Package 'biostat3' reference manual

Title:	Utility Functions, Datasets and Extended Examples for Survival Analysis
Description:	Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".
Authors:	Annika Tillander [ctb], Andreas Karlsson [aut], Johan Zetterqvist [ctb], Peter Strom [ctb], Benedicte Delcoigne [ctb], Mark Clements [aut, cre]
Maintainer:	Mark Clements <[email protected]>
License:	GPL (>=2)
Version:	0.2.3
Built:	2026-05-19 07:45:33 UTC
Source:	https://github.com/mclements/biostat3

Utility Functions, Datasets and Extended Examples for Survival Analysis

Description

Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".

Author(s)

Annika Tillander [ctb], Andreas Karlsson [aut], Johan Zetterqvist [ctb], Peter Strom [ctb], Benedicte Delcoigne [ctb], Mark Clements [aut, cre]

Maintainer: Mark Clements <[email protected]>

Examples

  plot(muhaz2(Surv(surv_mm, status == "Dead: cancer")~1, melanoma))
plot(muhaz2(Surv(surv_mm, status == "Dead: cancer")~1, melanoma))

Utility to add indicators from a data-frame based on a formula.

Description

Column-bind a model matrix to the source data-frame

Usage

addIndicators(data, formula, drop.intercept = TRUE)
addIndicators(data, formula, drop.intercept = TRUE)

Arguments

data

source data-frame or matrix.

formula

model formula used to add columns.

drop.intercept

logical as to whether to drop the column named '(Intercept)'.

Details

This function calls model.matrix, conditionally checks for and removes '(Intercept)', and binds with the original data-frame (or matrix).

Value

data-frame or matrix.

Examples

addIndicators(data.frame(f = c("a","a","b")), ~f+0)
addIndicators(data.frame(f = c("a","a","b")), ~f+0)

Functions to work with bshazard objects.

Description

Convert a bshazard object to a data-frame.

Usage

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

Arguments

x

bshazard object

...

other arguments

Value

Returns a data-frame with names time, hazard, conf.low and conf.high (cf. lower.ci and upper.ci provided in the object).

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, ...) 
{
    with(x, data.frame(time, hazard, conf.low = lower.ci, conf.high = upper.ci))
  }
##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, ...) 
{
    with(x, data.frame(time, hazard, conf.low = lower.ci, conf.high = upper.ci))
  }

Bereavement dataset

Description

Bereavement dataset

Usage

data("brv")data("brv")

Format

A data frame with 399 observations on the following 11 variables.

id: a numeric vector the id of a subject
couple: a numeric vector for the id of a couple
dob: a Date for the date of birth
doe: a Date for the date of entry into study
dox: a Date for the date of exit from study
dosp: a Date for the date of bereavement
fail: a numeric vector for status at study exit 0=alive 1=died
group: a numeric vector for Group
disab: a numeric vector for disability level
health: a numeric vector for perceived health status
sex: a numeric vector for sex 1=M 2=F

Examples

data(brv)
## maybe str(brv) ; plot(brv) ...
data(brv)
## maybe str(brv) ; plot(brv) ...

Colon cancer dataset

Description

Colon cancer patients based on a simulation of a population-based cancer registry.

Usage

data("colon")data("colon")

Format

A data frame with 15564 observations on the following 18 variables.

sex: a factor with levels Male Female
age: a numeric vector age at diagnosis in years (12-99)
stage: a factor with levels Unknown Localised Regional Distant
mmdx: a numeric vector for the month of diagnosis (1-12)
yydx: a numeric vector for the year of diagnosis (1975-1994)
surv_mm: a numeric vector observed time from diagnosis to end of follow-up in months (0.5-251.5)
surv_yy: a numeric vector observed time from diagnosis to end of follow-up in years (0.5-20.5)
status: a factor with levels Alive Dead: cancer Dead: other Lost to follow-up
subsite: a factor with levels Coecum and ascending Transverse Descending and sigmoid Other and NOS
year8594: a factor with levels Diagnosed 75-84 Diagnosed 85-94
agegrp: a factor with levels 0-44 45-59 60-74 75+
dx: a Date date of diagnosis
exit: a Date date of exit
id: a numeric vector individual identifier
ydx: a numeric vector for continuous year of diagnosis
yexit: a numeric vector for continuous year of exit

Examples

data(colon)
## maybe str(colon) ; plot(colon) ...
data(colon)
## maybe str(colon) ; plot(colon) ...

