These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE to indicate whether an item in a vector is the start of a new episode.
Arguments
- x
vector of dates (class
DateorPOSIXt), will be sorted internally to determine episodes- episode_days
required episode length in days, can also be less than a day or
Inf, see Details- ...
ignored, only in place to allow future extensions
Details
Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least episode_days days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least episode_days days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored.
The first_isolate() function is a wrapper around the is_new_episode() function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods.
The dplyr package is not required for these functions to work, but these functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise().
Examples
# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates
df <- example_isolates[sample(seq_len(2000), size = 200), ]
get_episode(df$date, episode_days = 60) # indices
#> [1] 49 11 47 52 11 50 54 53 64 23 65 64 4 33 34 43 52 35 16 4 9 27 38 10 37
#> [26] 34 39 43 60 8 7 12 47 22 19 22 19 16 6 8 58 4 17 15 21 4 55 3 7 2
#> [51] 65 45 66 40 62 50 22 18 26 38 17 32 27 49 35 36 30 47 34 24 58 61 36 45 17
#> [76] 48 23 34 28 55 21 53 13 59 30 23 17 60 1 29 48 26 21 8 1 61 29 43 16 29
#> [101] 31 31 12 15 46 31 13 35 39 14 8 64 52 36 60 32 4 48 28 33 30 45 10 25 17
#> [126] 25 42 8 61 60 54 2 20 38 18 63 3 63 10 14 5 26 52 15 57 65 30 22 64 50
#> [151] 23 61 41 64 58 36 20 15 44 51 57 20 27 41 26 37 29 37 47 18 18 30 28 62 13
#> [176] 38 43 20 31 21 18 35 15 48 14 39 26 44 49 56 64 30 34 22 46 20 1 18 49 12
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
#> [13] FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [25] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [37] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [49] TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
#> [61] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
#> [85] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE
#> [97] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
#> [109] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
#> [121] FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
#> [133] FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE
#> [145] TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
#> [157] FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
#> [169] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [181] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE
#> [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# filter on results from the third 60-day episode only, using base R
df[which(get_episode(df$date, 60) == 3), ]
#> # A tibble: 2 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <rsi> <rsi> <rsi> <rsi>
#> 1 2002-08-19 A49852 70 M Clinical B_ESCHR_COLI R NA NA NA
#> 2 2002-07-30 218912 76 F ICU B_ESCHR_COLI R NA NA NA
#> # … with 36 more variables: AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>,
#> # FEP <rsi>, CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>,
#> # GEN <rsi>, TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>,
#> # NIT <rsi>, FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>,
#> # TEC <rsi>, TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>,
#> # AZM <rsi>, IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>,
#> # MUP <rsi>, RIF <rsi>
# the functions also work for less than a day, e.g. to include one per hour:
get_episode(c(
Sys.time(),
Sys.time() + 60 * 60
),
episode_days = 1 / 24
)
#> [1] 1 2
# \donttest{
if (require("dplyr")) {
# is_new_episode() can also be used in dplyr verbs to determine patient
# episodes based on any (combination of) grouping variables:
df %>%
mutate(condition = sample(
x = c("A", "B", "C"),
size = 200,
replace = TRUE
)) %>%
group_by(condition) %>%
mutate(new_episode = is_new_episode(date, 365)) %>%
select(patient, date, condition, new_episode)
}
#> # A tibble: 200 × 4
#> # Groups: condition [3]
#> patient date condition new_episode
#> <chr> <date> <chr> <lgl>
#> 1 245525 2013-11-02 A FALSE
#> 2 765860 2004-04-07 A FALSE
#> 3 702351 2013-01-26 B FALSE
#> 4 188588 2014-08-19 B FALSE
#> 5 28F906 2004-04-05 C FALSE
#> 6 62B634 2014-01-16 B FALSE
#> 7 B61087 2015-03-17 C FALSE
#> 8 4F9406 2015-01-23 C FALSE
#> 9 D39422 2017-05-17 C FALSE
#> 10 5B78D5 2007-01-01 A TRUE
#> # … with 190 more rows
if (require("dplyr")) {
df %>%
group_by(ward, patient) %>%
transmute(date,
patient,
new_index = get_episode(date, 60),
new_logical = is_new_episode(date, 60)
)
}
#> # A tibble: 200 × 5
#> # Groups: ward, patient [182]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <dbl> <lgl>
#> 1 ICU 2013-11-02 245525 1 TRUE
#> 2 Clinical 2004-04-07 765860 1 TRUE
#> 3 ICU 2013-01-26 702351 1 TRUE
#> 4 Clinical 2014-08-19 188588 1 TRUE
#> 5 ICU 2004-04-05 28F906 1 TRUE
#> 6 Clinical 2014-01-16 62B634 1 TRUE
#> 7 ICU 2015-03-17 B61087 1 TRUE
#> 8 Clinical 2015-01-23 4F9406 1 TRUE
#> 9 Clinical 2017-05-17 D39422 1 TRUE
#> 10 Outpatient 2007-01-01 5B78D5 1 TRUE
#> # … with 190 more rows
if (require("dplyr")) {
df %>%
group_by(ward) %>%
summarise(
n_patients = n_distinct(patient),
n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),
n_episodes_30 = sum(is_new_episode(date, episode_days = 30))
)
}
#> # A tibble: 3 × 5
#> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30
#> <chr> <int> <int> <int> <int>
#> 1 Clinical 116 14 54 75
#> 2 ICU 57 12 39 46
#> 3 Outpatient 9 6 7 7
if (require("dplyr")) {
# grouping on patients and microorganisms leads to the same
# results as first_isolate() when using 'episode-based':
x <- df %>%
filter_first_isolate(
include_unknown = TRUE,
method = "episode-based"
)
y <- df %>%
group_by(patient, mo) %>%
filter(is_new_episode(date, 365)) %>%
ungroup()
identical(x, y)
}
#> Including isolates from ICU.
#> [1] FALSE
if (require("dplyr")) {
# but is_new_episode() has a lot more flexibility than first_isolate(),
# since you can now group on anything that seems relevant:
df %>%
group_by(patient, mo, ward) %>%
mutate(flag_episode = is_new_episode(date, 365)) %>%
select(group_vars(.), flag_episode)
}
#> # A tibble: 200 × 4
#> # Groups: patient, mo, ward [190]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 245525 B_PROTS_MRBL ICU TRUE
#> 2 765860 B_STRPT_GRPA Clinical TRUE
#> 3 702351 B_ESCHR_COLI ICU TRUE
#> 4 188588 B_CTRBC_KOSR Clinical TRUE
#> 5 28F906 B_STPHY_CONS ICU TRUE
#> 6 62B634 B_STRPT_PNMN Clinical TRUE
#> 7 B61087 B_STPHY_EPDR ICU TRUE
#> 8 4F9406 B_KLBSL_PNMN Clinical TRUE
#> 9 D39422 B_KLBSL_PNMN Clinical TRUE
#> 10 5B78D5 B_STPHY_AURS Outpatient TRUE
#> # … with 190 more rows
# }