Skip to contents

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.

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

  • get_episode(): a double vector

  • is_new_episode(): a logical vector

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().

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 = 200), ]

get_episode(df$date, episode_days = 60) # indices
#>   [1] 39 28 17 35 24 12 22 42 33 12 15 14 62 30 28 37 35 21 57 31 56  7 64 24 30
#>  [26] 57 32 31 62 38 64 22 19 16 55 19  1 53 21 64 34 41 15 23  4  8 47  4 32 10
#>  [51] 56 17  5 23  1 59 56 16 63 35 11 22 58 48 10 59 57 14 18 44 36  5 20 50 37
#>  [76]  7 30 59 55  5 47 59 64 41  1  6 24 24 25  3 49 51 32 49 21 52 50 27  5 13
#> [101] 45  9 17  1 12 28 31 26  3 64 44 34 32 25 62 65 42 59  2 34 21 59 20  6 15
#> [126] 14 29 19 39 30  7 45 16 34 31 18 35 32 20 17  7 33 31  4 35  9 25 27 35 60
#> [151] 14 41 40 61 58 25 64 53  9 46 26 51 65 61 35 61 18 47 52 48  6  1 13 58 44
#> [176] 53 60 54 65 16 11 14 52 40 53 64 67 17 22 43 39  1 55 32 32 23 17 65 66 64
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#>   [1]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE
#>  [13] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [25] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [37] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [49] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
#>  [61] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
#>  [73]  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE
#>  [85] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>  [97]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [109] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE
#> [121] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE
#> [133] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [145] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> [157] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [169] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [181]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [193] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE

# filter on results from the third 60-day episode only, using base R
df[which(get_episode(df$date, 60) == 3), ]
#> # A tibble: 2 × 46
#>   date       patient   age gender ward  mo           PEN   OXA   FLC   AMX  
#>   <date>     <chr>   <dbl> <chr>  <chr> <mo>         <rsi> <rsi> <rsi> <rsi>
#> 1 2002-06-18 012595     30 M      ICU   B_CRYNB      I     NA    NA    NA   
#> 2 2002-06-22 FD8039     75 F      ICU   B_ESCHR_COLI R     NA    NA    NA   
#> # … 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 F15984  2011-04-01 B         FALSE      
#>  2 800264  2008-04-10 B         TRUE       
#>  3 277241  2005-08-31 B         FALSE      
#>  4 662978  2010-02-20 C         FALSE      
#>  5 0E2483  2007-04-06 B         TRUE       
#>  6 189363  2004-06-22 B         FALSE      
#>  7 E19253  2006-08-22 B         FALSE      
#>  8 582258  2012-03-12 C         TRUE       
#>  9 006827  2009-07-24 A         FALSE      
#> 10 189363  2004-06-22 C         TRUE       
#> # … 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 [185]
#>    ward       date       patient new_index new_logical
#>    <chr>      <date>     <chr>       <dbl> <lgl>      
#>  1 Outpatient 2011-04-01 F15984          1 TRUE       
#>  2 Outpatient 2008-04-10 800264          1 TRUE       
#>  3 ICU        2005-08-31 277241          1 TRUE       
#>  4 Clinical   2010-02-20 662978          1 TRUE       
#>  5 Clinical   2007-04-06 0E2483          1 TRUE       
#>  6 Clinical   2004-06-22 189363          1 TRUE       
#>  7 Clinical   2006-08-22 E19253          1 TRUE       
#>  8 ICU        2012-03-12 582258          1 TRUE       
#>  9 Clinical   2009-07-24 006827          1 TRUE       
#> 10 Clinical   2004-06-22 189363          1 FALSE      
#> # … 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          114             15            52            74
#> 2 ICU                57             13            36            42
#> 3 Outpatient         14              8            13            13
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 [192]
#>    patient mo           ward       flag_episode
#>    <chr>   <mo>         <chr>      <lgl>       
#>  1 F15984  B_STPHY_CONS Outpatient TRUE        
#>  2 800264  B_ESCHR_COLI Outpatient TRUE        
#>  3 277241  B_STPHY_AURS ICU        TRUE        
#>  4 662978  B_STPHY_CONS Clinical   TRUE        
#>  5 0E2483  B_ESCHR_COLI Clinical   TRUE        
#>  6 189363  B_STPHY_AURS Clinical   TRUE        
#>  7 E19253  B_STPHY_CONS Clinical   TRUE        
#>  8 582258  B_STPHY_CONS ICU        TRUE        
#>  9 006827  B_ENTRC_FCLS Clinical   TRUE        
#> 10 189363  B_STPHY_AURS Clinical   FALSE       
#> # … with 190 more rows
# }