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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(...)).

Usage

get_episode(x, episode_days, ...)

is_new_episode(x, episode_days, ...)

Arguments

x

vector of dates (class Date or POSIXt), 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

Value

Details

The functions get_episode() and is_new_episode() differ in this way when setting episode_days to 365:

person_iddateget_episode()is_new_episode()
A2019-01-011TRUE
A2019-03-011FALSE
A2021-01-012TRUE
B2008-01-011TRUE
B2008-01-011FALSE
C2020-01-011TRUE

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().

See also

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$date, episode_days = 60) # indices
#>   [1] 35 25 44 41 29 17 16  4 49 30 12 28 37 49 43 47 11 44 35  3  1 19 11 18 41
#>  [26] 45 23  1  3 33  5 26 48 49  2 48 10 39 14 10 28  8  4 27  9 42 18 16 47 47
#>  [51]  7 24  8  9 36 31  7 34 18 39 32 29 10 23  2 13 44 22 15 40 47 17 38 48 15
#>  [76] 16 21 21 21 20 15 41  6 49 24 31  6  7 30  4 20 46 19 32  1 39 48  3 45 27
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [13]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE
#>  [25] FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#>  [37]  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#>  [49] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#>  [61]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE
#>  [73]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#>  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [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: 3 × 46
#>   date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
#>   <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
#> 1 2002-11-16 762305     87 F      Clinical B_PROTS_MRBL R     NA    NA    NA   
#> 2 2003-01-08 783073     83 M      Clinical B_STPHY_CONS R     NA    S     NA   
#> 3 2002-11-18 956065     89 F      Clinical B_ESCHR_COLI R     NA    NA    NA   
#> # … 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 [96]
#>    patient date       condition new_episode
#>    <chr>   <date>     <chr>     <lgl>      
#>  1 023456  2002-02-05 C         TRUE       
#>  2 023456  2011-04-25 C         TRUE       
#>  3 032343  2003-06-09 A         TRUE       
#>  4 047634  2004-06-28 A         TRUE       
#>  5 067927  2002-01-13 B         TRUE       
#>  6 078381  2014-07-17 A         TRUE       
#>  7 0C0688  2014-09-05 B         TRUE       
#>  8 0DBF93  2015-12-03 B         TRUE       
#>  9 0E2483  2007-08-10 A         TRUE       
#> 10 0E2483  2008-07-22 A         FALSE      
#> # … 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 [96]
#>    ward     date       patient new_index new_logical
#>    <chr>    <date>     <chr>       <int> <lgl>      
#>  1 Clinical 2002-02-05 023456          2 TRUE       
#>  2 Clinical 2011-04-25 023456          1 TRUE       
#>  3 Clinical 2003-06-09 032343          1 TRUE       
#>  4 Clinical 2004-06-28 047634          1 TRUE       
#>  5 ICU      2002-01-13 067927          1 TRUE       
#>  6 ICU      2014-07-17 078381          1 TRUE       
#>  7 Clinical 2014-09-05 0C0688          1 TRUE       
#>  8 ICU      2015-12-03 0DBF93          1 TRUE       
#>  9 Clinical 2008-07-22 0E2483          1 TRUE       
#> 10 ICU      2007-08-10 0E2483          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           61             14            40            47
#> 2 ICU                31             10            24            26
#> 3 Outpatient          4              4             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 [100]
#>    patient mo           ward     flag_episode
#>    <chr>   <mo>         <chr>    <lgl>       
#>  1 187841  B_STPHY_CONS Clinical TRUE        
#>  2 AB0003  B_ESCHR_COLI Clinical TRUE        
#>  3 0DBF93  B_STPHY_AURS ICU      TRUE        
#>  4 0C0688  B_ESCHR_COLI Clinical TRUE        
#>  5 B61944  B_STPHY_CONS ICU      TRUE        
#>  6 E31163  B_STPHY_CONS ICU      TRUE        
#>  7 868305  B_STPHY_AURS Clinical TRUE        
#>  8 032343  B_STPHY_CONS Clinical TRUE        
#>  9 C13048  B_ESCHR_COLI Clinical TRUE        
#> 10 023456  B_PROTS_MRBL Clinical TRUE        
#> # … with 90 more rows
# }