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

# 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>         <sir> <sir> <sir> <sir>
#> 1 2002-08-28 390178     57 M      Clinical B_STRPT_SLVR S     NA    NA    S    
#> 2 2002-09-23 F35553     51 M      ICU      B_STPHY_AURS S     NA    S     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 = 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 533225  2008-10-01 C         FALSE      
#>  2 686445  2012-05-24 C         FALSE      
#>  3 351193  2007-01-15 A         TRUE       
#>  4 175532  2012-10-11 A         FALSE      
#>  5 F50462  2008-05-06 B         FALSE      
#>  6 1B144C  2004-05-10 B         FALSE      
#>  7 353710  2008-12-29 C         FALSE      
#>  8 450741  2002-11-21 A         FALSE      
#>  9 A76045  2015-10-06 A         FALSE      
#> 10 914520  2003-07-11 A         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   2008-10-01 533225          1 TRUE       
#>  2 Clinical   2012-05-24 686445          1 TRUE       
#>  3 ICU        2007-01-15 351193          1 TRUE       
#>  4 Clinical   2012-10-11 175532          1 TRUE       
#>  5 ICU        2008-05-06 F50462          1 TRUE       
#>  6 Outpatient 2004-05-10 1B144C          1 TRUE       
#>  7 ICU        2008-12-29 353710          1 TRUE       
#>  8 ICU        2002-11-21 450741          1 TRUE       
#>  9 ICU        2015-10-06 A76045          1 TRUE       
#> 10 ICU        2003-07-11 914520          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          104             15            51            69
#> 2 ICU                68             13            38            50
#> 3 Outpatient         12              7            11            11
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 533225  B_ESCHR_COLI Clinical   TRUE        
#>  2 686445  B_STPHY_CONS Clinical   TRUE        
#>  3 351193  B_ESCHR_COLI ICU        TRUE        
#>  4 175532  B_ESCHR_COLI Clinical   TRUE        
#>  5 F50462  B_BCTRD_FRGL ICU        TRUE        
#>  6 1B144C  B_STRPT_PNMN Outpatient TRUE        
#>  7 353710  B_STPHY_EPDR ICU        TRUE        
#>  8 450741  B_STPHY_EPDR ICU        TRUE        
#>  9 A76045  B_STPHY_EPDR ICU        TRUE        
#> 10 914520  B_STPHY_CONS ICU        TRUE        
#> # … with 190 more rows
# }