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 TRUE for every new get_episode() index, and is thus equal to !duplicated(get_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
The functions get_episode() and is_new_episode() differ in this way when setting episode_days to 365:
| person_id | date | get_episode() | is_new_episode() |
| A | 2019-01-01 | 1 | TRUE |
| A | 2019-03-01 | 1 | FALSE |
| A | 2021-01-01 | 2 | TRUE |
| B | 2008-01-01 | 1 | TRUE |
| B | 2008-01-01 | 1 | FALSE |
| C | 2020-01-01 | 1 | TRUE |
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 episode 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 = 100), ]
get_episode(df$date, episode_days = 60) # indices
#> [1] 41 12 5 11 39 27 43 35 7 21 18 14 45 44 26 41 9 36 34 1 14 14 31 4 38
#> [26] 5 28 40 15 27 26 34 16 46 10 33 18 9 7 8 41 3 3 22 36 42 13 12 22 15
#> [51] 11 19 21 39 20 2 9 25 3 13 37 12 29 11 2 23 31 6 28 47 29 5 32 24 25
#> [76] 3 43 30 24 36 2 1 14 18 40 6 19 9 20 11 19 15 31 12 3 17 32 24 45 38
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [13] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
#> [25] TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
#> [37] FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE
#> [49] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
#> [61] TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
#> [73] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [97] FALSE 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: 5 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2003-05-26 065187 76 F ICU B_STPHY_AURS R NA S R
#> 2 2003-04-30 6BC362 62 M ICU B_STPHY_EPDR R NA R R
#> 3 2003-06-01 CBC201 87 F Clinical B_STPHY_CONS S NA S NA
#> 4 2003-04-22 114570 74 M ICU B_STPHY_CONS R NA R R
#> 5 2003-04-08 114570 74 M ICU B_STRPT_PYGN S NA S S
#> # … 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 = 100,
replace = TRUE
)) %>%
group_by(patient, condition) %>%
mutate(new_episode = is_new_episode(date, 365)) %>%
select(patient, date, condition, new_episode) %>%
arrange(patient, condition, date)
}
#> # A tibble: 100 × 4
#> # Groups: patient, condition [97]
#> patient date condition new_episode
#> <chr> <date> <chr> <lgl>
#> 1 065133 2011-12-19 B TRUE
#> 2 065187 2003-05-26 B TRUE
#> 3 080086 2007-10-26 C TRUE
#> 4 114570 2003-04-08 A TRUE
#> 5 114570 2003-04-22 B TRUE
#> 6 126334 2009-11-26 A TRUE
#> 7 146120 2011-09-23 A TRUE
#> 8 15D386 2004-09-06 A TRUE
#> 9 161740 2005-06-21 A TRUE
#> 10 16F0F7 2010-01-17 C TRUE
#> # … with 90 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)
) %>%
arrange(patient, ward, date)
}
#> # A tibble: 100 × 5
#> # Groups: ward, patient [95]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <int> <lgl>
#> 1 Clinical 2011-12-19 065133 1 TRUE
#> 2 ICU 2003-05-26 065187 1 TRUE
#> 3 Clinical 2007-10-26 080086 1 TRUE
#> 4 ICU 2003-04-08 114570 1 FALSE
#> 5 ICU 2003-04-22 114570 1 TRUE
#> 6 Outpatient 2009-11-26 126334 1 TRUE
#> 7 Clinical 2011-09-23 146120 1 TRUE
#> 8 ICU 2004-09-06 15D386 1 TRUE
#> 9 Clinical 2005-06-21 161740 1 TRUE
#> 10 Clinical 2010-01-17 16F0F7 1 TRUE
#> # … with 90 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 60 13 33 45
#> 2 ICU 27 9 21 24
#> 3 Outpatient 8 6 7 7
# grouping on patients and microorganisms leads to the same
# results as first_isolate() when using 'episode-based':
if (require("dplyr")) {
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)
}
#> [1] FALSE
# but is_new_episode() has a lot more flexibility than first_isolate(),
# since you can now group on anything that seems relevant:
if (require("dplyr")) {
df %>%
group_by(patient, mo, ward) %>%
mutate(flag_episode = is_new_episode(date, 365)) %>%
select(group_vars(.), flag_episode)
}
#> # A tibble: 100 × 4
#> # Groups: patient, mo, ward [99]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 E52521 B_KLBSL_AERG Clinical TRUE
#> 2 6D6319 B_STPHY_CONS Outpatient TRUE
#> 3 984417 B_STPHY_AURS ICU TRUE
#> 4 161740 B_STPHY_CONS Clinical TRUE
#> 5 904640 B_PROTS_MRBL Clinical TRUE
#> 6 387C40 B_KLBSL_PNMN Clinical TRUE
#> 7 59F400 B_STRPT_PNMN ICU TRUE
#> 8 A97263 B_ESCHR_COLI Clinical TRUE
#> 9 619705 B_KLBSL_PNMN ICU TRUE
#> 10 314039 UNKNOWN Clinical TRUE
#> # … with 90 more rows
# }