addDrug.Lexis: Expand a Lexis object with information of drug exposure based on purchase dates and -amounts

Description

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.

In some circumstances the result is a Lexis object with a very large number of very small follow-up intervals. The function coarse.Lexis combines consecutive follow-up intervals using the covariates from the first of the intervals.

Usage

# S3 method for Lexis
addDrug(Lx,  # Lexis object
            pdat,  # list of data frames with drug purchase information
             amt = "amt", # name of the variable with purchased amount
             dpt = "dpt", # name of the variable with amount consumed per time
             apt = NULL,  # old name for dpt
          method = "ext", # method use to compute exposure 
            maxt = NULL,  # max duration for a purchase when using "fix"
           grace = 0,     # grace  period to be added
            tnam = setdiff(names(pdat[[1]]), c("lex.id", amt))[1],
                          # name of the time variable from Lx
          prefix = TRUE,  # should drug names prefix variable names 
          sepfix = ".",   # what should the separator be when forming prefix/suffix
         verbose = TRUE,
           ...)
coarse.Lexis(Lx, lim, keep = FALSE)

Value

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 each drug coverage. Further, for each drug (i.e. the data frame in the pdat

list) the name of the pdat component determines the prefix for the 4 variables that will be added. Supposing this is AA for a given drug, then 4 new variables will be:

AA.ex: logical; is the person exposed in this interval
AA.tf: numeric: time since first purchase, same units as lex.dur
AA.ct: numeric: cumulative time on the drug, same units as lex.dur
AA.cd: numeric: cumulative dose of the drug, same units as amt

So if pdat is a list of length 3 with names c("a","b","c")

the function will add variables a.ex, a.tf, a.ct, a.cd, b.ex, b.tf, b.ct, b.cd, c.ex, c.tf, c.ct, c.cd

Arguments

Lx: A Lexis object.
pdat: Named list of data frames with drug purchase data.
amt: Name of the variable in the data frames in pdat with the purchased amount.
dpt: Name of the variable in the data frames in pdat with the consumed dose per time. Must be given in units of units of amt per units of lex.dur in Lx.
apt: Name previously used for dpt. Will disappear in next version.
method: Character. One of "ext" (default), "dos" or "fix", for a description, see details.
maxt: Numerical. Maximal duration for a purchase when using method="fix", same units as lex.dur.
grace: Numeric. Grace period to be added after last time of computed drug coverage to define end of exposure, same units as lex.dur.
tnam: Character. Name of the timescale used in the data frames in pdat.
prefix: Logical. Should the names of pdat be used as prefix for the 4 generated exposure variables for each drug. If false the names of pdat will be used as suffix.
sepfix: Character, used to separate the prefix and the name of the generated type of variable.
verbose: Logical. Should the function tell you about the choices you made?
lim: Numeric vector of length 2. Consecutive follow-up intervals are combined if the first has lex.dur < lim[1], and the sum of lex.dur in the two intervals is smaller than lim[2]. If a scalar i given, c(lim,3*lim) is used.
keep: Logical of length 1 or nrow(Lx) that points to records that cannot be combined with preceding records.
...: Arguments passed on. Ignored.

Author

Bendix Carstensen, http://bendixcarstensen.com

Details

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 a variable dpt.

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 subsequent 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/dpt 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 period 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.

Examples

Run this code

# Follow-up of 2 persons
clear()
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))

# drug purchases, one data frame for each drug 
ra <- data.frame(per = c(2007 + runif(12,0,10)),
                 amt = sample(2:4, 12, r = TRUE),
              lex.id = sample(1:2, 12, r = TRUE))
ra <- ra[order(ra$lex.id, ra$per),]

rb <- 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))
rb <- rb[order(rb$lex.id, rb$per),]

# put in a named list
pdat <- list(A = ra, B = rb)
pdat

ex1 <- addDrug.Lexis(Sx, pdat, method = "ext") # default
summary(ex1)
# collapsing some of the smaller intervals with the next
summary(coarse.Lexis(ex1, c(0.2,0.5)))

ex2 <- addDrug.Lexis(Sx, pdat, method = "ext", grace = 0.2)
dos <- addDrug.Lexis(Sx, pdat, method = "dos", dpt = 6)
fix <- addDrug.Lexis(Sx, pdat, method = "fix", maxt = 1)

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