From records of drug purchase and possibly known treatment intensity, the time since first drug use and cumulative dose at prespecified times is computed. Optionally, lagged exposures are computed too, i.e. cumulative exposure a prespecified time ago.

1 2 3 4 5 6 7 8 |

`purchase` |
Data frame with columns |

`id` |
Character. Name of the id variable in the data frame. |

`dop` |
Character. Name of the date of purchase variable in the data frame. |

`amt` |
Character. Name of the amount purchased variable in the data frame. |

`dpt` |
Character. Name of the dose-per-time variable in the data frame. |

`fu` |
Data frame with follow-up period for each person, the person
id variable must have the same name as in the |

`doe` |
Character. Name of the date of entry variable. |

`dox` |
Character. Name of the date of exit variable. |

`use.dpt` |
Logical: should we use information on dose per time. |

`breaks` |
Numerical vector of dates at which the time since first exposure, cumulative dose etc. are computed. |

`lags` |
Numerical vector of lag-times used in computing lagged cumulative doses. |

`push.max` |
Numerical. How much can purchases maximally be pushed forward in time. See details. |

`pred.win` |
The length of the window used for constructing the average dose per time used to compute the duration of the last purchase |

`lag.dec` |
How many decimals to use in the construction of names for the lagged exposure variables |

Each purchase record is converted into a time-interval of exposure.

If `use.dpt`

is `TRUE`

then the dose per time information is
used to compute the exposure interval associated with each purchase.
Exposure intervals are stacked, that is each interval is put after any
previous. This means that the start of exposure to a given purchase
can be pushed into the future. The parameter `push.max`

indicates
the maximally tolerated push. If this is reached by a person, the
assumption is that some of the purchased drug is not counted in the
exposure calculations.

The `dpt`

can either be a constant, basically translating the
purchased amount into exposure time the same way for all persons, or
it can be a vector with different treatment intensities for each
purchase. In any case the cumulative dose is computed taking this
into account.

If `use.dpt`

is `FALSE`

then the exposure from one purchase
is assumed to stretch over the time to the next purchase, so we are
effectively assuming different rates of dose per time between any two
adjacent purchases. Moreover, with this approach, periods of
non-exposure does not exist. Formally this approach conditions on the
future, because the rate of consumption (the accumulation of
cumulative exposure) is computed based on knowledge of when next
purchase is made.

The intention of this function is to generate covariates for a particular drug for the entire follow-up of each person. The reason that the follow-up prior to drug purchase and post-exposure is included is that the covariates must be defined for these periods too, in order to be useful for analysis of disease outcomes.

This function is described in terme of calendar time as underlying
time scale, because this will normally be the time scale for drug
purchases and for entry and exit for persons. In principle the
variables termed as dates might equally well refer to say the age
scale, but this would then have to be true *both* for the
purchase data and the follow-up data.

A data frame with one record per follow-up interval between
`breaks`

, with columns:

`id`

person id.

`dof`

date of follow up, i.e. start of interval. Apart from possibly the first interval for each person, this will assume values in the set of the values in

`breaks`

.`Y`

the length of interval.

`tfi`

time from first initiation of drug.

`tfc`

time from latest cessation of drug.

`cdur`

cumulative time on the drug.

`cdos`

cumulative dose.

`ldos`

suffixed with one value per element in

`lags`

, the latter giving the cumulative doses`lags`

before`dof`

.

Bendix Carstensen, bxc@steno.dk. The development of this function was supported partly through a grant from the EFSD (European Foundation for the Study of Diabetes), ""

`Lexis`

, `splitLexis`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ```
# Construct a simple data frame of purchases for 3 persons
# The purchase units (in variable dose) correspond to
n <- c( 10, 17, 8 )
dop <- c( 1995.2+cumsum(sample(1:4/10,n[1],replace=TRUE)),
1997.3+cumsum(sample(1:4/10,n[2],replace=TRUE)),
1997.3+cumsum(sample(1:4/10,n[3],replace=TRUE)) )
amt <- sample( 1:3/15, sum(n), replace=TRUE )
dpt <- sample( 15:20/25, sum(n), replace=TRUE )
dfr <- data.frame( id = rep(1:3,n),
dop,
amt = amt,
dpt = dpt )
round( dfr, 3 )
# Construct a simple dataframe for follow-up periods for these 3 persons
fu <- data.frame( id = 1:3,
doe = c(1995,1997,1996)+1:3/4,
dox = c(2001,2003,2002)+1:3/5 )
round( fu, 3 )
( dpos <- gen.exp( dfr,
fu = fu,
breaks = seq(1990,2015,0.5),
lags = 2:3/5 ) )
( xpos <- gen.exp( dfr,
fu = fu,
use.dpt = FALSE,
breaks = seq(1990,2015,0.5),
lags = 2:3/5 ) )
# How many relevant columns
nvar <- ncol(xpos)-3
clrs <- rainbow(nvar)
# Show how the variables relate to the follow-up time
par( mfrow=c(3,1), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, bty="n" )
for( i in unique(xpos$id) )
matplot( xpos[xpos$id==i,"dof"],
xpos[xpos$id==i,-(1:3)],
xlim=range(xpos$dof), ylim=range(xpos[,-(1:3)]),
type="l", lwd=2, lty=1, col=clrs,
ylab="", xlab="Date of follow-up" )
ytxt <- par("usr")[3:4]
ytxt <- ytxt[1] + (nvar:1)*diff(ytxt)/(nvar+2)
xtxt <- rep( sum(par("usr")[1:2]*c(0.98,0.02)), nvar )
text( xtxt, ytxt, colnames(xpos)[-(1:3)], font=2,
col=clrs, cex=1.5, adj=0 )
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.