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336 lines
12 KiB
Markdown
336 lines
12 KiB
Markdown
# Determine Clinical or Epidemic Episodes
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These functions determine which items in a vector can be considered (the
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start of) a new episode. This can be used to determine clinical episodes
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for any epidemiological analysis. The `get_episode()` function returns
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the index number of the episode per group, while the `is_new_episode()`
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function returns `TRUE` for every new `get_episode()` index. Both
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absolute and relative episode determination are supported.
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## Usage
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``` r
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get_episode(x, episode_days = NULL, case_free_days = NULL, ...)
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is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)
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```
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## Arguments
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- x:
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Vector of dates (class `Date` or `POSIXt`), will be sorted internally
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to determine episodes.
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- episode_days:
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Episode length in days to specify the time period after which a new
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episode begins, can also be less than a day or `Inf`, see *Details*.
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- case_free_days:
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(inter-epidemic) interval length in days after which a new episode
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will start, can also be less than a day or `Inf`, see *Details*.
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- ...:
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Ignored, only in place to allow future extensions.
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## Value
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- `get_episode()`: an [integer](https://rdrr.io/r/base/integer.html)
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vector
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- `is_new_episode()`: a [logical](https://rdrr.io/r/base/logical.html)
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vector
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## Details
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Episodes can be determined in two ways: absolute and relative.
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1. Absolute
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This method uses `episode_days` to define an episode length in days,
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after which a new episode will start. A common use case in AMR data
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analysis is microbial epidemiology: episodes of *S. aureus*
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bacteraemia in ICU patients for example. The episode length could
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then be 30 days, so that new *S. aureus* isolates after an ICU
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episode of 30 days will be considered a different (or new) episode.
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Thus, this method counts **since the start of the previous
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episode**.
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2. Relative
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This method uses `case_free_days` to quantify the duration of
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case-free days (the inter-epidemic interval), after which a new
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episode will start. A common use case is infectious disease
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epidemiology: episodes of norovirus outbreaks in a hospital for
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example. The case-free period could then be 14 days, so that new
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norovirus cases after that time will be considered a different (or
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new) episode.
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Thus, this methods counts **since the last case in the previous
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episode**.
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In a table:
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|------------|--------------------------|----------------------------|
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| Date | Using `episode_days = 7` | Using `case_free_days = 7` |
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| 2023-01-01 | 1 | 1 |
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| 2023-01-02 | 1 | 1 |
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| 2023-01-05 | 1 | 1 |
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| 2023-01-08 | 2\*\* | 1 |
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| 2023-02-21 | 3 | 2\*\*\* |
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| 2023-02-22 | 3 | 2 |
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| 2023-02-23 | 3 | 2 |
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| 2023-02-24 | 3 | 2 |
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| 2023-03-01 | 4 | 2 |
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\*\* This marks the start of a new episode, because 8 January 2023 is
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more than 7 days since the start of the previous episode (1 January
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2023).
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\*\*\* This marks the start of a new episode, because 21 January 2023 is
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more than 7 days since the last case in the previous episode (8 January
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2023).
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Either `episode_days` or `case_free_days` must be provided in the
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function.
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### Difference between `get_episode()` and `is_new_episode()`
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The `get_episode()` function returns the index number of the episode, so
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all cases/patients/isolates in the first episode will have the number 1,
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all cases/patients/isolates in the second episode will have the number
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2, etc.
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The `is_new_episode()` function on the other hand, returns `TRUE` for
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every new `get_episode()` index.
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To specify, when setting `episode_days = 365` (using method 1 as
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explained above), this is how the two functions differ:
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| | | | |
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|---------|------------|-----------------|--------------------|
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| patient | date | `get_episode()` | `is_new_episode()` |
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| A | 2019-01-01 | 1 | TRUE |
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| A | 2019-03-01 | 1 | FALSE |
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| A | 2021-01-01 | 2 | TRUE |
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| B | 2008-01-01 | 1 | TRUE |
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| B | 2008-01-01 | 1 | FALSE |
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| C | 2020-01-01 | 1 | TRUE |
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### Other
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The
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[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
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function is a wrapper around the `is_new_episode()` function, but is
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more efficient for data sets containing microorganism codes or names and
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allows for different isolate selection methods.