Sample from the `colon` dataset used for teaching.

Description

Sample from the colon dataset, which describes survival after a diagnosis of colon cancer.

Usage

data("colon_sample")data("colon_sample")

Format

A data frame with 35 observations on the following 9 variables.

sex: a factor with levels Male Female
age: a numeric vector age at diagnosis in years
stage: a factor with levels Unknown Localised Regional Distant
mmdx: a numeric vector month of diagnosis (1-12)
yydx: a numeric vector year of diagnosis
surv_mm: a numeric vector time from diagnosis to exit (months)
surv_yy: a numeric vector time from diagnosis to exit (years)
status: a factor with levels Alive Dead: cancer Dead: other Lost to follow-up
subsite: a factor with levels Coecum and ascending Transverse Descending and sigmoid Other and NOS

Examples

data(colon_sample)
## maybe str(colon_sample) ; plot(colon_sample) ...
data(colon_sample)
## maybe str(colon_sample) ; plot(colon_sample) ...

Smoothed hazard estimates for `coxph`

Description

Smoothed hazard estimates for coxph

Usage

coxphHaz(object, newdata, n.grid = 300, kernel = "epanechnikov", from,
to, ...)
## S3 method for class 'coxphHaz'
print(x, digits=NULL, ...)
## S3 method for class 'coxphHaz'
plot(x, xlab="Time", ylab="Hazard", type="l", ...)
## S3 method for class 'coxphHazList'
plot(x, xlab="Time", ylab="Hazard", type="l",
                  col=1:length(x), lty=1, legend.args=list(), ...)
## S3 method for class 'coxphHazList'
lines(x, ...)
## S3 method for class 'coxphHaz'
as.data.frame(x, row.names=NULL, optional=FALSE, level=0.95, ...)
## S3 method for class 'coxphHazList'
as.data.frame(x, row.names=NULL, optional=FALSE, ...)
coxphHaz(object, newdata, n.grid = 300, kernel = "epanechnikov", from,
to, ...)
## S3 method for class 'coxphHaz'
print(x, digits=NULL, ...)
## S3 method for class 'coxphHaz'
plot(x, xlab="Time", ylab="Hazard", type="l", ...)
## S3 method for class 'coxphHazList'
plot(x, xlab="Time", ylab="Hazard", type="l",
                  col=1:length(x), lty=1, legend.args=list(), ...)
## S3 method for class 'coxphHazList'
lines(x, ...)
## S3 method for class 'coxphHaz'
as.data.frame(x, row.names=NULL, optional=FALSE, level=0.95, ...)
## S3 method for class 'coxphHazList'
as.data.frame(x, row.names=NULL, optional=FALSE, ...)

Arguments

object

coxph object

newdata

data-frame with covariates for prediction

n.grid

the number of grid values for which the hazard is calculated

kernel

the kernel used for smoothing

from

argument for density. Defaults to the minimum time.

to

argument for density. Defaults to the maximum time.

x

object

digits

argument passed to print.density

col

graphics argument

lty

graphics argument

xlab

graphics argument

ylab

graphics argument

type

graphics argument

level

level for confidence intervals (default=0.95)

row.names

NULL or a character vector giving the row names for the data frame. Missing values are not allowed.

optional

logical. If TRUE, setting row names and converting column names (to syntactic names: see make.names) is optional. Note that all of R's base package as.data.frame() methods use optional only for column names treatment, basically with the meaning of data.frame(*, check.names = !optional). See also the make.names argument of the matrix method.

legend.args

a list of options that are passed to the legend call. Defaults are list(x="topright",legend=strata(attr(x,"newdata")),col=col,lty=lty).

...

other arguments. For coxphHaz, these arguments are passed to density. For the plot and lines methods, these are passed to the relevant plot, matplot and matlines functions.

Details

Smooth hazard estimates from a Cox model using kernel smoothing of the Nelson-Aalen estimator.

Value

The coxphHaz function returns either a class of type c("coxphHaz","density") when newdata has one row or, for multiple rows in newdata, a class of type "coxphHazList", which is a list of type c("coxphHaz","density").

