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$date, episode_days = 60) # indices
#> [1] 45 29 45 57 14 40 18 59 22 11 17 11 1 44 60 54 15 38 11 21 17 55 46 57 34
#> [26] 53 5 57 3 28 45 20 15 38 16 60 20 25 24 28 63 10 4 9 19 17 56 34 19 59
#> [51] 8 38 62 51 62 7 36 57 14 46 54 58 44 37 59 57 7 6 46 9 10 8 60 54 17
#> [76] 21 5 49 50 8 9 39 8 52 20 30 3 14 54 13 36 47 2 59 18 56 18 17 41 61
#> [101] 58 22 53 26 43 29 53 42 25 53 61 37 2 1 55 62 61 32 27 39 12 7 62 10 30
#> [126] 55 9 18 25 41 7 11 2 58 31 48 56 5 58 5 61 63 20 59 34 15 44 55 60 37
#> [151] 5 15 27 14 33 25 38 55 55 60 7 48 2 32 54 12 54 22 51 42 52 36 31 56 45
#> [176] 52 11 37 42 1 4 3 43 42 16 52 25 49 52 17 5 23 31 20 21 26 35 45 49 28
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
#> [13] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37] FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
#> [49] TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
#> [61] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE
#> [73] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
#> [85] TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
#> [97] TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
#> [109] TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [121] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [133] FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
#> [145] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
#> [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
#> [169] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
#> [181] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
#> [193] FALSE FALSE TRUE TRUE 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> <rsi> <rsi> <rsi> <rsi>
#> 1 2002-07-30 218912 76 F ICU B_ESCHR_COLI R NA NA NA
#> 2 2002-06-22 FD8039 75 F ICU B_ESCHR_COLI R NA NA NA
#> 3 2002-07-15 C42193 84 M ICU B_STPHY_HMNS R NA R R
#> # … 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 255339 2013-07-22 C FALSE
#> 2 942999 2009-02-17 C FALSE
#> 3 963927 2013-07-04 B FALSE
#> 4 7D5503 2016-05-30 B TRUE
#> 5 990867 2005-08-16 B FALSE
#> 6 C04744 2012-04-10 B FALSE
#> 7 14D826 2006-09-14 A FALSE
#> 8 B77047 2016-12-03 C FALSE
#> 9 005088 2007-08-22 A FALSE
#> 10 D65308 2004-11-03 C 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 [184]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <dbl> <lgl>
#> 1 Clinical 2013-07-22 255339 1 TRUE
#> 2 Clinical 2009-02-17 942999 1 TRUE
#> 3 Clinical 2013-07-04 963927 1 TRUE
#> 4 Clinical 2016-05-30 7D5503 1 TRUE
#> 5 Clinical 2005-08-16 990867 1 TRUE
#> 6 Clinical 2012-04-10 C04744 1 TRUE
#> 7 Clinical 2006-09-14 14D826 1 TRUE
#> 8 ICU 2016-12-03 B77047 1 TRUE
#> 9 Clinical 2007-08-22 005088 1 TRUE
#> 10 ICU 2004-11-03 D65308 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 120 14 54 74
#> 2 ICU 54 13 37 46
#> 3 Outpatient 10 7 10 10
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 [194]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 255339 B_ESCHR_COLI Clinical TRUE
#> 2 942999 B_STRPT_PNMN Clinical TRUE
#> 3 963927 B_STRPT_PNMN Clinical TRUE
#> 4 7D5503 B_STPHY_AURS Clinical TRUE
#> 5 990867 B_STPHY_AURS Clinical TRUE
#> 6 C04744 B_STRPT_EQNS Clinical TRUE
#> 7 14D826 B_STPHY_CONS Clinical TRUE
#> 8 B77047 B_STPHY_AURS ICU TRUE
#> 9 005088 B_SLMNL_ENTR Clinical TRUE
#> 10 D65308 B_STPHY_EPDR ICU TRUE
#> # … with 190 more rows
# }