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$date, episode_days = 60) # indices
#> [1] 14 30 51 29 53 28 49 36 9 7 29 9 43 30 20 51 5 2 5 9 27 6 21 25 14
#> [26] 20 29 22 42 51 56 39 31 47 1 10 56 7 54 13 39 20 44 10 37 45 26 42 53 3
#> [51] 2 7 13 51 48 52 31 9 38 50 54 15 41 53 38 23 16 9 26 12 5 26 4 6 31
#> [76] 7 40 27 55 57 22 52 27 36 26 16 10 48 23 36 49 47 6 6 56 22 51 48 44 24
#> [101] 58 33 47 49 58 32 8 6 21 3 14 33 57 43 14 50 6 3 53 6 18 15 52 31 22
#> [126] 7 47 6 42 39 45 1 49 23 53 13 18 35 8 57 12 11 17 3 55 16 12 54 55 14
#> [151] 9 46 51 55 19 7 36 8 57 4 14 45 45 59 38 55 25 23 60 57 39 38 54 16 31
#> [176] 3 16 39 50 37 40 14 53 60 6 34 32 14 3 30 52 24 23 1 59 52 11 41 7 26
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [25] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE
#> [37] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
#> [49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [73] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [85] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE
#> [109] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
#> [121] TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [133] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE
#> [145] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
#> [157] TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
#> [169] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [181] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE
#> [193] TRUE TRUE 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: 6 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2002-11-07 430011 82 F Clinical B_STPHY_CONS R NA R R
#> 2 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R
#> 3 2002-11-14 058917 76 F ICU B_STPHY_HMNS R NA S NA
#> 4 2002-10-18 E55128 57 F ICU B_STPHY_AURS R NA S R
#> 5 2002-11-18 956065 89 F Clinical B_ESCHR_COLI R NA NA NA
#> 6 2002-10-14 FCC668 54 F ICU B_ACNTB R NA NA NA
#> # … 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 277241 2005-08-31 A FALSE
#> 2 A86006 2009-08-09 C FALSE
#> 3 16DC39 2015-11-19 A FALSE
#> 4 F68330 2009-04-09 A FALSE
#> 5 A79917 2016-05-21 B FALSE
#> 6 A80D37 2009-01-19 A FALSE
#> 7 335263 2015-04-30 B FALSE
#> 8 257844 2011-05-22 B FALSE
#> 9 B8F499 2004-08-22 B FALSE
#> 10 F35553 2004-02-28 A FALSE
#> # … 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 2005-08-31 277241 1 TRUE
#> 2 Clinical 2009-08-09 A86006 1 TRUE
#> 3 ICU 2015-11-19 16DC39 1 TRUE
#> 4 Clinical 2009-04-09 F68330 1 TRUE
#> 5 Clinical 2016-05-21 A79917 1 TRUE
#> 6 Clinical 2009-01-19 A80D37 1 TRUE
#> 7 Clinical 2015-04-30 335263 1 TRUE
#> 8 Clinical 2011-05-22 257844 1 TRUE
#> 9 Clinical 2004-08-22 B8F499 1 TRUE
#> 10 ICU 2004-02-28 F35553 2 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 114 15 53 73
#> 2 ICU 62 12 38 46
#> 3 Outpatient 7 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 [189]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 277241 B_STPHY_AURS ICU TRUE
#> 2 A86006 B_STPHY_CONS Clinical TRUE
#> 3 16DC39 B_STPHY_HMLY ICU TRUE
#> 4 F68330 B_STRPT_PNMN Clinical TRUE
#> 5 A79917 B_ENTRC_FACM Clinical TRUE
#> 6 A80D37 B_ESCHR_COLI Clinical TRUE
#> 7 335263 B_ENTRBC_CLOC Clinical TRUE
#> 8 257844 B_STPHY_CONS Clinical TRUE
#> 9 B8F499 B_STPHY_CONS Clinical TRUE
#> 10 F35553 B_STPHY_AURS ICU TRUE
#> # … with 190 more rows
# }