Examples

fit <- coxph(Surv(surv_mm/12,status=="Dead: cancer")~agegrp, data=colon)
newdata <- data.frame(agegrp=levels(colon$agegrp))
haz <- suppressWarnings(coxphHaz(fit,newdata))
plot(haz, xlab="Time since diagnosis (years)")
fit <- coxph(Surv(surv_mm/12,status=="Dead: cancer")~agegrp, data=colon)
newdata <- data.frame(agegrp=levels(colon$agegrp))
haz <- suppressWarnings(coxphHaz(fit,newdata))
plot(haz, xlab="Time since diagnosis (years)")

Diet data set

Description

Dataset on a cohort investigating the incidence of coronary heart disease (CHD)

Usage

data("diet")data("diet")

Format

A data frame with 337 observations on the following 15 variables.

id: a numeric vector individual identifier
chd: a numeric vector event indicator for CHD at study exit (1=Yes, 0=No)
y: a numeric vector number of years at risk
hieng: a factor with levels low for energy < 2750 or high for energy >= 2750
energy: a numeric vector total recorded energy intake (kCal/day/100)
job: a factor for occupation with levels for bus driver, bus conductor and bank work
month: a numeric vector month of the year (1-12)
height: a numeric vector height in cm
weight: a numeric vector weight in kg
doe: a Date for date of study entry
dox: a Date for date of study exit
dob: a Date for date of birth
yob: a numeric vector for continuous year of birth
yoe: a numeric vector for continuous year of entry
yox: a numeric vector for continuous year of exit

Examples

data(diet)
## maybe str(diet) ; plot(diet) ...
data(diet)
## maybe str(diet) ; plot(diet) ...

Calculate the exponential form for coefficients and their confidence intervals using either profile likelihood-based or Wald-based confidence intervals.

Description

irr and or use eform with a different name for the estimator.

Usage

eform(object, ...)
## Default S3 method:
eform(object, parm, level = 0.95, method =
c("Delta","Profile"), name = "exp(beta)", ...)
irr(..., name = "IRR")
or(..., name = "OR")
eform(object, ...)
## Default S3 method:
eform(object, parm, level = 0.95, method =
c("Delta","Profile"), name = "exp(beta)", ...)
irr(..., name = "IRR")
or(..., name = "OR")

Arguments

object

A fitted model object with coef and confint methods

parm

a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.

level

the confidence level required

method

string to determine method to use the delta method (stats::confint.default), which assumes that the parameters are asymptotically normal, or profile likelihood-based confidence intervals (MASS:::confint.gllm), respectively.

name

name of the estimator.

...

arguments to pass from irr or or to eform.

Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in

Examples

## from example(glm)
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3, 1, 9); treatment <- gl(3, 3)
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
eform(glm.D93) 
eform(glm.D93, method="Profile") 
## from example(glm)
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3, 1, 9); treatment <- gl(3, 3)
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
eform(glm.D93) 
eform(glm.D93, method="Profile")

Create cohort life table

Description

Create cohort life table.

Usage

lifetab(tis, ninit, nlost, nevent)
lifetab(tis, ninit, nlost, nevent)

Arguments

tis

a vector of end points of time intervals, whose length is 1 greater than nlost and nevent.

ninit

the number of subjects initially entering the study.

nlost

a vector of the number of individuals lost follow or withdrawn alive for whatever reason.

nevent

a vector of the number of individuals who experienced the event

Details

This is a minor update of the lifetab function from the KMsurv package, where the start and stop times of the intervals are now included in the return value.

Value

A data.frame with the following columns:

tstart

interval start time.

tstop

interval end time.

nsubs

the number of subject entering the intervals who have not experienced the event.

nlost

the number of individuals lost follow or withdrawn alive for whatever reason.

nrisk

the estimated number of individuals at risk of experiencing the event.

nevent

the number of individuals who experienced the event.

surv

the estimated survival function at the start of the intervals.

pdf

the estimated probability density function at the midpoint of the intervals.

hazard

the estimated hazard rate at the midpoint of the intervals.

se.surv

the estimated standard deviation of survival at the beginning of the intervals.

se.pdf

the estimated standard deviation of the prbability density function at the midpoint of the intervals.

se.hazard

the estimated standard deviation of the hazard function at the midpoint of the intervals

The row.names are the intervals.

