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:	0.2.2
Built:	2024-10-26 08:24:29 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 dataset

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
stage: a factor with levels Unknown Localised Regional Distant
mmdx: a numeric vector
yydx: a numeric vector
surv_mm: a numeric vector
surv_yy: a numeric vector
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
exit: a Date
id: a numeric vector
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 used for teaching.

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
stage: a factor with levels Unknown Localised Regional Distant
mmdx: a numeric vector
yydx: a numeric vector
surv_mm: a numeric vector
surv_yy: a numeric vector
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

Diet data set

Usage

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

Format

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

id: a numeric vector
chd: a numeric vector
y: a numeric vector
hieng: a factor with levels low high
energy: a numeric vector
job: a factor with levels driver conductor bank
month: a numeric vector
height: a numeric vector
weight: a numeric vector
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, ...)
## S3 method for class 'lifetab2'
lines(x, y=NULL, ...)
lifetab2(formula, data, subset, breaks = NULL)
## S3 method for class 'lifetab2'
plot(x, y=NULL, ...)
## S3 method for class 'lifetab2'
lines(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)
`...`	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
stage: a factor with levels Unknown Localised Regional Distant
mmdx: a numeric vector
yydx: a numeric vector
surv_mm: a numeric vector
surv_yy: a numeric vector
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
exit: a Date
agegrp: a factor with levels 0-44 45-59 60-74 75+
id: a numeric vector
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