Package 'pammtools'

Description

Add cumulative incidence function to data

Usage

add_cif(newdata, object, ...)

## Default S3 method:
add_cif(
  newdata,
  object,
  ci = TRUE,
  overwrite = FALSE,
  alpha = 0.05,
  n_sim = 500L,
  cause_var = "cause",
  time_var = NULL,
  ...
)

Arguments

Description

Add (cumulative) hazard based on the provided data set and model. If ci=TRUE confidence intervals (CI) are also added. Their width can be controlled via the se_mult argument. The method by which the CI are calculated can be specified by ci_type. This is a wrapper around predict.gam. When reference is specified, the (log-)hazard ratio is calculated.

Usage

add_hazard(newdata, object, ...)

## Default S3 method:
add_hazard(
  newdata,
  object,
  reference = NULL,
  type = c("response", "link"),
  ci = TRUE,
  se_mult = 2,
  ci_type = c("default", "delta", "sim"),
  overwrite = FALSE,
  time_var = NULL,
  ...
)

add_cumu_hazard(
  newdata,
  object,
  ci = TRUE,
  se_mult = 2,
  overwrite = FALSE,
  time_var = NULL,
  interval_length = "intlen",
  ...
)

Arguments

See Also

predict.gam, add_surv_prob

Examples

ped <- tumor[1:50,] %>% as_ped(Surv(days, status)~ age)
pam <- mgcv::gam(ped_status ~ s(tend)+age, data = ped, family=poisson(), offset=offset)
ped_info(ped) %>% add_hazard(pam, type="link")
ped_info(ped) %>% add_hazard(pam, type = "response")
ped_info(ped) %>% add_cumu_hazard(pam)

Description

Given suitable data (i.e. data with all columns used for estimation of the model), this functions adds a column surv_prob containing survival probabilities for the specified covariate and follow-up information (and CIs surv_lower, surv_upper if ci=TRUE).

Usage

add_surv_prob(
  newdata,
  object,
  ci = TRUE,
  se_mult = 2,
  overwrite = FALSE,
  time_var = NULL,
  interval_length = "intlen",
  ...
)

Arguments

See Also

predict.gam, add_surv_prob

Examples

ped <- tumor[1:50,] %>% as_ped(Surv(days, status)~ age)
pam <- mgcv::gam(ped_status ~ s(tend)+age, data=ped, family=poisson(), offset=offset)
ped_info(ped) %>% add_surv_prob(pam, ci=TRUE)

Description

Given a data set in standard format (with one row per subject/observation), this function adds a column with the specified exposure time points and a column with respective exposures, created from rng_fun. This function should usually only be used to create data sets passed to sim_pexp.

Usage

add_tdc(data, tz, rng_fun, ...)

Arguments

Description

Adds the contribution of a specific term to the linear predictor to the data specified by newdata. Essentially a wrapper to predict.gam, with type="terms". Thus most arguments and their documentation below is from predict.gam.

Usage

add_term(newdata, object, term, reference = NULL, ci = TRUE, se_mult = 2, ...)

Arguments

Examples

library(ggplot2)
ped <- as_ped(tumor, Surv(days, status)~ age, cut = seq(0, 2000, by = 100))
pam <- mgcv::gam(ped_status ~ s(tend) + s(age), family = poisson(),
  offset = offset, data = ped)
#term contribution for sequence of ages
s_age <- ped %>% make_newdata(age = seq_range(age, 50)) %>%
  add_term(pam, term = "age")
ggplot(s_age, aes(x = age, y = fit)) + geom_line() +
  geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper), alpha = .3)
# term contribution relative to mean age
s_age2 <- ped %>% make_newdata(age = seq_range(age, 50)) %>%
  add_term(pam, term = "age", reference = list(age = mean(.$age)))
ggplot(s_age2, aes(x = age, y = fit)) + geom_line() +
  geom_ribbon(aes(ymin = ci_lower, ymax = ci_upper), alpha = .3)

Description

Aas.data.frame S3 method for objects of class crps.

Usage

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

Arguments

Time-dependent covariates of the patient data set.

Description

This data set contains the time-dependent covariates (TDCs) for the patient data set. Note that nutrition was protocoled for at most 12 days after ICU admission. The data set includes:

CombinedID

Unique patient identifier. Can be used to merge with patient data

Study_Day

The calendar (!) day at which calories (or proteins) were administered

caloriesPercentage

The percentage of target calories supplied to the patient by the ICU staff

proteinGproKG

The amount of protein supplied to the patient by the ICU staff

Usage

daily

Format

An object of class tbl_df (inherits from tbl, data.frame) with 18797 rows and 4 columns.

