health-Group/easysurv

• https://maple-health-group.github.io/easysurv/

• Report bugs athttps://github.com/Maple-Health-Group/easysurv/issues

Formatted Copy ofggsurvfit::adtte

Description

BackgroundThe example simulated data set is based on large phase III clinical trials inbreast cancer such as the ALTTO trialdoi:10.1200/JCO.2015.62.1797.The example trial aims to determine if a combination of two therapiestablemab (T) plus vismab (V) improves outcomes for metastatic human epidermalgrowth factor 2-positive breast cancer and increases the pathologic completeresponse in the neoadjuvant setting (i.e. treatment given as a first step toshrink a tumor before the main treatment or surgery).

The trial has four treatment arms, patients with centrally confirmed humanepidermal growth factor 2-positive early breast cancer were randomly assignedto 1 year of adjuvant therapy with V, T, their sequence (T to V), or theircombination (T+V) for 52 weeks.

The primary end point was progression-free survival (PFS) as defined byCancer.gov: '"the length of time during and after the treatment of a disease,such as cancer, that a patient lives with the disease but it does not getworse. In a clinical trial, measuring the progression-free survival is oneway to see how well a new treatment works"'.

A number of baseline measurements (taken at randomization) are also includedsuch as age, hormone receptor status and prior radiotherapy treatment.

Additional details on reasons for study discontinuation and censoringevent description are also included.

The data set adopts an abridged version of the CDISC ADaM ADTTEtime to event data model. See here for more info on CDISC ADaM data standardshttps://www.cdisc.org/standards/foundational/adam and specifically theADTTE time to event data model herehttps://www.cdisc.org/standards/foundational/adam/adam-basic-data-structure-bds-time-event-tte-analyses-v1-0.

Usage

easy_adtte

Format

The data set contains the following variables:

STUDYID

The study identifier. A code unique to the clinical trial

SUBJID

subject identifier. Numeric ID unique to each patient

USUBJID

unique subject identifier. Text ID combining study and patient IDs

AGE

age at randomisation (years)

STR01

Hormone receptor status at randomisation

STR01N

Hormone receptor positive (Numeric)

STR01L

Hormone receptor positive (Long format)

STR02

Prior Radiotherapy at randomisation

STR02N

Prior Radiotherapy at randomisation (Numeric)

STR02L

Prior Radiotherapy at randomisation (Long format)

TRT01P

Planned treatment assigned at randomisation

TRT01PN

Planned treatment assigned at randomisation (Numeric)

PARAM

Analysis parameter: Progression free survival

PARAMCD

Analysis parameter code

AVAL

Analysis value (time to event (years)

CNSR

Censoring (0 = Event, 1 = Censored)

EVNTDESC

Event description

CNSDTDSC

Censoring description

DCTREAS

Discontinuation from study reason

Source

ggsurvfit::adtte

Formatted Copy offlexsurv::bc

Description

This is a copy of the bc data set exported by the flexsurvpackage. This data set, however, has column labels assigned.

Usage

easy_bc

Format

The data set contains the following variables:

censrec

0 = Censored, 1 = Dead

rectime

Time of censoring or death in days

group

Prognostic group: Good, Medium, or Poor

recyrs

Time of censoring or death in years

Source

flexsurv::bc

Formatted Copy ofsurvival::lung

Description

This is a copy of the lung data set exported by the survivalpackage. This data set, however, has column labels assigned and time inmonths.

Usage

easy_lung

Format

The data set contains the following variables:

inst

Institution code

time

Survival time, months

status

Censoring status, 1 = censored, 2 = dead

age

Age

sex

Sex, 1 = Male, 2 = Female

ph.ecog

ECOG Performance Status (Physician)

ph.karno

Karnofsky performance score (Physician)

pat.karno

Karnofsky performance score (Patient)

meal.cal

Calories consumed

wt.loss

Weight loss, lbs

Source

survival::lung

Fit Survival Models

Description

Fits survival models to the provided data using the specified engine andreturns various outputs including model parameters, goodness of fit, andestimates of median survival.

Usage

fit_models(  data,  time,  event,  predict_by = NULL,  covariates = NULL,  dists = c("exp", "gamma", "gengamma", "gompertz", "llogis", "lnorm", "weibull"),  engine = "flexsurv",  k = c(1, 2, 3),  scale = "hazard",  add_time_0 = TRUE,  ...)

Arguments

Value

A list containing information about the fit_models() call, thedistributions attempted, goodness of fit, fit averages, and cure fractions(if applicable).

