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$date, episode_days = 60) # indices
#> [1] 56 28 13 12 36 4 6 15 40 7 48 3 31 55 4 54 20 43 1 32 38 34 9 36 53
#> [26] 16 54 27 44 54 62 3 8 36 1 19 6 9 54 27 4 18 47 9 13 31 11 32 16 50
#> [51] 48 7 37 36 24 37 36 10 40 52 51 21 34 47 59 27 56 23 59 58 59 34 4 11 38
#> [76] 3 57 61 15 32 3 13 12 44 26 62 62 22 60 21 62 43 47 49 59 55 13 8 46 8
#> [101] 5 10 55 28 12 20 26 16 56 24 34 13 39 40 41 50 35 50 11 56 51 22 19 54 4
#> [126] 37 56 32 4 13 45 9 30 38 27 13 20 41 35 43 55 59 59 17 15 40 23 20 25 8
#> [151] 22 44 1 14 13 52 32 60 13 14 29 33 55 17 46 51 57 28 17 30 1 43 31 26 38
#> [176] 2 17 13 56 51 34 10 31 58 2 56 46 58 1 9 59 27 42 33 61 26 20 58 19 33
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [13] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [25] TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
#> [37] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
#> [49] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [61] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [85] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [97] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
#> [109] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [121] FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE
#> [133] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [145] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
#> [157] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
#> [169] FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
#> [181] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
#> [193] TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
# filter on results from the third 60-day episode only, using base R
df[which(get_episode(df$date, 60) == 3), ]
#> # A tibble: 4 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <rsi> <rsi> <rsi> <rsi>
#> 1 2002-07-21 955940 82 F Clinical B_PSDMN_AERG R NA NA R
#> 2 2002-06-04 082413 78 M ICU B_STRPT_PNMN S NA NA S
#> 3 2002-07-28 F54261 69 M Clinical B_STPHY_CONS R NA S NA
#> 4 2002-06-05 24D393 20 F 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 784436 2016-05-28 B TRUE
#> 2 533225 2008-10-01 C FALSE
#> 3 B95551 2004-12-17 B FALSE
#> 4 D65308 2004-11-03 A FALSE
#> 5 BF4515 2010-12-29 A FALSE
#> 6 F35553 2002-10-20 A FALSE
#> 7 D97054 2003-04-13 C FALSE
#> 8 452212 2005-04-22 C TRUE
#> 9 083080 2012-04-16 A FALSE
#> 10 914520 2003-07-31 B 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 [173]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <dbl> <lgl>
#> 1 ICU 2016-05-28 784436 1 TRUE
#> 2 Clinical 2008-10-01 533225 1 TRUE
#> 3 Outpatient 2004-12-17 B95551 1 TRUE
#> 4 ICU 2004-11-03 D65308 1 TRUE
#> 5 Clinical 2010-12-29 BF4515 1 TRUE
#> 6 ICU 2002-10-20 F35553 1 TRUE
#> 7 Clinical 2003-04-13 D97054 1 TRUE
#> 8 ICU 2005-04-22 452212 1 TRUE
#> 9 Clinical 2012-04-16 083080 1 TRUE
#> 10 Clinical 2003-07-31 914520 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 107 14 49 69
#> 2 ICU 55 13 34 44
#> 3 Outpatient 11 7 11 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 [186]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 784436 B_STPHY_HMNS ICU TRUE
#> 2 533225 B_ESCHR_COLI Clinical TRUE
#> 3 B95551 B_STPHY_CONS Outpatient TRUE
#> 4 D65308 B_STPHY_EPDR ICU TRUE
#> 5 BF4515 B_ESCHR_COLI Clinical TRUE
#> 6 F35553 B_STPHY_AURS ICU TRUE
#> 7 D97054 B_STPHY_AURS Clinical TRUE
#> 8 452212 B_ENTRC ICU TRUE
#> 9 083080 B_ESCHR_COLI Clinical TRUE
#> 10 914520 B_STPHY_AURS Clinical TRUE
#> # … with 190 more rows
# }