https://github.com/cran/Epi
Tip revision: 1b7936858d7579a1bee0401f9e2f498a98401915 authored by Bendix Carstensen on 14 April 2017, 14:01:05 UTC
version 2.11
version 2.11
Tip revision: 1b79368
cutLexis.Rd
\name{cutLexis}
\alias{cutLexis}
\alias{countLexis}
\title{
Cut follow-up at a specified date for each person.
}
\description{
Follow-up intervals in a Lexis object are divided into two
sub-intervals: one before and one after an intermediate event. The
intermediate event may denote a change of state, in which case the
entry and exit status variables in the split Lexis object are
modified.
}
\usage{
cutLexis( data, cut, timescale = 1,
new.state = nlevels(data$lex.Cst)+1,
new.scale = FALSE,
split.states = FALSE,
progressive = FALSE,
precursor.states = NULL,
count = FALSE)
countLexis( data, cut, timescale = 1 )
}
\arguments{
\item{data}{A \code{Lexis} object.}
\item{cut}{A numeric vector with the times of the intermediate event.
If a time is missing (\code{NA}) then the event is assumed to occur
at time \code{Inf}. \code{cut} can also be a dataframe, see details.}
\item{timescale}{The timescale that \code{cut} refers to. Numeric or character.}
\item{new.state}{The state to which a transition occur at time
\code{cut}. It may be a single value, which is then applied to all
rows of \code{data}, or a vector with a separate value for each row}
\item{new.scale}{Name of the timescale defined as "time since entry to
new.state". If \code{TRUE} a name for the new scale is constructed.
See details.}
\item{split.states}{Should states that are not precursor states be split
according to whether the intermediate event has occurred.}
\item{progressive}{a logical flag that determines the changes to exit
status. See details.}
\item{precursor.states}{an optional vector of states to be considered
as "less severe" than \code{new.state}. See Details below}
\item{count}{logical indicating whether the \code{countLexis} options should
be used. Specifying \code{count=TRUE} amounts to calling \code{countLexis},
in which case the arguments \code{new.state}, \code{progressive} and
\code{precursor.states} will be ignored. }
}
\value{
A \code{Lexis} object, for which each follow-up interval
containing the cutpoint is split in two: one before
and one after the cutpoint. An extra time-scale is added; the time
since the event at \code{cut}. This is \code{NA} for any follow-up
prior to the intermediate event.
}
\note{
The \code{cutLexis} function superficially resembles the
\code{splitLexis} function. However, the \code{splitLexis} function
splits on a vector of common cut-points for all rows of the Lexis
object, whereas the \code{cutLexis} function splits on a single time
point, which may be distinct for each row, modifies the status
variables, adds a new timescale and updates the attribute
"time.since". This attribute is a character vector of the same length
as the "time.scales" attribute, whose value is '""' if the
corresponding timescale is defined for any piece of follow-up, and if
the corresponding time scale is defined by say
\code{cutLexis(obj,new.state="A",new.scale=TRUE)}, it has the value
"A".
}
\details{
The \code{cutLexis} function allows a number of different ways
of specifying the cutpoints and of modifying the status variable.
If the \code{cut} argument is a dataframe it must have columns \code{lex.id},
\code{cut} and \code{new.state}. The values of \code{lex.id} must be unique.
In this case it is assumed that each row represents a cutpoint (on the
timescale indicated in the argument \code{timescale}). This cutpoint will
be applied to all records in \code{data} with the corresponding \code{lex.id}.
This makes it possible to apply \code{cutLexis} to a split \code{Lexis} object.
If a \code{new.state} argument is supplied, the status variable is
only modified at the time of the cut point. However, it is often
useful to modify the status variable after the cutpoint when an
important event occurs. There are three distinct ways of doing this.
If the \code{progressive=TRUE} argument is given, then a "progressive"
model is assumed, in which the status can either remain the same or
increase during follow-up, but never decrease. This assumes that the
state variables \code{lex.Cst} and \code{lex.Xst} are either numeric or
ordered factors. In this case, if
\code{new.state=X}, then any exit status with a value less than
\code{X} is replaced with \code{X}. The Lexis object
must already be progressive, so that there are no rows for which the
exit status is less than the entry status. If \code{lex.Cst} and
\code{lex.Xst} are factors they must be ordered factors if
\code{progressive=TRUE} is given.
As an alternative to the \code{progressive} argument, an explicit
vector of precursor states, that are considered less severe than the
new state, may be given. If \code{new.state=X} and
\code{precursor.states=c(Y,Z)} then any exit status of \code{Y} or
\code{Z} in the second interval is replaced with \code{X} and all
other values for the exit status are retained.
The \code{countLexis} function is a variant of \code{cutLexis} when
the cutpoint marks a recurrent event, and the status variable is used
to count the number of events that have occurred. Times given in \code{cut}
represent times of new events. Splitting with
\code{countLexis} increases the status variable by 1. If the current
status is \code{X} and the exit status is \code{Y} before cutting,
then after cutting the entry status is \code{X}, \code{X+1} for
the first and second intervals, respectively, and the exit status is
\code{X+1}, \code{Y+1} respectively. Moreover the values of the status
is increased by 1 for all intervals for all intervals after the cut
for the person in question. Hence, a call to \code{countLexis} is
needed for as many times as the person with most events. But also it
is immaterial in what order the cutpoints are entered.
}
\author{Bendix Carstensen, Steno Diabetes Center, \email{b@bxc.dk},
Martyn Plummer, IARC, \email{plummer@iarc.fr}
}
\seealso{
\code{\link{splitLexis}},
\code{\link{Lexis}},
\code{\link{summary.Lexis}},
\code{\link{boxes.Lexis}}
}
\examples{
# A small artificial example
xx <- Lexis( entry=list(age=c(17,24,33,29),per=c(1920,1933,1930,1929)),
duration=c(23,57,12,15), exit.status=c(1,2,1,2) )
xx
cut <- c(33,47,29,50)
cutLexis(xx, cut, new.state=3, precursor=1)
cutLexis(xx, cut, new.state=3, precursor=2)
cutLexis(xx, cut, new.state=3, precursor=1:2)
# The same as the last example
cutLexis(xx, cut, new.state=3)
# The same example with a factor status variable
yy <- Lexis(entry = list(age=c(17,24,33,29),per=c(1920,1933,1930,1929)),
duration = c(23,57,12,15),
entry.status = factor(rep("alpha",4),
levels=c("alpha","beta","gamma")),
exit.status = factor(c("alpha","beta","alpha","beta"),
levels=c("alpha","beta","gamma")))
cutLexis(yy,c(33,47,29,50),precursor="alpha",new.state="gamma")
cutLexis(yy,c(33,47,29,50),precursor=c("alpha","beta"),new.state="aleph")
## Using a dataframe as cut argument
rl <- data.frame( lex.id=1:3, cut=c(19,53,26), timescale="age", new.state=3 )
rl
cutLexis( xx, rl )
cutLexis( xx, rl, precursor=1 )
cutLexis( xx, rl, precursor=0:2 )
## It is immaterial in what order splitting and cutting is done
xs <- splitLexis( xx, breaks=seq(0,100,10), time.scale="age" )
xs
xsC <- cutLexis(xs, rl, precursor=0 )
xC <- cutLexis( xx, rl, pre=0 )
xC
xCs <- splitLexis( xC, breaks=seq(0,100,10), time.scale="age" )
xCs
str(xCs)
}
\keyword{survival}