Author(s)

Jun Yan [email protected]

Examples

tis <- c(0, 2, 3, 5, 7, 11, 17, 25, 37, 53, NA)
nsubs <- c(927, 848, 774, 649, 565, 449, 296, 186, 112, 27)
nlost <- c(2, 3, 6, 9, 7, 5, 3, rep(0, 3))
nevent <- c(77, 71, 119, 75, 109, 148, 107, 74, 85, 27)

lifetab(tis, nsubs[1], nlost, nevent)
tis <- c(0, 2, 3, 5, 7, 11, 17, 25, 37, 53, NA)
nsubs <- c(927, 848, 774, 649, 565, 449, 296, 186, 112, 27)
nlost <- c(2, 3, 6, 9, 7, 5, 3, rep(0, 3))
nevent <- c(77, 71, 119, 75, 109, 148, 107, 74, 85, 27)

lifetab(tis, nsubs[1], nlost, nevent)

Formula wrapper for `lifetab` from the `KMsurv` package.

Description

Calculate a life table using the actuarial method using a formula and a data-frame with optional breaks.

Usage

lifetab2(formula, data, subset, breaks = NULL)
## S3 method for class 'lifetab2'
plot(x, y=NULL, type="s", ...)
## S3 method for class 'lifetab2'
lines(x, y=NULL, type="s", ...)
## S3 method for class 'lifetab2'
points(x, y=NULL, ...)
lifetab2(formula, data, subset, breaks = NULL)
## S3 method for class 'lifetab2'
plot(x, y=NULL, type="s", ...)
## S3 method for class 'lifetab2'
lines(x, y=NULL, type="s", ...)
## S3 method for class 'lifetab2'
points(x, y=NULL, ...)

Arguments

formula

formula with the left-hand side being a Surv object, including a time and event indicator, and the right-hand side indicated stratification.

data

optional data.frame for the Surv object. If this is not provided, then the parent frame is used for the Surv object.

subset

optional subset statement

breaks

optional numeric vector of breaks. If this is not provided, then the unique time values from the Surv object are used together with Inf.

x

lifetab2 object

y

unused argument (part of the generic function)

type

line type

...

other arguments

Details

See lifetab for details. This wrapper is meant to make life easier.

A copy of the lifetab function has been included in the biostat3 package to reduce dependencies.

Value

A data.frame as per lifetab.

Author(s)

Mark Clements for the wrapper.

Examples

## we can use unique transformed times (colon_sample)
lifetab2(Surv(floor(surv_yy),status=="Dead: cancer")~1, colon_sample)

## we can also use the breaks argument (colon)
lifetab2(Surv(surv_yy,status=="Dead: cancer")~1, colon, breaks=0:10)
## we can use unique transformed times (colon_sample)
lifetab2(Surv(floor(surv_yy),status=="Dead: cancer")~1, colon_sample)

## we can also use the breaks argument (colon)
lifetab2(Surv(surv_yy,status=="Dead: cancer")~1, colon, breaks=0:10)

Linear combination of regression parameters.

Description

Using results calculated by the linearHypothesis function in the car package, calculate a linear combination of regression parameters.

Usage

lincom(model, specification, level = 0.95, eform = FALSE, ...)
lincom(model, specification, level = 0.95, eform = FALSE, ...)

Arguments

model

regression model object (as per the model argument in linearHypothesis)

specification

specification of the linear combination. This is the same as a single component of the hypothesis.matrix argument in linearHypothesis.

level

the confidence level required

eform

logical for whether to exponentiate the confidence interval (default=FALSE)

...

other arguments to the linearHypothesis function.

Details

Multiple specifications of linear combinations are called individually.

Value

A matrix with columns including the estimate, a normal-based confidence interval, test statistic and p-values.

Examples

  fit <- glm(chd ~ hieng*job + offset(log(y)), data=diet, family=poisson)
  lincom(fit, c("hienghigh+hienghigh:jobconductor",
                "hienghigh+hienghigh:jobbank"),
         eform=TRUE)
fit <- glm(chd ~ hieng*job + offset(log(y)), data=diet, family=poisson)
  lincom(fit, c("hienghigh+hienghigh:jobconductor",
                "hienghigh+hienghigh:jobbank"),
         eform=TRUE)

Melanoma cancer dataset

Description

Melanoma cancer dataset

Usage

data("melanoma")data("melanoma")

Format

A data frame with 7775 observations on the following 18 variables.

