# neardate

##### Find the index of the closest value in data set 2, for each entry in data set one.

A common task in medical work is to find the closest lab value to some index date, for each subject.

##### Usage

```
neardate(id1, id2, y1, y2, best = c("after", "prior"),
nomatch = NA_integer_)
```

##### Arguments

- id1
vector of subject identifiers for the index group

- id2
vector of identifiers for the reference group

- y1
normally a vector of dates for the index group, but any orderable data type is allowed

- y2
reference set of dates

- best
if

`best='prior'`

find the index of the first y2 value less than or equal to the target y1 value, for each subject. If`best='after'`

find the first y2 value which is greater than or equal to the target y1 value, for each subject.- nomatch
the value to return for items without a match

##### Details

This routine is closely related to `match`

and to
`findInterval`

, the first of which finds exact matches and
the second closest matches. This finds the closest matching date
within sets of exactly matching identifiers.
Closest date matching is often needed in clinical studies. For
example data set 1 might contain the subject identifier and the date
of some procedure and data set set 2 has the dates and values for
laboratory tests, and the query is to find the first
test value after the intervention but no closer than 7 days.

The `id1`

and `id2`

arguments are similar to `match`

in
that we are searching for instances of `id1`

that will be found
in `id2`

, and the result is the same length as `id1`

.
However, instead of returning the first match with `id2`

this
routine returns the one that best matches with respect to `y1`

.

The `y1`

and `y2`

arguments need not be dates, the function
works for any data type such that the expression
`c(y1, y2)`

gives a sensible, sortable result.
Be careful about matching Date and DateTime values and the impact of
time zones, however, see `as.POSIXct`

.
If `y1`

and `y2`

are not of the same class the user is
on their own.
Since there exist pairs of unmatched data types where the result could
be sensible, the routine will in this case proceed under the assumption
that "the user knows what they are doing". Caveat emptor.

##### Value

the index of the matching observations in the second data set, or
the `nomatch`

value for no successful match

##### See Also

##### Examples

```
# NOT RUN {
data1 <- data.frame(id = 1:10,
entry.dt = as.Date(paste("2011", 1:10, "5", sep='-')))
temp1 <- c(1,4,5,1,3,6,9, 2,7,8,12,4,6,7,10,12,3)
data2 <- data.frame(id = c(1,1,1,2,2,4,4,5,5,5,6,8,8,9,10,10,12),
lab.dt = as.Date(paste("2011", temp1, "1", sep='-')),
chol = round(runif(17, 130, 280)))
#first cholesterol on or after enrollment
indx1 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt)
data2[indx1, "chol"]
# Closest one, either before or after.
#
indx2 <- neardate(data1$id, data2$id, data1$entry.dt, data2$lab.dt,
best="prior")
ifelse(is.na(indx1), indx2, # none after, take before
ifelse(is.na(indx2), indx1, #none before
ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) <
abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1)))
# closest date before or after, but no more than 21 days prior to index
indx2 <- ifelse((data1$entry.dt - data2$lab.dt[indx2]) >21, NA, indx2)
ifelse(is.na(indx1), indx2, # none after, take before
ifelse(is.na(indx2), indx1, #none before
ifelse(abs(data2$lab.dt[indx2]- data1$entry.dt) <
abs(data2$lab.dt[indx1]- data1$entry.dt), indx2, indx1)))
# }
```

*Documentation reproduced from package survival, version 3.1-12, License: LGPL (>= 2)*