Description

geom_hazard is an extension of the geom_line, and is optimized for (cumulative) hazard plots. Essentially, it adds a (0,0) row to the data, if not already the case. Stolen from the RmcdrPlugin.KMggplot2 (slightly modified).

Usage

geom_hazard(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_stephazard(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  direction = "vh",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_surv(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

See Also

geom_line, geom_step.

Examples

library(ggplot2)
library(pammtools)
ped <- tumor[10:50,] %>% as_ped(Surv(days, status)~1)
pam <- mgcv::gam(ped_status ~ s(tend), data=ped, family = poisson(), offset = offset)
ndf <- make_newdata(ped, tend = unique(tend)) %>% add_hazard(pam)
# piece-wise constant hazards
ggplot(ndf, aes(x = tend, y = hazard)) +
 geom_vline(xintercept = c(0, ndf$tend[c(1, (nrow(ndf)-2):nrow(ndf))]), lty = 3) +
 geom_hline(yintercept = c(ndf$hazard[1:3], ndf$hazard[nrow(ndf)]), lty = 3) +
 geom_stephazard() +
 geom_step(col=2) +
 geom_step(col=2, lty = 2, direction="vh")

# comulative hazard
ndf <- ndf %>% add_cumu_hazard(pam)
ggplot(ndf, aes(x = tend, y = cumu_hazard)) +
 geom_hazard() +
 geom_line(col=2) # doesn't start at (0, 0)

# survival probability
ndf <- ndf %>% add_surv_prob(pam)
ggplot(ndf, aes(x = tend, y = surv_prob)) +
 geom_surv() +
 geom_line(col=2) # doesn't start at c(0,1)

Description

geom_stepribbon is an extension of the geom_ribbon, and is optimized for Kaplan-Meier plots with pointwise confidence intervals or a confidence band. The default direction-argument "hv" is appropriate for right-continuous step functions like the hazard rates etc returned by pammtools.

Usage

geom_stepribbon(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  direction = "hv",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

See Also

geom_ribbon geom_stepribbon inherits from geom_ribbon.

Examples

library(ggplot2)
huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron))
h <- ggplot(huron, aes(year))
h + geom_stepribbon(aes(ymin = level - 1, ymax = level + 1), fill = "grey70") +
    geom_step(aes(y = level))
h + geom_ribbon(aes(ymin = level - 1, ymax = level + 1), fill = "grey70") +
    geom_line(aes(y = level))

Description

These functions are designed to extract (or mimic) the cumulative coefficients usually used in additive hazards models (Aalen model) to depict (time-varying) covariate effects. For PAMMs, these are the differences between the cumulative hazard rates where all covariates except one have the identical values. For a numeric covariate of interest, this calculates $\Lambda(t|x+1) - \Lambda(t|x)$. For non-numeric covariates the cumulative hazard of the reference level is subtracted from the cumulative hazards evaluated at all non reference levels. Standard errors are calculated using the delta method.

Usage

get_cumu_coef(model, data = NULL, terms, ...)

## S3 method for class 'gam'
get_cumu_coef(model, data, terms, ...)

## S3 method for class 'aalen'
get_cumu_coef(model, data = NULL, terms, ci = TRUE, ...)

## S3 method for class 'cox.aalen'
get_cumu_coef(model, data = NULL, terms, ci = TRUE, ...)

Arguments

Description

Calculate (or plot) cumulative effect for all time-points of the follow-up

Usage

get_cumu_eff(data, model, term, z1, z2 = NULL, se_mult = 2)

gg_cumu_eff(data, model, term, z1, z2 = NULL, se_mult = 2, ci = TRUE)

Arguments

Description

Information on intervals in which times fall

Usage

get_intervals(x, times, ...)

## Default S3 method:
get_intervals(x, times, left.open = TRUE, rightmost.closed = TRUE, ...)

Arguments

Value

A data.frame containing information on intervals in which values of times fall.

See Also

findInterval int_info

Examples

set.seed(111018)
brks <- c(0, 4.5, 5, 10, 30)
int_info(brks)
x <- runif (3, 0, 30)
x
get_intervals(brks, x)

Description

Constructs lag-lead window data set from raw inputs or from data objects with suitable information stored in attributes, e.g., objects created by as_ped.

