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$date, episode_days = 60) # indices
#> [1] 10 6 35 25 25 48 41 23 28 27 51 26 47 12 1 52 2 16 34 47 57 66 62 34 41
#> [26] 27 12 12 59 49 15 13 51 14 14 5 13 10 66 34 65 26 44 51 5 27 50 42 58 45
#> [51] 9 53 51 65 24 8 13 50 1 40 31 7 62 32 40 28 19 55 34 36 24 7 28 60 31
#> [76] 10 12 41 43 58 64 57 60 8 9 47 39 30 52 4 67 61 55 13 37 4 57 32 44 67
#> [101] 27 10 8 56 15 6 66 67 54 38 34 17 3 64 49 18 50 65 40 60 3 49 10 17 21
#> [126] 12 12 51 4 41 60 12 63 18 9 45 11 47 21 37 65 61 65 57 5 58 57 12 28 17
#> [151] 58 25 66 60 24 8 29 58 39 30 60 45 23 50 12 38 55 63 20 43 63 2 36 31 11
#> [176] 7 10 16 61 33 20 18 44 35 26 40 43 14 22 33 42 28 46 40 39 2 42 32 7 12
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE
#> [37] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE
#> [49] FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [61] TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [73] TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
#> [85] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [97] FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
#> [109] TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [121] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [133] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE
#> [145] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
#> [157] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [169] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [181] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [193] TRUE FALSE FALSE FALSE TRUE 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 2002-07-30 218912 76 F ICU B_ESCHR_COLI R NA NA NA
#> 2 2002-07-15 C42193 84 M ICU B_STPHY_HMNS R NA R R
#> # … 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 189363 2004-06-22 C FALSE
#> 2 F35553 2003-09-05 C FALSE
#> 3 2E6911 2010-06-05 A FALSE
#> 4 451990 2008-03-22 A FALSE
#> 5 192790 2008-02-15 C FALSE
#> 6 B43936 2013-05-16 C FALSE
#> 7 5B78D5 2011-09-19 C FALSE
#> 8 179451 2007-09-15 A TRUE
#> 9 329273 2008-10-27 A FALSE
#> 10 B12441 2008-09-21 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 [183]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <dbl> <lgl>
#> 1 Clinical 2004-06-22 189363 2 TRUE
#> 2 ICU 2003-09-05 F35553 2 TRUE
#> 3 Clinical 2010-06-05 2E6911 1 TRUE
#> 4 Clinical 2008-03-22 451990 1 TRUE
#> 5 Outpatient 2008-02-15 192790 1 TRUE
#> 6 ICU 2013-05-16 B43936 1 TRUE
#> 7 Clinical 2011-09-19 5B78D5 1 TRUE
#> 8 ICU 2007-09-15 179451 1 TRUE
#> 9 Clinical 2008-10-27 329273 1 TRUE
#> 10 Clinical 2008-09-21 B12441 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 117 15 54 78
#> 2 ICU 56 12 36 43
#> 3 Outpatient 10 6 9 9
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 189363 B_STPHY_AURS Clinical FALSE
#> 2 F35553 B_ENTRC ICU TRUE
#> 3 2E6911 B_ESCHR_COLI Clinical TRUE
#> 4 451990 B_ESCHR_COLI Clinical TRUE
#> 5 192790 B_STPHY_CONS Outpatient TRUE
#> 6 B43936 B_BCTRD_FRGL ICU TRUE
#> 7 5B78D5 B_STPHY_AURS Clinical TRUE
#> 8 179451 B_ESCHR_COLI ICU TRUE
#> 9 329273 B_STRPT_PNMN Clinical TRUE
#> 10 B12441 B_STPHY_CONS Clinical TRUE
#> # … with 190 more rows
# }