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$date, episode_days = 60) # indices
#> [1] 53 49 6 33 7 22 60 55 14 11 43 17 48 43 60 28 15 12 27 46 4 24 22 19 6
#> [26] 48 41 21 48 40 46 26 39 54 43 35 2 14 31 13 41 61 42 1 23 49 45 40 60 16
#> [51] 13 9 48 13 2 58 38 32 55 7 28 43 1 46 44 53 57 6 20 61 30 55 34 6 5
#> [76] 25 52 29 51 8 25 45 53 45 44 4 50 8 27 22 18 26 53 17 58 16 62 46 59 55
#> [101] 28 2 48 19 9 17 62 30 20 34 29 50 42 11 46 42 6 14 37 41 10 15 59 41 53
#> [126] 6 52 1 36 63 62 14 49 47 26 4 59 35 46 18 21 35 27 53 29 11 27 36 57 6
#> [151] 18 10 27 35 24 49 13 9 46 37 54 7 60 47 47 49 55 63 12 60 61 11 59 37 7
#> [176] 55 36 20 49 3 37 40 62 2 3 8 24 59 51 9 54 56 50 45 36 38 7 30 41 22
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [25] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
#> [37] FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [49] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE
#> [61] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [73] FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE
#> [85] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
#> [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE
#> [109] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
#> [121] TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE
#> [133] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [145] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE
#> [157] FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [169] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [181] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [193] FALSE 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 2002-09-08 B8CB09 60 F Outpatie… B_STPHY_CONS S NA S NA
#> 2 2002-08-19 A49852 70 M Clinical 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 074321 2015-09-20 A FALSE
#> 2 483195 2014-12-08 B FALSE
#> 3 065187 2003-05-26 A FALSE
#> 4 317826 2010-05-26 A TRUE
#> 5 B82C75 2003-06-15 B FALSE
#> 6 579075 2007-06-01 B FALSE
#> 7 422833 2017-03-21 A FALSE
#> 8 7D5503 2016-06-01 B TRUE
#> 9 838694 2005-09-13 B FALSE
#> 10 071099 2005-01-11 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 [180]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <dbl> <lgl>
#> 1 ICU 2015-09-20 074321 1 TRUE
#> 2 Clinical 2014-12-08 483195 1 TRUE
#> 3 ICU 2003-05-26 065187 1 TRUE
#> 4 ICU 2010-05-26 317826 1 TRUE
#> 5 Clinical 2003-06-15 B82C75 1 TRUE
#> 6 Clinical 2007-06-01 579075 1 TRUE
#> 7 ICU 2017-03-21 422833 1 TRUE
#> 8 Clinical 2016-06-01 7D5503 1 TRUE
#> 9 Clinical 2005-09-13 838694 1 TRUE
#> 10 Clinical 2005-01-11 071099 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 119 15 52 79
#> 2 ICU 48 14 32 39
#> 3 Outpatient 13 7 10 11
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 [188]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 074321 B_STPHY_HMLY ICU TRUE
#> 2 483195 B_STPHY_HMNS Clinical TRUE
#> 3 065187 B_STPHY_AURS ICU TRUE
#> 4 317826 B_STPHY_CONS ICU TRUE
#> 5 B82C75 B_STPHY_CONS Clinical TRUE
#> 6 579075 B_STPHY_CONS Clinical TRUE
#> 7 422833 B_STPHY_EPDR ICU TRUE
#> 8 7D5503 B_STPHY_AURS Clinical TRUE
#> 9 838694 B_STPHY_CONS Clinical TRUE
#> 10 071099 B_STPHY_CONS Clinical TRUE
#> # … with 190 more rows
# }