lexisTwo: lexisTwo
In tagteam/heaven: Data Preparation Routines for Medical Registry Data
lexisTwo R Documentation
lexisTwo
Description
Splitting is about collecting person specific exposure-outcome-confounder
pattern over time in start-stop-event format.
lexixTwo is one of 3 splitting functions in heaven.
lexixTwo is useful to add the time-dynamic information about comorbidities
and other events in binary (yes/no) format
to an existing data set which readily contains person specific information
in start-stop-event format. A person specific time interval (start-stop) of
the existing data set is split according to the occurrence dates of the
comorbidities and other events whenever the comorbidity status (event status)
changes within the time interval.
The "base" data are the data to be split. They
may contain much information, but the key is "id","start","end" and "event".
These describe the participant's id, start of time interval, end of time
interval and the event of interest (must be 0/1).
The other input is a data.table with the splitting guide.
This can be supplied in two formats: wide and long
Wide format:
This requires one record pr. individual that have dates to be split on.
One column defines the same id as in the "base" table. The other columns
contain dates for each condition where the split should occur. These column
names will also appear in the output data, but on output the values are zero
before the dates and 1 after. When dates are NA output has zero.
Long format:
This requires one record per data where a possible split should occur. The
columns should contain id, name of condition and the data to split on
Usage
lexisTwo(indat,splitdat,invars,splitvars,format="wide",datacheck=TRUE)
Arguments
indat
A data.table or data.frame whose first 4 columns are in that
order:
id Person identification variable such as PNR. The data may contain
multiple lines per subject.
start Start of time interval. Either a date or an integer/numeric.
end End of time interval. Either in date format or given as
numeric/integer.
event Binary 0-1 variable indicating if an event occurred at end of
interval
splitdat
The splitting guide. A data.table which contains person
specific information about the onset dates of comorbidities and other events.
Wide format:
id Person identification variable such as PNR. The data may contain
multiple lines per subject.
Date 1 Either a date or an integer/numeric. Format must match that of
the start and stop of arguments
indat The onset date of comorbidity 1 or other event. If
integer/numeric it can be time since a baseline date on project specific
scale (e.g., days or months).
Date 2 Either a date or an integer/numeric. The onset date of
comorbidity 2 or other event. If integer/numeric it can be time since a
baseline date on project specific scale (e.g., days or months).
Dat3 ....
Long format:
#'
id Person identification variable such as PNR. The data may contain
multiple lines per subject.
Condition nameCharacter providing the variable name of condition
Date Either a date or an integer/numeric. The onset date of
comorbidity or other event. If integer/numeric it can be time since a
baseline date on project specific scale (e.g., days or months).
invars
vector of column names for id/entry/exit/event - in that
order, example: c("id","start","end","event")
splitvars
For wide format:
- vector of column names of columns containing dates to
split by. example: c("date1","date2","date3","date4")
For long format:
- vector of the 3 columns in the data.table: id/name/date,
example: c("id","name","date")
The name of the id column must be the same in both datasets
format
- format of splitting guide - "wide" or "long"
datacheck
- This program may crash if intervals are overlapping or
negative. Datachecking produces an error in such cases. This can be omitted
if the data have been checked by other means. For the splitting guide
this options checks that there is only one entry for each variable to
split by for each person identifier.
Details
The program checks that intervals are not negative. Violation results in
an error. Overlap may occur in real data, but the user needs to make
decisions regarding this prior to using this function.
It is required that the splitting guide contains at least one record.
Missing data in the person id variables are not allowed and will cause errors.
A note of caution: This function works with dates as numeric. R has a default
original of dates as 1 January 1970, but other programs have different
default origins - and this includes SAS and Excel. It is therefor important
for decent results that care is taken that all dates are defined similarly.
The output will always have the "next" period starting on the day where the
last period ended. This is to ensure that period lengths are calculated pro-
perly. The program will also allow periods of zero lengths which is a conse-
quence when multiple splits are made on the same day. When there is an event
on a period with zero length it is important to keep that period not to
loose events for calculations. Whether other zero length records should be
kept in calculations depends on the context.
Value
The function returns a new data table where records have been split according
to the splittingguide dataset. Variables unrelated to the splitting are
left unchanged. The names of columns from "splitvars" are also in output
data, but now they have the value zero before the dates and 1 after.