sex: a factor with levels Male Female
age: a numeric vector for age at diagnosis in years (0-99)
stage: a factor with levels Unknown Localised Regional Distant
mmdx: a numeric vector for the month of diagnosis (1-12)
yydx: a numeric vector for the year of diagnosis (1975-1994)
surv_mm: a numeric vector observed time from diagnosis to end of follow-up in months (0.5-251.5)
surv_yy: a numeric vector observed time from diagnosis to end of follow-up in years (0.5-20.5)
status: a factor with levels Alive Dead: cancer Dead: other Lost to follow-up
subsite: a factor with levels Head and Neck Trunk Limbs Multiple and NOS
year8594: a factor with levels Diagnosed 75-84 Diagnosed 85-94
dx: a Date date of diagnosis
exit: a Date date of exit
agegrp: a factor with levels 0-44 45-59 60-74 75+
id: a numeric vector individual identifier
ydx: a numeric vector for continuous year of diagnosis
yexit: a numeric vector for continuous year of exit

Examples

data(melanoma)
## maybe str(melanoma) ; plot(melanoma) ...
data(melanoma)
## maybe str(melanoma) ; plot(melanoma) ...

Formula wrapper for the `muhaz` function from the `muhaz` package.

Description

Formula wrapper for the muhaz function from the muhaz package.

Usage

muhaz2(formula, data, subset, max.time, ...)
## S3 method for class 'muhaz2'
plot(x, haz.scale=1, ylab="Hazard", ylim=NULL, log="", ...)
## S3 method for class 'muhazList'
plot(x, lty=1:5, col=1:length(x), log="", legend.args=list(), ...)
## S3 method for class 'muhaz2'
lines(x, ..., haz.scale = 1)
## S3 method for class 'muhazList'
lines(x, lty=1, col=1:length(x), ...)
## S3 method for class 'muhazList'
summary(object, ...)
## S3 method for class 'muhazList'
ggplot(data, mapping=NULL, 
                            xlab="Time", ylab="Hazard", ...,
                            environment = parent.frame())
## S3 method for class 'muhazList'
as.data.frame(x, row.names, optional, ...)
## S3 method for class 'muhaz'
as.data.frame(x, row.names, optional, ...)
muhaz2(formula, data, subset, max.time, ...)
## S3 method for class 'muhaz2'
plot(x, haz.scale=1, ylab="Hazard", ylim=NULL, log="", ...)
## S3 method for class 'muhazList'
plot(x, lty=1:5, col=1:length(x), log="", legend.args=list(), ...)
## S3 method for class 'muhaz2'
lines(x, ..., haz.scale = 1)
## S3 method for class 'muhazList'
lines(x, lty=1, col=1:length(x), ...)
## S3 method for class 'muhazList'
summary(object, ...)
## S3 method for class 'muhazList'
ggplot(data, mapping=NULL, 
                            xlab="Time", ylab="Hazard", ...,
                            environment = parent.frame())
## S3 method for class 'muhazList'
as.data.frame(x, row.names, optional, ...)
## S3 method for class 'muhaz'
as.data.frame(x, row.names, optional, ...)

Arguments

formula

formula with the left-hand side being a Surv object, including a time and event indicator, and the right-hand side indicated stratification.

data

optional data.frame for the Surv object. If this is not provided, then the parent frame is used for the Surv object.

subset

subset predictate for the dataset

max.time

maximum follow-up time for the hazards

xlab

graphics argument for xlab (x-axis label)

ylab

graphics argument for ylab (y-axis label)

lty

graphics argument for line type

col

graphics argument for line colour

legend.args

a list of options that are passed to the legend call. Defaults are list(x="topright",legend=names(x),col=col,lty=lty).

haz.scale

scale for the hazard in the plot

row.names

not currently used

object

muhazList object

ylim

graphics argument for the limits of the y axis

log

graphics argument for a log transformation of the x or y axes

x

muhazList or muhaz object

environment

*[Deprecated]* Used prior to tidy evaluation.

optional

not currently used

mapping

Default list of aesthetic mappings to use for plot. If not specified, must be supplied in each layer added to the plot.