Usage

get_laglead(x, ...)

## Default S3 method:
get_laglead(x, tz, ll_fun, ...)

## S3 method for class 'data.frame'
get_laglead(x, ...)

Arguments

Examples

get_laglead(0:10, tz=-5:5, ll_fun=function(t, tz) { t >= tz + 2 & t <= tz + 2 + 3})
gg_laglead(0:10, tz=-5:5, ll_fun=function(t, tz) { t >= tz + 2 & t <= tz + 2 + 3})

Description

Given a mgcv gamObject, returns the information used for the default plots produced by plot.gam.

Usage

get_plotinfo(x, ...)

Arguments

Description

This function basically creates a new df from data for each term in terms, creating a range from minimum and maximum of the predict(fit, newdata=df, type="terms"). Terms are then stacked to a tidy data frame.

Usage

get_terms(data, fit, terms, ...)

Arguments

Value

A tibble with 5 columns.

Examples

library(survival)
fit <- coxph(Surv(time, status) ~ pspline(karno) + pspline(age), data=veteran)
terms_df <- veteran %>% get_terms(fit, terms = c("karno", "age"))
head(terms_df)
tail(terms_df)

Description

Given a model object, returns a data frame with columns variable, coef (coefficient), ci_lower (lower 95\ ci_upper (upper 95\

Usage

gg_fixed(x, intercept = FALSE, ...)

Arguments

See Also

tidy_fixed

Examples

g <- mgcv::gam(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species,
 data=iris)
gg_fixed(g, intercept=TRUE)
gg_fixed(g)

Description

Given data defining a Lag-lead window, returns respective plot as a ggplot2 object.

Usage

gg_laglead(x, ...)

## Default S3 method:
gg_laglead(x, tz, ll_fun, ...)

## S3 method for class 'LL_df'
gg_laglead(
  x,
  high_col = "grey20",
  low_col = "whitesmoke",
  grid_col = "lightgrey",
  ...
)

## S3 method for class 'nested_fdf'
gg_laglead(x, ...)

Arguments

See Also

get_laglead

Examples

## Example 1: supply t, tz, ll_fun directly
 gg_laglead(1:10, tz=-5:5,
  ll_fun=function(t, tz) { t >= tz + 2 & t <= tz + 2 + 3})

## Example 2: extract information on t, tz, ll_from data with respective attributes
data("simdf_elra", package = "pammtools")
gg_laglead(simdf_elra)

Description

Depending on the plot function and input, creates either a 1-dimensional slices, bivariate surface or (1D) cumulative effect.

Usage

gg_partial(data, model, term, ..., reference = NULL, ci = TRUE)

gg_partial_ll(
  data,
  model,
  term,
  ...,
  reference = NULL,
  ci = FALSE,
  time_var = "tend"
)

get_partial_ll(
  data,
  model,
  term,
  ...,
  reference = NULL,
  ci = FALSE,
  time_var = "tend"
)

Arguments

Description

Plot Normal QQ plots for random effects

Usage

gg_re(x, ...)

Arguments

See Also

tidy_re

Examples

library(pammtools)
data("patient")
ped <- patient %>%
 dplyr::slice(1:100) %>%
 as_ped(Surv(Survdays, PatientDied)~ ApacheIIScore + CombinedicuID, id="CombinedID")
pam <- mgcv::gam(ped_status ~ s(tend) + ApacheIIScore + s(CombinedicuID, bs="re"),
 data=ped, family=poisson(), offset=offset)
gg_re(pam)
plot(pam, select = 2)

Description

Flexible, high-level plotting function for (non-linear) effects conditional on further covariate specifications and potentially relative to a comparison specification.

Usage

gg_slice(data, model, term, ..., reference = NULL, ci = TRUE)

Arguments

Examples

ped <- tumor[1:200, ] %>% as_ped(Surv(days, status) ~ . )
model <- mgcv::gam(ped_status~s(tend) + s(age, by = complications), data=ped,
  family = poisson(), offset=offset)
make_newdata(ped, age = seq_range(age, 20), complications = levels(complications))
gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications))
gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications),
 ci = FALSE)
gg_slice(ped, model, "age", age=seq_range(age, 20), complications=levels(complications),
  reference=list(age = 50))

Description

Given a gam model this convenience function returns a plot of all smooth terms contained in the model. If more than one smooth is present, the different smooth are faceted.

