Expand a Lexis object with information of drug exposure based on purchase dates and -amounts
A Lexis
object will contain information on follow-up for a
cohort of persons through time, each record containing information of
one time interval, including the time at the beginning of each
interval. If information on drug purchase is known for the persons via
lex.id
in a list of data frames, addDrug.Lexis
will expand
the Lexis
object by cutting at all drug purchase dates, and
compute the exposure status for any number of drugs, and add these as
variables.
addDrug.Lexis(Lx, # Lexis object pdat, # list of data frames with drug purchase information amt = "amt", # name of the variable with purchased amount apt = "apt", # name of the variable with amount consumed per time method = "ext", # method use to compute exposure maxt = NULL, # max duration for a purchase when using \code{method="fix"} grace = 0, # grace period to be added tnam = setdiff(names(pdat[[1]]), c("lex.id", amt))[1], # name of the prefix = NULL, # character vector of length \code{length(pdat)} suffix = NULL, # character vector of length \code{length(pdat)} sepfix = ".") # what should the separator be when forming prefix/suffix
Lx |
A |
pdat |
Named list of data frames with drug |
amt |
Name of the variable in the data frames in |
apt |
Name of the variable in the data frames in |
method |
Character. One of |
maxt |
Numerical. Maximal duration for a purchase when using
|
grace |
Numeric. Grace period to be added after last time of computed drug
coverage to define end of exposure, same units as |
tnam |
Character. Name of the timescale used in the data frames in |
prefix |
Character vector of length |
suffix |
Character vector of length |
sepfix |
Character, used to separate the |
This function internally uses addCov.Lexis
to attach
exposure status for several drugs (dispensed medicine) to follow-up in a
Lexis
object. Once that is done, the exposure measures are
calculated at each time.
There is one input data frame per type of drug, each with variables
lex.id
, amt
, a timescale variable and possibly apt
.
Three different methods for computing drug exposures from dates and
amounts of purchases are supported via the argument method
.
"ext"
: Extrapolation: the first drug purchase is assumed
consumed over the interval to the second purchase. Exposure
for next purchases are assumed to last as long as it would have if
consumed at a speed corresponding to the previous purchase being
consumed over the time span between the previous and current purchase,
plus a period of length grace
.
"dos"
: Dosage: assumes that each purchase lasts
amt
/apt
plus grace
.
"fix"
: Fixed time: assumes that each purchase lasts
maxt
.
So for each purchase we have defined an end of coverage (expiry date). If next purchase is before this, we assume that the amount purchased is consumed over the period between the two purchases, otherwise over the perido to the end of coverage. So the only difference between the methods is the determination of the coverage for each purchase.
Based on this, for each date in the resulting Lexis
four
exposure variables are computed, see next section.
A Lexis
object with the same risk time, states and events
as Lx
. The follow-up for each person has been cut at the purchase
times of each of the drugs, as well as at the expiry times for drug
coverage. Further for each drug (i.e. data frame in the pdat
list) 4 variables have been added:
pre.ex
: logical; is the person exposed in this interval
pre.tf
: numeric: time since first purchase, same units as
lex.dur
pre.ct
: numeric: cumulative time on the drug, same units
as lex.dur
pre.cd
: numeric: cumulative dose of the drug, same units
as amt
Bendix Carstensen, http://bendixcarstensen.com
# Follow-up of 2 persons fu <- data.frame(doe = c(2006, 2008), dox = c(2015, 2018), dob = c(1950, 1951), xst = factor(c("A","D"))) Lx <- Lexis(entry = list(per = doe, age = doe- dob), exit = list(per = dox), exit.status = xst, data = fu) Lx <- subset(Lx, select = -c(doe, dob, dox, xst)) # split FU in 1 year intervals Sx <- splitLexis(Lx, "per", breaks = seq(1990, 2020, 1.0)) rc <- data.frame(per = c(2007 + runif(12,0,10)), amt = sample(2:4, 12, r = TRUE), lex.id = sample(1:2, 12, r = TRUE)) rc <- rc[order(rc$lex.id, rc$per),] rd <- data.frame(per = c(2009 + runif(10, 0, 10)), amt = sample(round(2:4/3,1), 10, r = TRUE), lex.id = sample(1:2, 10, r = TRUE)) rd <- rd[order(rd$lex.id, rd$per),] pdat <- list(Joe = rc, Pat = rd) pdat addDrug.Lexis(Sx, pdat, method = "ext") # default addDrug.Lexis(Sx, pdat, method = "ext", grace = 0.2) addDrug.Lexis(Sx, pdat, method = "dos", apt = 6) addDrug.Lexis(Sx, pdat, method = "fix", maxt = 1)
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