...

other arguments

Value

For a single strata, this is a muhaz object. For multiple strata, this is a muhazList object, which includes methods for

Examples

  plot(muhaz2(Surv(surv_mm, status == "Dead: cancer")~1, melanoma))
plot(muhaz2(Surv(surv_mm, status == "Dead: cancer")~1, melanoma))

Exact Poisson confidence intervals.

Description

A wrapper for the poisson.test that allows for vector values.

Usage

poisson.ci(x, T = 1, alternative = c("two.sided", "less", "greater"), conf.level = 0.95)
poisson.ci(x, T = 1, alternative = c("two.sided", "less", "greater"), conf.level = 0.95)

Arguments

x

number of events.

T

time base for event count.

alternative

indicates the type of confidence interval and must be one of "two.sided", "greater" or "less". You can specify just the initial letter.

conf.level

confidence level for the returned confidence interval.

Details

This uses a vectorised algorithm based on stats::poisson.test.

Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in % (by default 2.5% and 97.5%). for the two-sided alternative.

Examples

### These are paraphrased from data sets in the ISwR package

## SMR, Welsh Nickel workers
poisson.ci(137, 24.19893)

## eba1977, compare Fredericia to other three cities for ages 55-59
poisson.ci(c(11, 6+8+7), c(800, 1083+1050+878))
### These are paraphrased from data sets in the ISwR package

## SMR, Welsh Nickel workers
poisson.ci(137, 24.19893)

## eba1977, compare Fredericia to other three cities for ages 55-59
poisson.ci(c(11, 6+8+7), c(800, 1083+1050+878))

popmort dataset, with population-based mortality rates

Description

popmort dataset, with population-based mortality rates

Usage

data("popmort")data("popmort")

Format

A data frame with 10600 observations on the following 5 variables.

sex: a numeric vector
‘⁠_year⁠’: a numeric vector
‘⁠_age⁠’: a numeric vector
prob: a numeric vector
rate: a numeric vector

Examples

data(popmort)
## maybe str(popmort) ; plot(popmort) ...
data(popmort)
## maybe str(popmort) ; plot(popmort) ...

Simple implementation for kernel density smoothing of the Nelson-Aalen estimator.

Description

Simple implementation for kernel density smoothing of the Nelson-Aalen estimator. Prefer muhaz for right censored data and bshazard for left truncated and right censored data.

Usage

smoothHaz(object, n.grid = 300, kernel = "epanechnikov",
          from = NULL, to = NULL, min.n.risk = 1, ...)
## S3 method for class 'smoothHaz'
plot(x, xlab = "Time", ylab = "Hazard", type = "l", ...)
smoothHaz(object, n.grid = 300, kernel = "epanechnikov",
          from = NULL, to = NULL, min.n.risk = 1, ...)
## S3 method for class 'smoothHaz'
plot(x, xlab = "Time", ylab = "Hazard", type = "l", ...)

Arguments

object

survfit object

n.grid

number of grid points; passed to density

kernel

kernel used; passed to density

from

left boundary; passed to density

to

right boundary; passed to density

min.n.risk

minimum number at risk

x

object of class smoothHaz

xlab

graphics argument

ylab

graphics argument

type

graphics argument

...

Other arguments

Plot to assess non-proportionality

Description

Plot of log(time) versus -log(-log(survival)) to assess non-proportionality. A constant distance between curves suggest proportionality.

Usage

survPHplot(formula, data, subset, contrasts, weights, col = 1:5,
           lty = 1:5, pch = 19, xlab = "Time (log scale)",
           ylab = "-log(-log(Survival))", log = "x",
           legend.args = list(), ...)
survPHplot(formula, data, subset, contrasts, weights, col = 1:5,
           lty = 1:5, pch = 19, xlab = "Time (log scale)",
           ylab = "-log(-log(Survival))", log = "x",
           legend.args = list(), ...)

Arguments

formula

either (i) formula with a Surv object on the left-hand-side and stratification covariates on the right-hand-side, or (ii) a survfit object

data

data argument passed to survfit

subset

subset argument passed to survfit

contrasts

contrasts argument passed to survfit

weights

weights argument passed to survfit

col

colours of the curves passed to lines

lty

line type of the curves passed to lines

pch

pch for the curves passed to points

xlab

xlab graphics argument passed to plot.default

ylab

ylab graphics argument passed to plot.default

log

log graphics argument passed to plot.default

legend.args

list of arguments passed to legend. These arguments update the base arguments, which are list(x="topright",legend=names(survfit$strata),col=col,lty=lty,pch=pch)

...