Usage

gg_smooth(x, ...)

## Default S3 method:
gg_smooth(x, fit, ...)

Arguments

Value

A ggplot object.

See Also

get_terms

Examples

g1 <- mgcv::gam(Sepal.Length ~ s(Sepal.Width) + s(Petal.Length), data=iris)
gg_smooth(iris, g1, terms=c("Sepal.Width", "Petal.Length"))

Description

Given a gam model this convenience function returns a ggplot2 object depicting 2d smooth terms specified in the model as heat/contour plots. If more than one 2d smooth term is present individual terms are faceted.

Usage

gg_tensor(x, ci = FALSE, ...)

Arguments

See Also

tidy_smooth2d

Examples

g <- mgcv::gam(Sepal.Length ~ te(Sepal.Width, Petal.Length), data=iris)
gg_tensor(g)
gg_tensor(g, ci=TRUE)
gg_tensor(update(g, .~. + te(Petal.Width, Petal.Length)))

Description

This functions provides a flexible interface to create a data set that can be plugged in as newdata argument to a suitable predict function (or similar). The function is particularly useful in combination with one of the add_* functions, e.g., add_term, add_hazard, etc.

Usage

make_newdata(x, ...)

## Default S3 method:
make_newdata(x, ...)

## S3 method for class 'ped'
make_newdata(x, ...)

## S3 method for class 'fped'
make_newdata(x, ...)

Arguments

Details

Depending on the type of variables in x, mean or modus values will be used for variables not specified in ellipsis (see also sample_info). If x is an object that inherits from class ped, useful data set completion will be attempted depending on variables specified in ellipsis. This is especially useful, when creating a data set with different time points, e.g. to calculate survival probabilities over time (add_surv_prob) or to calculate a time-varying covariate effects (add_term). To do so, the time variable has to be specified in ..., e.g., tend = seq_range(tend, 20). The problem with this specification is that not all values produced by seq_range(tend, 20) will be actual values of tend used at the stage of estimation (and in general, it will often be tedious to specify exact tend values). make_newdata therefore finds the correct interval and sets tend to the respective interval endpoint. For example, if the intervals of the PED object are $(0,1], (1,2]$ then tend = 1.5 will be set to 2 and the remaining time-varying information (e.g. offset) completed accordingly. See examples below.

Examples

# General functionality
tumor %>% make_newdata()
tumor %>% make_newdata(age=c(50))
tumor %>% make_newdata(days=seq_range(days, 3), age=c(50, 55))
tumor %>% make_newdata(days=seq_range(days, 3), status=unique(status), age=c(50, 55))
# mean/modus values of unspecified variables are calculated over whole data
tumor %>% make_newdata(sex=unique(sex))
tumor %>% group_by(sex) %>% make_newdata()

# Examples for PED data
ped <- tumor %>% slice(1:3) %>% as_ped(Surv(days, status)~., cut = c(0, 500, 1000))
ped %>% make_newdata(age=c(50, 55))

# if time information is specified, other time variables will be specified
# accordingly and offset calculated correctly
ped %>% make_newdata(tend = c(1000), age = c(50, 55))
ped %>% make_newdata(tend = unique(tend))
ped %>% group_by(sex) %>% make_newdata(tend = unique(tend))

# tend is set to the end point of respective interval:
ped <- tumor %>% as_ped(Surv(days, status)~.)
seq_range(ped$tend, 3)
make_newdata(ped, tend = seq_range(tend, 3))

Description

pammtools provides functions and utilities that facilitate fitting Piece-wise Exponential Additive Mixed Models (PAMMs), including data transformation and other convenience functions for pre- and post-processing as well as plotting.

Details

The best way to get an overview of the functionality provided and how to fit PAMMs is to view the vignettes available at https://adibender.github.io/pammtools/articles/. A summary of the vignettes' content is given below:

• basics: Introduction to PAMMs and basic modeling.

• baseline: Shows how to estimate and visualize baseline model (without covariates) and comparison to respective Cox-PH model.

• convenience: Convenience functions for post-processing and plotting PAMMs.

• data-transformation: Transforming data into a format suitable to fit PAMMs.

• frailty: Specifying "frailty" terms, i.e., random effects for PAMMs.