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The `dplyr` package is not required for these functions to work, but
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these episode functions do support [variable
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grouping](https://dplyr.tidyverse.org/reference/group_by.html) and work
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conveniently inside `dplyr` verbs such as
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[`filter()`](https://dplyr.tidyverse.org/reference/filter.html),
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[`mutate()`](https://dplyr.tidyverse.org/reference/mutate.html) and
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[`summarise()`](https://dplyr.tidyverse.org/reference/summarise.html).
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## See also
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[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
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## Examples
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``` r
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# difference between absolute and relative determination of episodes:
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x <- data.frame(dates = as.Date(c(
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"2021-01-01",
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"2021-01-02",
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"2021-01-05",
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"2021-01-08",
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"2021-02-21",
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"2021-02-22",
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"2021-02-23",
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"2021-02-24",
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"2021-03-01",
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"2021-03-01"
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)))
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x$absolute <- get_episode(x$dates, episode_days = 7)
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x$relative <- get_episode(x$dates, case_free_days = 7)
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x
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#> dates absolute relative
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#> 1 2021-01-01 1 1
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#> 2 2021-01-02 1 1
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#> 3 2021-01-05 1 1
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#> 4 2021-01-08 2 1
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#> 5 2021-02-21 3 2
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#> 6 2021-02-22 3 2
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#> 7 2021-02-23 3 2
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#> 8 2021-02-24 3 2
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#> 9 2021-03-01 4 2
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#> 10 2021-03-01 4 2
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# `example_isolates` is a data set available in the AMR package.
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# See ?example_isolates
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df <- example_isolates[sample(seq_len(2000), size = 100), ]
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get_episode(df$date, episode_days = 60) # indices
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#> [1] 28 47 7 7 6 17 9 4 37 11 43 43 14 38 26 38 12 39 49 18 15 27 5 22 25
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#> [26] 36 11 18 22 41 42 38 33 35 18 45 11 30 40 31 46 19 24 18 17 16 43 46 1 23
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#> [51] 2 18 34 45 21 3 45 12 48 30 10 13 29 40 48 30 2 20 9 19 14 36 19 32 36
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#> [76] 10 44 20 4 4 36 8 48 43 46 9 32 6 8 29 13 6 45 9 12 38 45 44 35 5
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is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
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#> [1] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
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#> [13] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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#> [25] TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
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#> [37] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE
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#> [49] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE
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#> [61] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
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#> [73] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
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#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [97] FALSE FALSE FALSE FALSE
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# filter on results from the third 60-day episode only, using base R
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df[which(get_episode(df$date, 60) == 3), ]
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#> # A tibble: 1 × 46
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#> date patient age gender ward mo PEN OXA FLC AMX
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#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
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#> 1 2002-06-07 710157 76 M Outpatie… B_STPHY_CONS S NA S NA
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#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
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#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
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#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
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#> # FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
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#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
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#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>
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# the functions also work for less than a day, e.g. to include one per hour:
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get_episode(
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c(
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Sys.time(),
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Sys.time() + 60 * 60
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),
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episode_days = 1 / 24
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)
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#> [1] 1 2
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# \donttest{
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if (require("dplyr")) {
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# is_new_episode() can also be used in dplyr verbs to determine patient
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# episodes based on any (combination of) grouping variables:
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df %>%
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mutate(condition = sample(
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x = c("A", "B", "C"),
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size = 100,
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replace = TRUE
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)) %>%
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group_by(patient, condition) %>%
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mutate(new_episode = is_new_episode(date, 365)) %>%
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select(patient, date, condition, new_episode) %>%
