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$date, episode_days = 60) # indices
#> [1] 6 3 12 3 12 9 27 30 21 7 43 27 2 25 14 5 15 6 37 29 18 40 17 13 8
#> [26] 20 25 39 41 10 8 45 29 1 29 8 12 30 26 1 31 13 25 10 4 25 40 24 21 39
#> [51] 45 28 31 39 38 36 11 6 36 47 18 28 25 5 11 5 33 35 23 22 2 47 9 32 3
#> [76] 26 44 8 34 38 39 16 6 14 18 29 41 19 33 44 30 36 15 31 12 46 42 24 16 45
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
#> [13] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
#> [25] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
#> [37] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
#> [49] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE
#> [61] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
#> [73] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
#> [85] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [97] 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: 3 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2002-12-02 374815 77 F Clinical B_STPHY_CONS S NA S NA
#> 2 2002-12-28 E26415 70 M ICU B_STPHY_CONS S NA S NA
#> 3 2003-01-06 894506 83 M ICU B_STRPT_PNMN S NA NA 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 [100]
#> patient date condition new_episode
#> <chr> <date> <chr> <lgl>
#> 1 011307 2011-09-20 A TRUE
#> 2 047634 2004-06-28 A TRUE
#> 3 077922 2009-08-24 A TRUE
#> 4 092034 2006-06-12 A TRUE
#> 5 0DBB93 2003-10-01 A TRUE
#> 6 0DBF93 2015-12-03 C TRUE
#> 7 0E2483 2007-05-29 B TRUE
#> 8 105248 2005-06-16 B TRUE
#> 9 122506 2007-08-11 A TRUE
#> 10 141061 2014-10-22 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 [97]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <int> <lgl>
#> 1 Clinical 2011-09-20 011307 1 TRUE
#> 2 Clinical 2004-06-28 047634 1 TRUE
#> 3 Clinical 2009-08-24 077922 1 TRUE
#> 4 ICU 2006-06-12 092034 1 TRUE
#> 5 Clinical 2003-10-01 0DBB93 1 TRUE
#> 6 ICU 2015-12-03 0DBF93 1 TRUE
#> 7 Clinical 2007-05-29 0E2483 1 TRUE
#> 8 Clinical 2005-06-16 105248 1 TRUE
#> 9 Clinical 2007-08-11 122506 1 TRUE
#> 10 Clinical 2014-10-22 141061 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 66 13 39 47
#> 2 ICU 27 9 22 25
#> 3 Outpatient 4 3 4 4
# 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] TRUE
# 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 B70881 B_KLBSL_OXYT Clinical TRUE
#> 2 374815 B_STPHY_CONS Clinical TRUE
#> 3 578848 B_STPHY_CONS Clinical TRUE
#> 4 E26415 B_STPHY_CONS ICU TRUE
#> 5 54890C B_ESCHR_COLI Clinical TRUE
#> 6 827322 B_KLBSL_OXYT Clinical TRUE
#> 7 704554 B_STPHY_CONS ICU TRUE
#> 8 207804 B_STRPT_PNMN Clinical TRUE
#> 9 F54287 B_STRPT_ANGN Clinical TRUE
#> 10 869231 B_KLBSL_PNMN Clinical TRUE
#> # … with 90 more rows
# }