• splines: Specifying spline smooth terms for PAMMs.

• strata: Specifying stratified models in which each level of a grouping variable has a different baseline hazard.

• tdcovar: Dealing with time-dependent covariates.

• tveffects: Specifying time-varying effects.

• left-truncation: Estimation for left-truncated data.

• competing-risks: Competing risks analysis.

Author(s)

Maintainer: Andreas Bender [email protected] (ORCID)

Authors:

• Fabian Scheipl [email protected] (ORCID)

• Philipp Kopper [email protected] (ORCID)

Other contributors:

• Lukas Burk [email protected] (ORCID) [contributor]

References

Bender, Andreas, Andreas Groll, and Fabian Scheipl. 2018. “A Generalized Additive Model Approach to Time-to-Event Analysis” Statistical Modelling, February. https://doi.org/10.1177/1471082X17748083.

Bender, Andreas, Fabian Scheipl, Wolfgang Hartl, Andrew G. Day, and Helmut Küchenhoff. 2019. “Penalized Estimation of Complex, Non-Linear Exposure-Lag-Response Associations.” Biostatistics 20 (2): 315–31. https://doi.org/10.1093/biostatistics/kxy003.

Bender, Andreas, and Fabian Scheipl. 2018. “pammtools: Piece-Wise Exponential Additive Mixed Modeling Tools.” ArXiv:1806.01042 Stat, June. https://arxiv.org/abs/1806.01042.

See Also

Useful links:

• https://adibender.github.io/pammtools/

• Report bugs at https://github.com/adibender/pammtools/issues

Description

A data set containing the survival time (or hospital release time) among other covariates. The full data is available here. The following variables are provided:

Year

The year of ICU Admission

CombinedicuID

Intensive Care Unit (ICU) ID

CombinedID

Patient identificator

Survdays

Survival time of patients. Here it is assumed that patients survive until t=30 if released from hospital.

PatientDied

Status indicator; 1=death, 0=censoring

survhosp

Survival time in hospital. Here it is assumed that patients are censored at time of hospital release (potentially informative)

Gender

Male or female

Age

The patients age at Admission

AdmCatID

Admission category: medical, surgical elective or surgical emergency

ApacheIIScore

The patient's Apache II Score at Admission

BMI

Patient's Body Mass Index

DiagID2

Diagnosis at admission in 9 categories

Usage

patient

Format

An object of class data.frame with 2000 rows and 12 columns.

Description

Given an object of class ped, returns data frame with one row for each interval containing interval information, mean values for numerical variables and modus for non-numeric variables in the data set.

Usage

ped_info(ped)

## S3 method for class 'ped'
ped_info(ped)

Arguments

Value

A data frame with one row for each unique interval in ped.

See Also

int_info, sample_info

Examples

ped <- tumor[1:4,] %>% as_ped(Surv(days, status)~ sex + age)
ped_info(ped)

Description

S3 method for pamm objects for compatibility with package pec

Usage

## S3 method for class 'pamm'
predictSurvProb(object, newdata, times, ...)

Arguments

Description

Stolen from here

Usage

seq_range(x, n, by, trim = NULL, expand = NULL, pretty = FALSE)

Arguments

Examples

x <- rcauchy(100)
seq_range(x, n = 10)
seq_range(x, n = 10, trim = 0.1)
seq_range(x, by = 1, trim = 0.1)

# Make pretty sequences
y <- runif (100)
seq_range(y, n = 10)
seq_range(y, n = 10, pretty = TRUE)
seq_range(y, n = 10, expand = 0.5, pretty = TRUE)

seq_range(y, by = 0.1)
seq_range(y, by = 0.1, pretty = TRUE)

Description

Simulate survival times from the piece-wise exponential distribution

Usage

sim_pexp(formula, data, cut)

Arguments

Examples

library(survival)
library(dplyr)
library(pammtools)

# set number of observations/subjects
n <- 250
# create data set with variables which will affect the hazard rate.
df <- cbind.data.frame(x1 = runif (n, -3, 3), x2 = runif (n, 0, 6)) %>%
 as_tibble()
# the formula which specifies how covariates affet the hazard rate
f0 <- function(t) {
 dgamma(t, 8, 2) *6
}
form <- ~ -3.5 + f0(t) -0.5*x1 + sqrt(x2)
set.seed(24032018)
sim_df <- sim_pexp(form, df, 1:10)
head(sim_df)
plot(survfit(Surv(time, status)~1, data = sim_df ))

