Construct a matched dataset from a match_time object

After performing time-dependent matching using the match_time function, users usually want to extract the resulting data. If the resulting data should not contain cases that did not receive the specified number of matches, this function can be called to remove them before data extraction.

Usage

get_match_data(object, remove_unmatched=TRUE, n_required=object$info$ratio)

Arguments

object: A match_time object created using the match_time function.
remove_unmatched: Either TRUE (default) or FALSE, specifying whether to remove pairs with no or not enough matches. The number of matches that each case should have received during matching can be set using the n_required argument.
n_required: A single positive integer, specifying the number of matched controls a pair needs to have to stay in the data. For example, if 1:1 matching was used and the user only wants to keep pairs where cases actually did receive one control, it should be set to 1. If ratio=3 was used and users want to keep all pairs with 2 or more matched controls, it should be set to 2 etc. By default, the number of matches specified in the ratio argument of the original match_time call is used. A warning is produced if users try to use this method when match_method in the original match_time call was set to a method in matchit that does not use pair matching.

Value

Returns a single data.table. This data.table will include the same columns as the $data object in the output of match_time, with the exception of the ".fully_matched" column. Note that if no ".id_pair" variable was created in match_time, it will also not be present in the output here.

Details

The resulting data may contain multiple rows per actual individual, depending on the arguments used in the original match_time call. This might be because individuals were included as controls and cases at different points in time, or because they were included as controls at multiple points in time (replace_over_t=TRUE), or because they were included as controls multiple times at the same point in time (replace_at_t=TRUE), or due to a mix of these reasons.

Contrary to the MatchIt package, there is no option to change this. "Replaced" individuals will always appear as often as they were selected during the matching process. The output will therefore also always contain an ".id_pair" column, indicating in which "pair" the cases and controls are. The only instance where this column is not included is when match_method is set to some method in matchit that does not create pairs internally.

Author

Robin Denz

Examples

library(data.table)
library(MatchTime)

# only execute if packages are available
if (requireNamespace("survival") & requireNamespace("MatchIt")) {

library(survival)
library(MatchIt)

# set random seed to make the output replicably
set.seed(1234)

# load "heart" data from survival package
data("heart")

# perform nearest neighbor time-dependent matching on "age" and "surgery"
# (plus exact matching on time)
m_obj <- match_time(transplant ~ age + surgery, data=heart, id="id",
                    match_method="nearest")

# get data without unmatched individuals
data <- get_match_data(m_obj)
head(data)
}
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Key: <id>
#>       id .id_new .id_pair .treat .treat_time .next_treat_time .weights event
#>    <num>   <int>   <char> <lgcl>       <num>            <num>    <num> <num>
#> 1:     1      72       36  FALSE          27               NA        1     1
#> 2:     3       1        1   TRUE           1               NA        1     1
#> 3:     4      10        5  FALSE           2               36        1     0
#> 4:     4      89       45   TRUE          36               NA        1     1
#> 5:     7      86       43  FALSE          32               51        1     0
#> 6:     8      16        8  FALSE           3               NA        1     1
#>           age      year surgery
#>         <num>     <num>   <num>
#> 1: -17.155373 0.1232033       0
#> 2:   6.297057 0.2655715       0
#> 3:  -7.737166 0.4900753       0
#> 4:  -7.737166 0.4900753       0
#> 5:   2.869268 0.7802875       0
#> 6:  -2.650240 0.8350445       0