Author(s)
Christian Torp-Pedersen
See Also
lexisSeq lexisFromTo
Examples
library(data.table)
dat <- data.table(pnr=c("123456","123456","234567","234567","345678","345678"
,"456789","456789"),
start=as.Date(c(0,100,0,100,0,100,0,100),origin="1970-01-01"),
end=as.Date(c(100,200,100,200,100,200,100,200),origin="1970-01-01"),
event=as.integer(c(0,1,0,0,0,1,0,1)))
split <- data.table (pnr=c("123456","234567","345678","456789"),
como1.onset=as.integer(c(0,NA,49,50)), como2.onset=as.integer(c(25,75,49,49)),
como3.onset=as.integer(c(30,NA,49,48)), como4.onset=as.integer(c(50,49,49,47)))
#Show the datasets:
dat[]
split[]
lexisTwo(dat # inddato with id/in/out/event
,split # Data with id and dates
,c("pnr","start","end","event") #names of id/in/out/event - in that order
,c("pnr","como1.onset","como2.onset","como3.onset","como4.onset"))
#Names of date-vars to split by
# And with splittingguide in long format
splitvars <- c("como1.onset","como2.onset","como3.onset","como4.onset")
split <- data.table::melt(data=split,id.vars="pnr",measure.vars=splitvars,
variable.name="name",value.name="value")
split[,value:=as.Date(value,origin="1970-01-01")]
split[]
split <- split[!is.na(value)] # remove missing values
lexisTwo(dat # in-data with id/in/out/event
,split # Data with id/name/date
,c("pnr","start","end","event") #names of id/in/out/event - in that order
,c("pnr","name","value")
,format="long")
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| lexisTwo | R Documentation |
lexisTwo
Description
Splitting is about collecting person specific exposure-outcome-confounder pattern over time in start-stop-event format. lexixTwo is one of 3 splitting functions in heaven. lexixTwo is useful to add the time-dynamic information about comorbidities and other events in binary (yes/no) format to an existing data set which readily contains person specific information in start-stop-event format. A person specific time interval (start-stop) of the existing data set is split according to the occurrence dates of the comorbidities and other events whenever the comorbidity status (event status) changes within the time interval.
The "base" data are the data to be split. They may contain much information, but the key is "id","start","end" and "event". These describe the participant's id, start of time interval, end of time interval and the event of interest (must be 0/1).
The other input is a data.table with the splitting guide. This can be supplied in two formats: wide and long Wide format: This requires one record pr. individual that have dates to be split on. One column defines the same id as in the "base" table. The other columns contain dates for each condition where the split should occur. These column names will also appear in the output data, but on output the values are zero before the dates and 1 after. When dates are NA output has zero.
Long format: This requires one record per data where a possible split should occur. The columns should contain id, name of condition and the data to split on
Usage
lexisTwo(indat,splitdat,invars,splitvars,format="wide",datacheck=TRUE)
Arguments
indat |
A data.table or data.frame whose first 4 columns are in that order:
|
splitdat |
The splitting guide. A data.table which contains person specific information about the onset dates of comorbidities and other events. Wide format:
Long format: #'
|
invars |
vector of column names for id/entry/exit/event - in that order, example: c("id","start","end","event") |
splitvars |
For wide format: - vector of column names of columns containing dates to split by. example: c("date1","date2","date3","date4") For long format: - vector of the 3 columns in the data.table: id/name/date, example: c("id","name","date") The name of the id column must be the same in both datasets |
format |
- format of splitting guide - "wide" or "long" |
datacheck |
- This program may crash if intervals are overlapping or negative. Datachecking produces an error in such cases. This can be omitted if the data have been checked by other means. For the splitting guide this options checks that there is only one entry for each variable to split by for each person identifier. |
Details
The program checks that intervals are not negative. Violation results in an error. Overlap may occur in real data, but the user needs to make decisions regarding this prior to using this function.
It is required that the splitting guide contains at least one record. Missing data in the person id variables are not allowed and will cause errors.
A note of caution: This function works with dates as numeric. R has a default original of dates as 1 January 1970, but other programs have different default origins - and this includes SAS and Excel. It is therefor important for decent results that care is taken that all dates are defined similarly.
The output will always have the "next" period starting on the day where the last period ended. This is to ensure that period lengths are calculated pro- perly. The program will also allow periods of zero lengths which is a conse- quence when multiple splits are made on the same day. When there is an event on a period with zero length it is important to keep that period not to loose events for calculations. Whether other zero length records should be kept in calculations depends on the context.
Value
The function returns a new data table where records have been split according to the splittingguide dataset. Variables unrelated to the splitting are left unchanged. The names of columns from "splitvars" are also in output data, but now they have the value zero before the dates and 1 after.
Author(s)
Christian Torp-Pedersen
See Also
lexisSeq lexisFromTo
Examples
library(data.table)
dat <- data.table(pnr=c("123456","123456","234567","234567","345678","345678"
,"456789","456789"),
start=as.Date(c(0,100,0,100,0,100,0,100),origin="1970-01-01"),
end=as.Date(c(100,200,100,200,100,200,100,200),origin="1970-01-01"),
event=as.integer(c(0,1,0,0,0,1,0,1)))
split <- data.table (pnr=c("123456","234567","345678","456789"),
como1.onset=as.integer(c(0,NA,49,50)), como2.onset=as.integer(c(25,75,49,49)),
como3.onset=as.integer(c(30,NA,49,48)), como4.onset=as.integer(c(50,49,49,47)))
#Show the datasets:
dat[]
split[]
lexisTwo(dat # inddato with id/in/out/event
,split # Data with id and dates
,c("pnr","start","end","event") #names of id/in/out/event - in that order
,c("pnr","como1.onset","como2.onset","como3.onset","como4.onset"))
#Names of date-vars to split by
# And with splittingguide in long format
splitvars <- c("como1.onset","como2.onset","como3.onset","como4.onset")
split <- data.table::melt(data=split,id.vars="pnr",measure.vars=splitvars,
variable.name="name",value.name="value")
split[,value:=as.Date(value,origin="1970-01-01")]
split[]
split <- split[!is.na(value)] # remove missing values
lexisTwo(dat # in-data with id/in/out/event
,split # Data with id/name/date
,c("pnr","start","end","event") #names of id/in/out/event - in that order
,c("pnr","name","value")
,format="long")
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