Examples

models <- fit_models(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  predict_by = "group",  covariates = "group")models

Generate Kaplan-Meier estimates

Description

Calculates Kaplan-Meier estimates for survival data and returns summarystatistics, plots, and additional outputs.

Usage

get_km(  data,  time,  event,  group = NULL,  group_labels = NULL,  just_km = FALSE,  ...)

Arguments

Value

A list containing Kaplan-Meier estimates, summary statistics, andplots.

Examples

km_results <- get_km(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  group = "group",  risktable_symbols = FALSE)km_results

Extract Schoenfeld Residuals

Description

This function extracts Schoenfeld residuals from a fittedcox.zph objectand formats them into a tidy data frame.

Usage

get_schoenfeld(fit_zph)

Arguments

Value

A tibble with the Schoenfeld residuals in long format, containingthe columns:

Examples

library(survival)test_fit <- survival::coxph(survival::Surv(time, status) ~ sex, data = lung)test_fit_zph <- survival::cox.zph(test_fit)get_schoenfeld(test_fit_zph)

Inspect Survival Data

Description

Quickly inspect the survival data to ensure it is in the correct format.

Usage

inspect_surv_data(data, time, event, group = NULL)

Arguments

Value

A list containing tibbles that summarise the first few rows of thesurvival data, the sample sizes, the events, and median survival.

Examples

inspect_surv_data(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  group = "group")

Plot method forfit_models

Description

Plot method forfit_models

Usage

## S3 method for class 'fit_models'plot(  x,  eval_time = NULL,  km_include = TRUE,  subtitle_include = TRUE,  add_plotly = FALSE,  ...)

Arguments

Value

A list containing predictions and plots for the survival and hazardsof models in afit_models object.

Examples

models <- fit_models(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  predict_by = "group",  covariates = "group")plot(models)

Cumulative Log Log Plot

Description

Generates a Cumulative Log Log survival curve plot usingggsurvfit::ggsurvfit() with customizable options.

Usage

plot_cloglog(  fit,  median_line = FALSE,  legend_position = "top",  plot_theme = theme_easysurv())

Arguments

Value

A ggplot object representing the cumulative log log plot.

Examples

library(ggsurvfit)fit <- survfit2(Surv(time, status) ~ surg, data = df_colon)plot_cloglog(fit)

Plot Kaplan-Meier Data

Description

Generates a Kaplan-Meier survival curve plot usingggsurvfit::ggsurvfit() with customizable options.This function provides sensible defaults while allowing for customization.

Usage

plot_km(  fit,  risktable = TRUE,  risktable_symbols = TRUE,  median_line = TRUE,  legend_position = "top",  plot_theme = theme_easysurv(),  risktable_theme = theme_risktable_easysurv())

Arguments

Value

A ggplot object representing the Kaplan-Meier survival curve plot.

Examples

library(ggsurvfit)fit <- survfit2(Surv(time, status) ~ surg, data = df_colon)plot_km(fit, risktable_symbols = FALSE)

Plot Schoenfeld Residuals

Description

Plot the residuals generated by theget_schoenfeld function.This function creates a visual representation of Schoenfeld residuals from aCox proportional hazards model.It allows for customization of the plot, including the addition of horizontaland smoothed lines, and styling of points and plot elements.

Usage

plot_schoenfeld(  residuals,  hline = TRUE,  sline = TRUE,  sline_se = TRUE,  hline_col = "#F8766D",  hline_size = 1,  hline_alpha = 1,  hline_yintercept = 0,  hline_lty = "dashed",  sline_col = "#00BFC4",  sline_size = 1,  sline_alpha = 0.2,  sline_lty = "dashed",  point_col = "black",  point_size = 1,  point_shape = 19,  point_alpha = 1,  plot_theme = ggplot2::theme_bw())

Arguments

Value

A ggplot object representing the plot of Schoenfeld residuals.

Examples

library(survival)test_fit <- survival::coxph(survival::Surv(time, status) ~ sex, data = lung)test_fit_zph <- survival::cox.zph(test_fit)plot_schoenfeld(get_schoenfeld(test_fit_zph))

Predict method forfit_models

Description

Predict method forfit_models

Usage

## S3 method for class 'fit_models'predict(object, eval_time = NULL, type = c("survival", "hazard"), ...)

Arguments

Value

A list of predictions for each model in thefit_models object.

Examples

models <- fit_models(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  predict_by = "group",  covariates = "group")predict(models)

Predict and Plot Fitted Models

Description

This function generates survival and hazard predictions and plots for eachmodel in afit_models object. Optionally, interactiveplotlyoutputs can be added for each plot.