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arrange(patient, condition, date)
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}
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#> # A tibble: 100 × 4
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#> # Groups: patient, condition [95]
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#> patient date condition new_episode
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#> <chr> <date> <chr> <lgl>
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#> 1 005088 2017-09-28 B TRUE
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#> 2 010257 2004-04-03 C TRUE
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#> 3 080086 2007-10-26 A TRUE
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#> 4 083080 2012-04-16 B TRUE
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#> 5 0E2483 2008-07-22 B TRUE
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#> 6 141061 2014-10-22 B TRUE
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#> 7 16DC39 2015-11-19 A TRUE
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#> 8 204562 2010-07-03 B TRUE
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#> 9 22B987 2009-10-19 C TRUE
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#> 10 257844 2011-05-22 C TRUE
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#> # ℹ 90 more rows
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if (require("dplyr")) {
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df %>%
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group_by(ward, patient) %>%
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transmute(date,
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patient,
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new_index = get_episode(date, 60),
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new_logical = is_new_episode(date, 60)
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) %>%
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arrange(patient, ward, date)
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}
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#> # A tibble: 100 × 5
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#> # Groups: ward, patient [91]
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#> ward date patient new_index new_logical
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#> <chr> <date> <chr> <int> <lgl>
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#> 1 Clinical 2017-09-28 005088 1 TRUE
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#> 2 Clinical 2004-04-03 010257 1 TRUE
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#> 3 Clinical 2007-10-26 080086 1 TRUE
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#> 4 Clinical 2012-04-16 083080 1 TRUE
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#> 5 Clinical 2008-07-22 0E2483 1 TRUE
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#> 6 Clinical 2014-10-22 141061 1 TRUE
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#> 7 ICU 2015-11-19 16DC39 1 TRUE
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#> 8 Outpatient 2010-07-03 204562 1 TRUE
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#> 9 Clinical 2009-10-19 22B987 1 TRUE
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#> 10 Clinical 2011-05-22 257844 1 TRUE
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#> # ℹ 90 more rows
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if (require("dplyr")) {
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df %>%
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group_by(ward) %>%
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summarise(
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n_patients = n_distinct(patient),
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n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
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n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),
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n_episodes_30 = sum(is_new_episode(date, episode_days = 30))
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)
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}
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#> # A tibble: 3 × 5
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#> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30
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#> <chr> <int> <int> <int> <int>
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#> 1 Clinical 60 13 39 49
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#> 2 ICU 26 11 23 24
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#> 3 Outpatient 5 5 5 5
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# grouping on patients and microorganisms leads to the same
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# results as first_isolate() when using 'episode-based':
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if (require("dplyr")) {
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x <- df %>%
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filter_first_isolate(
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include_unknown = TRUE,
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method = "episode-based"
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)
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y <- df %>%
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group_by(patient, mo) %>%
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filter(is_new_episode(date, 365)) %>%
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ungroup()
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identical(x, y)
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}
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#> [1] FALSE
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# but is_new_episode() has a lot more flexibility than first_isolate(),
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# since you can now group on anything that seems relevant:
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if (require("dplyr")) {
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df %>%
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group_by(patient, mo, ward) %>%
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mutate(flag_episode = is_new_episode(date, 365)) %>%
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select(group_vars(.), flag_episode)
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}
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#> # A tibble: 100 × 4
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#> # Groups: patient, mo, ward [94]
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#> patient mo ward flag_episode
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#> <chr> <mo> <chr> <lgl>
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#> 1 D91230 B_STPHY_EPDR Clinical TRUE
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#> 2 F5F794 B_STPHY_AURS ICU TRUE
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#> 3 419655 B_STPHY_EPDR Clinical TRUE
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#> 4 010257 B_ESCHR_COLI Clinical TRUE
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#> 5 E60130 B_KLBSL_OXYT Clinical TRUE
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#> 6 693505 B_STPHY_AURS ICU TRUE
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#> 7 E1C9D4 B_STPHY_CONS Clinical TRUE
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#> 8 762305 B_PROTS_MRBL Clinical TRUE
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#> 9 545388 B_STPHY_HMNS Clinical TRUE
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#> 10 E02001 B_ESCHR_COLI Clinical TRUE
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#> # ℹ 90 more rows
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# }
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```
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