# for control, estimate with Cox PH
mod <- coxph(Surv(time, status) ~ x1 + pspline(x2), data=sim_df)
coef(mod)[1]
layout(matrix(1:2, nrow=1))
termplot(mod, se = TRUE)

# and using PAMs
layout(1)
ped <- sim_df %>% as_ped(Surv(time, status)~., max_time=10)
library(mgcv)
pam <- gam(ped_status ~ s(tend) + x1 + s(x2), data=ped, family=poisson, offset=offset)
coef(pam)[2]
plot(pam, page=1)

## Not run: 
# Example 2: Functional covariates/cumulative coefficients
# function to generate one exposure profile, tz is a vector of time points
# at which TDC z was observed
rng_z = function(nz) {
  as.numeric(arima.sim(n = nz, list(ar = c(.8, -.6))))
}
# two different exposure times  for two different exposures
tz1 <- 1:10
tz2 <- -5:5
# generate exposures and add to data set
df <- df %>%
  add_tdc(tz1, rng_z) %>%
  add_tdc(tz2, rng_z)
df

# define tri-variate function of time, exposure time and exposure z
ft <- function(t, tmax) {
  -1*cos(t/tmax*pi)
}
fdnorm <- function(x) (dnorm(x,1.5,2)+1.5*dnorm(x,7.5,1))
wpeak2 <- function(lag) 15*dnorm(lag,8,10)
wdnorm <- function(lag) 5*(dnorm(lag,4,6)+dnorm(lag,25,4))
f_xyz1 <- function(t, tz, z) {
  ft(t, tmax=10) * 0.8*fdnorm(z)* wpeak2(t - tz)
}
f_xyz2 <- function(t, tz, z) {
  wdnorm(t-tz) * z
}

# define lag-lead window function
ll_fun <- function(t, tz) {t >= tz}
ll_fun2 <- function(t, tz) {t - 2 >= tz}
# simulate data with cumulative effect
sim_df <- sim_pexp(
  formula = ~ -3.5 + f0(t) -0.5*x1 + sqrt(x2)|
     fcumu(t, tz1, z.tz1, f_xyz=f_xyz1, ll_fun=ll_fun) +
     fcumu(t, tz2, z.tz2, f_xyz=f_xyz2, ll_fun=ll_fun2),
  data = df,
  cut = 0:10)

## End(Not run)

Description

This is data simulated using the sim_pexp function. It contains two time-constant and two time-dependent covariates (observed on different exposure time grids). The code used for simulation is contained in the examples of ?sim_pexp.

Usage

simdf_elra

Format

An object of class nested_fdf (inherits from sim_df, tbl_df, tbl, data.frame) with 250 rows and 9 columns.

Description

This dataset originates from the Drakenstein child health study. The data contains the following variables:

id

Randomly generated unique child ID

t.start

The time at which the child enters the risk set for the $k$-th event

t.stop

Time of $k$-th infection or censoring

.

enum

Event number. Maximum of 6.

hiv

Usage

staph

Format

An object of class tbl_df (inherits from tbl, data.frame) with 374 rows and 6 columns.

Description

Extract random effects in tidy data format.

Usage

tidy_re(x, keep = c("fit", "main", "xlab", "ylab"), ...)

Arguments

See Also

qqline

Description

Extract 1d smooth objects in tidy data format.

Usage

tidy_smooth(x, keep = c("x", "fit", "se", "xlab", "ylab"), ci = TRUE, ...)

Arguments

Description

Extract 2d smooth objects in tidy format.

Usage

tidy_smooth2d(
  x,
  keep = c("x", "y", "fit", "se", "xlab", "ylab", "main"),
  ci = FALSE,
  ...
)

Arguments

Description

Information on patients treated for a cancer disease located in the stomach area. The data set includes:

days

Time from operation until death in days.

status

Event indicator (0 = censored, 1 = death).

age

The subject's age.

sex

The subject's sex (male/female).

charlson_score

Charlson comorbidity score, 1-6.

transfusion

Has subject received transfusions (no/yes).

complications

Did major complications occur during operation (no/yes).

metastases

Did the tumor develop metastases? (no/yes).

resection

Was the operation accompanied by a major resection (no/yes).

Usage

tumor

Format

An object of class tbl_df (inherits from tbl, data.frame) with 776 rows and 9 columns.