mcutLexis: Cut follow-up at multiple event dates and keep track of order of events

Description

A generalization of cutLexis to the case where different events may occur in any order (but at most once for each).

Usage

mcutLexis( L0, timescale = 1, wh,
              new.states = NULL,
        precursor.states = transient(L0),
              seq.states = TRUE,
              new.scales = NULL,
            ties.resolve = FALSE )

Value

A Lexis object with extra states created by occurrence of a number of intermediate events.

Arguments

L0

A Lexis object.

timescale

Which time scale do the variables in L0[,wh] refer to. Can be character or integer.

wh

Which variables contain the event dates. Character or integer vector

new.states

Names of the events forming new states. If NULL equal to the variable names from wh.

precursor.states

Which states are precursor states. See cutLexis for definition of precursor states.

seq.states

Should the sequence of events be kept track of? That is, should A-B be considered different from B-A. If FALSE, the state with combined preceding events A and B will be called A+B (alphabetically sorted).

May also be supplied as character: s - sequence, keep track of sequence of states occupied (same as TRUE), u - unordered, keep track only of states visited (same as FALSE) or l, c - last or current state, only record the latest state visited. If given as character, only the first letter converted to lower case is used.

new.scales

Should we construct new time scales indicating the time since each of the event occurrences.

ties.resolve

Should tied event times be resolved by adding random noise to tied event dates. If FALSE the function will not accept that two events occur at the same time for a person (ties). If TRUE a random quantity in the range c(-1,1)/100 will be added to all event times in all records with at least one tie. If ties.resolve is numeric a random quantity in the range c(-1,1)*ties.resolve will be added to all event times in all records with at least one tie.

Author

Bendix Carstensen, http://bendixcarstensen.com

Examples

Run this code

# A dataframe of times
set.seed(563248)
dd <- data.frame( id = 1:30,
                 doN = round(runif(30,-30, 0),1),
                 doE = round(runif(30,  0,20),1),
                 doX = round(runif(30, 50,60),1),
                 doD = round(runif(30, 50,60),1),
                 # these are the event times
                 doA = c(NA,21,NA,27,35,NA,52, 5,43,80,
                         NA,22,56,28,53,NA,51, 5,43,80,
                         NA,23,NA,33,51,NA,55, 5,43,80),
                 doB = c(NA,20,NA,53,27,NA, 5,52,34,83,
                         NA,20,23,37,35,NA,52, 8,33,NA,
                         25,NA,37,40,NA,NA,15,23,36,61) )

# set up a Lexis object with time from entry to death/exit
Lx <- Lexis( entry = list(time=doE,
                           age=doE-doN),
              exit = list(time=pmin(doX,doD)),
       exit.status = factor(doD