Other arguments passed to plot.default

Details

The default plot is to use straight lines between the transformed survival values for each strata, rather than using steps.

Value

Primary purpose is for plotting (side effect). The return value is initial plot.

Examples

survPHplot(Surv(surv_mm/12, status == "Dead: cancer") ~ year8594,
           data=colon, subset=(stage=="Localised"),
           legend.args=list(bty="n"))
survPHplot(Surv(surv_mm/12, status == "Dead: cancer") ~ year8594,
           data=colon, subset=(stage=="Localised"),
           legend.args=list(bty="n"))

Describe rates

Description

Describe rates using the Surv function.

Usage

survRate(formula, data, subset, addvars = TRUE, ci=TRUE, ...)
survRate(formula, data, subset, addvars = TRUE, ci=TRUE, ...)

Arguments

formula

formula with the left-hand-side being a Surv function and the right-hand-side being any stratification variables.

data

source dataset

subset

subset conditions for the source dataset

addvars

logical for whether to add the stratification variables to the output (default=TRUE). This is useful for subsequent analysis.

ci

logical for whether to calculate the confidence interval (default=TRUE).

...

other arguments to the poisson.test function for calculation of the confidence intervals.

Value

data-frame with columns tstop, event, rate, lower and upper. Covariates are appended if addvar=TRUE.

Confidence intervals use stats::poisson.test.

Examples

## incidence rates for CHD for low- or high-energy diets
survRate(Surv(y,chd) ~ hieng, data=diet)
## incidence rates for CHD for low- or high-energy diets
survRate(Surv(y,chd) ~ hieng, data=diet)

Utility functions for the `biostat3` package

Description

Utility functions for the biostat3 package.

Usage

updateList(object, ...)
format_perc(probs, digits)
updateList(object, ...)
format_perc(probs, digits)

Arguments

object

base object (list)

...

arguments to update

probs

probability to express as a percentage

digits

number of significant digits

Details

Update the names in the base object list that are specified in the arguments to update.

Value

list

Examples

  updateList(list(a=1,b=2), a=10, c=30)
updateList(list(a=1,b=2), a=10, c=30)

Convert a `Date` vector to a numeric vector

Description

Convert a Date vector to a numeric vector (either continuous or truncated).

Usage

year(date, trunc = FALSE, year.length = 365.24)
year(date, trunc = FALSE, year.length = 365.24)

Arguments

date

Date vector

trunc

logical for whether to truncate the date to a whole year or consider the date as a double (default).

year.length

assumed length of a year

Details

For the double calculation, we use (truncated year of Date) + (date - 1 Jan of Year)/year.length.

Value

numeric vector

Examples

  c(year(as.Date("2001-07-01")),year(as.Date("2001-01-01"),trunc=TRUE))
c(year(as.Date("2001-07-01")),year(as.Date("2001-01-01"),trunc=TRUE))

Package 'biostat3'

Help Index

Utility Functions, Datasets and Extended Examples for Survival Analysis

Description

Author(s)

Examples

Utility to add indicators from a data-frame based on a formula.

Description

Usage

Arguments

Details

Value

Examples

Functions to work with bshazard objects.

Description

Usage

Arguments

Value

Examples

Bereavement dataset

Description

Usage

Format

Examples

Colon cancer dataset

Description

Usage

Format

Examples

Sample from the colon dataset used for teaching.

Description

Usage

Format

Examples

Smoothed hazard estimates for coxph

Description

Usage

Arguments

Details

Value

See Also

Examples

Diet data set

Description

Usage

Format

Examples

Calculate the exponential form for coefficients and their confidence intervals using either profile likelihood-based or Wald-based confidence intervals.

Description

Usage

Arguments

Value

Examples

Create cohort life table

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Formula wrapper for lifetab from the KMsurv package.

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Linear combination of regression parameters.

Description

Usage

Arguments

Details

Value

See Also

Examples

Melanoma cancer dataset

Description

Usage

Sample from the `colon` dataset used for teaching.

Smoothed hazard estimates for `coxph`

Formula wrapper for `lifetab` from the `KMsurv` package.

Formula wrapper for the `muhaz` function from the `muhaz` package.

Utility functions for the `biostat3` package

Convert a `Date` vector to a numeric vector