Usage

predict_and_plot(  fit_models,  eval_time = NULL,  km_include = TRUE,  subtitle_include = TRUE,  add_plotly = FALSE)

Arguments

Value

A list of predictions and plots for each model in thefit_models object.

Examples

models <- fit_models(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  predict_by = "group")predict_and_plot(models)

Print methods forfit_models()

Description

Print methods forfit_models()

Usage

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

Arguments

Value

A print summary of thefit_models object.

Examples

models <- fit_models(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  predict_by = "group",  covariates = "group")models

Print methods forget_km()

Description

Print methods forget_km()

Usage

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

Arguments

Value

The summary of the Kaplan-Meier estimates, printed via the console.

Examples

km_results <- get_km(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  group = "group",  risktable_symbols = FALSE)print(km_results)

Print methods forinspect_surv_data()

Description

Print methods forinspect_surv_data()

Usage

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

Arguments

Value

A print summary of theinspect_surv_data object.

Print methods forpredict_and_plot()

Description

Print methods forpredict_and_plot()

Usage

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

Arguments

Value

A print summary of thepredict_and_plot object.

Examples

models <- fit_models(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  predict_by = "group")predict_and_plot(models)

Print methods fortest_ph()

Description

Print methods fortest_ph()

Usage

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

Arguments

Value

A print summary of thetest_ph object.

Examples

ph_results <- test_ph(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  group = "group")ph_results

Launch Example Survival Analysis Script using the easy_lung Data Set

Description

This function launches an example script for starting survival analysisusing the easysurv package. The script uses a modified version of thelung data set exported from the survival package. The code is inspired byusethis::use_template() but modified to work outside the context ofan .RProj or package.

Usage

quick_start(output_file_name = NULL)

Arguments

Value

A new R script file with example code.

Examples

quick_start()

Launch Example Survival Analysis Script using the easy_bc Data Set

Description

This function launches an example script for starting survival analysisusing the easysurv package. The script uses a modified version of the bc dataset exported from the flexsurv package. The code is inspired byusethis::use_template() but modified to work outside the context of an.RProj or package.

Usage

quick_start2(output_file_name = NULL)

Arguments

Value

A new R script file with example code.

Examples

quick_start2()

Launch Example Survival Analysis Script using the easy_adtte Data Set

Description

This function launches an example script for starting survival analysisusing the easysurv package. The script uses simulated phase III breastcancer trial data available from the ggsurvfit package.The code is inspired byusethis::use_template()but modified to work outside the context of an .RProj or package.

Usage

quick_start3(output_file_name = NULL)

Arguments

Value

A new R script file with example code.

Examples

quick_start3()

Test Proportional Hazards Assumption

Description

Assesses the proportional hazards assumption for survival data using aCox proportional hazards model and related tests.

Usage

test_ph(data, time, event, group, plot_theme = theme_easysurv())

Arguments

Value

A list containing plots and test results related to the assessmentof the proportional hazards assumption.

Examples

ph_results <- test_ph(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  group = "group")ph_results

Plot Theme for easysurv Survival and Hazard Plots

Description

Plot Theme for easysurv Survival and Hazard Plots

Usage

theme_easysurv()

Value

A ggplot2 theme object.

Examples

library(ggsurvfit)fit <- survfit2(Surv(time, status) ~ surg, data = df_colon)fit |> ggsurvfit() + theme_easysurv()

Plot Theme for easysurv Risk Tables

Description

To be used withggsurvfit::add_risktable().

Usage

theme_risktable_easysurv()

Value

A list containing a ggplot2 theme object.

Examples

library(ggsurvfit)fit <- survfit2(Surv(time, status) ~ surg, data = df_colon)fit <- fit |> ggsurvfit() +  theme_easysurv() +  add_risktable(theme = theme_risktable_easysurv())fit

Export easysurv output to Excel viaopenxlsx

Description

Export easysurv output to Excel viaopenxlsx

Usage

write_to_xl(wb, object)

Arguments

Value

An Excel workbook with the easysurv output.

Examples

km_results <- get_km(  data = easysurv::easy_bc,  time = "recyrs",  event = "censrec",  group = "group",  risktable_symbols = FALSE)wb <- openxlsx::createWorkbook()## Not run: write_to_xl(wb, km_results)openxlsx::saveWorkbook(wb, "km_results.xlsx", overwrite = TRUE)openxlsx::openXL("km_results.xlsx")## End(Not run)