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$date, episode_days = 60) # indices
#> [1] 9 29 29 17 30 21 4 56 31 35 18 50 62 7 28 28 57 37 53 46 15 23 14 39 42
#> [26] 46 20 62 29 54 18 54 1 61 3 12 6 8 9 43 24 1 61 37 11 42 28 36 12 8
#> [51] 26 61 55 60 4 19 30 7 20 9 13 57 9 59 47 8 17 1 64 8 2 4 60 7 34
#> [76] 61 44 20 61 55 7 2 32 33 18 6 18 11 7 10 18 5 3 44 23 54 31 18 4 19
#> [101] 25 17 7 41 43 17 55 21 30 61 11 16 14 57 52 41 40 23 43 60 37 26 12 12 4
#> [126] 10 6 40 11 2 45 30 51 47 29 25 58 10 61 19 8 39 1 24 25 58 10 18 34 20
#> [151] 63 43 57 51 60 41 36 64 4 61 55 22 19 27 53 64 51 15 55 23 22 36 45 57 48
#> [176] 40 44 49 57 38 40 29 32 2 11 50 38 53 10 33 8 9 54 4 6 58 57 3 14 60
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
#> [13] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE
#> [25] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
#> [49] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [61] TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [73] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [85] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [109] FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [121] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
#> [145] TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [157] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE
#> [169] TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
#> [181] TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [193] TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
# filter on results from the third 60-day episode only, using base R
df[which(get_episode(df$date, 60) == 3), ]
#> # A tibble: 3 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2002-08-28 390178 57 M Clinical B_STRPT_SLVR S NA NA S
#> 2 2002-08-14 785317 51 F ICU B_ESCHR_COLI R NA NA NA
#> 3 2002-07-24 F35553 51 M ICU B_STPHY_AURS R NA S R
#> # … with 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>,
#> # FEP <sir>, CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>,
#> # GEN <sir>, TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>,
#> # NIT <sir>, FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>,
#> # TEC <sir>, TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>,
#> # AZM <sir>, IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>,
#> # MUP <sir>, RIF <sir>
# 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 822116 2004-02-24 C FALSE
#> 2 144549 2009-09-01 C FALSE
#> 3 982666 2009-09-06 A FALSE
#> 4 5DF436 2006-01-04 B TRUE
#> 5 023456 2009-11-02 A FALSE
#> 6 725063 2007-06-07 A FALSE
#> 7 FCC668 2002-10-14 B FALSE
#> 8 D28985 2015-12-21 C FALSE
#> 9 B26404 2010-01-02 A FALSE
#> 10 F83217 2010-12-21 B 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 [183]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <dbl> <lgl>
#> 1 ICU 2004-02-24 822116 1 TRUE
#> 2 Clinical 2009-09-01 144549 1 TRUE
#> 3 Clinical 2009-09-06 982666 1 TRUE
#> 4 ICU 2006-01-04 5DF436 1 TRUE
#> 5 Clinical 2009-11-02 023456 1 TRUE
#> 6 ICU 2007-06-07 725063 1 TRUE
#> 7 ICU 2002-10-14 FCC668 1 TRUE
#> 8 Outpatient 2015-12-21 D28985 1 TRUE
#> 9 Clinical 2010-01-02 B26404 1 TRUE
#> 10 ICU 2010-12-21 F83217 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 118 14 51 70
#> 2 ICU 59 13 37 48
#> 3 Outpatient 6 5 6 6
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 [192]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 822116 B_STPHY_AURS ICU TRUE
#> 2 144549 B_ENTRBC_CLOC Clinical TRUE
#> 3 982666 B_STRPT_DYSG Clinical TRUE
#> 4 5DF436 B_STPHY_AURS ICU TRUE
#> 5 023456 B_KLBSL_PNMN Clinical TRUE
#> 6 725063 B_STPHY_CONS ICU TRUE
#> 7 FCC668 B_ACNTB ICU TRUE
#> 8 D28985 B_ESCHR_COLI Outpatient TRUE
#> 9 B26404 B_STPHY_CONS Clinical TRUE
#> 10 F83217 B_STPHY_CONS ICU TRUE
#> # … with 190 more rows
# }