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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$date, episode_days = 60) # indices
#>   [1] 20 35  5 21 40  9  3 60 33 52 45 42 58 43 38  1 10 12  9 37  3 42 60  7 60
#>  [26] 56 59 49 50 32 50 60 58 29 18 57 20 43 52 29 24 45 29 54 37 25 58 42 12 40
#>  [51] 59 25  5 52 59 25 59 48  7 48 57 14 59 28 55 49 12 24  6  7 11 38 48  7 37
#>  [76] 45 24 56 54 10 47 29 55  7  1 11 35  9 42 16 58 23  2 12 38  3 33 14 17 38
#> [101] 52 22 34 26 10  5 51  6 31 41 30  4 11 25 27 24 54 29 12 32 23 49 20 10 18
#> [126] 13 51 54 31  7 35 50 44 38 16  5 39 30 15 46  3 60 19  3 40 15 45 46 43  8
#> [151]  9 43 39  5  9 18 33 42 55 55 42 56 17 17 34 55 36 26 26 15  1 46 26 53 40
#> [176] 19 59 14 34 25 60 47  5 29 15 19 23 54 28  7 47  9 39 23 22  9 52 24 61 21
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#>   [1]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE
#>  [13]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [25] FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#>  [49] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [61]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#>  [73]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [85]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [97] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#> [109]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [121] FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE
#> [133]  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#> [145] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
#> [157] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [169]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [181] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [193] FALSE  TRUE  TRUE FALSE  TRUE FALSE  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: 5 × 46
#>   date       patient   age gender ward      mo           PEN   OXA   FLC   AMX  
#>   <date>     <chr>   <dbl> <chr>  <chr>     <mo>         <rsi> <rsi> <rsi> <rsi>
#> 1 2002-11-12 CF9318     29 M      ICU       B_STPHY_CONS R     NA    R     R    
#> 2 2002-11-11 D80753     74 F      Outpatie… B_STPHY_CONS R     NA    S     NA   
#> 3 2002-11-28 600057     88 M      Outpatie… B_STPHY_AURS R     NA    S     R    
#> 4 2002-12-13 285137     78 F      ICU       B_ESCHR_COLI R     NA    NA    NA   
#> 5 2002-11-04 304347     62 M      Clinical  B_STRPT_PNMN S     NA    NA    S    
#> # … 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 561303  2006-08-11 B         TRUE       
#>  2 F54287  2010-07-05 C         FALSE      
#>  3 6BC362  2003-04-21 C         TRUE       
#>  4 557456  2006-10-24 B         FALSE      
#>  5 F56314  2011-10-17 B         FALSE      
#>  6 406502  2004-02-29 B         FALSE      
#>  7 CF9318  2002-11-12 C         FALSE      
#>  8 8DD375  2017-09-12 C         FALSE      
#>  9 650870  2009-11-12 C         TRUE       
#> 10 0DBF93  2015-10-12 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 [180]
#>    ward       date       patient new_index new_logical
#>    <chr>      <date>     <chr>       <dbl> <lgl>      
#>  1 Clinical   2006-08-11 561303          1 TRUE       
#>  2 Clinical   2010-07-05 F54287          1 TRUE       
#>  3 ICU        2003-04-21 6BC362          1 FALSE      
#>  4 ICU        2006-10-24 557456          1 TRUE       
#>  5 Outpatient 2011-10-17 F56314          1 TRUE       
#>  6 Clinical   2004-02-29 406502          1 TRUE       
#>  7 ICU        2002-11-12 CF9318          1 TRUE       
#>  8 ICU        2017-09-12 8DD375          1 TRUE       
#>  9 Outpatient 2009-11-12 650870          1 TRUE       
#> 10 Clinical   2015-10-12 0DBF93          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          106             14            49            72
#> 2 ICU                60             10            36            42
#> 3 Outpatient         14              8            11            12
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 [189]
#>    patient mo           ward       flag_episode
#>    <chr>   <mo>         <chr>      <lgl>       
#>  1 561303  B_STPHY_CONS Clinical   TRUE        
#>  2 F54287  B_STRPT_ANGN Clinical   TRUE        
#>  3 6BC362  B_ENTRC      ICU        TRUE        
#>  4 557456  B_BCTRD_FRGL ICU        TRUE        
#>  5 F56314  B_ESCHR_COLI Outpatient TRUE        
#>  6 406502  B_STRPT_PNMN Clinical   TRUE        
#>  7 CF9318  B_STPHY_CONS ICU        TRUE        
#>  8 8DD375  B_ESCHR_COLI ICU        TRUE        
#>  9 650870  B_ESCHR_COLI Outpatient TRUE        
#> 10 0DBF93  B_STPHY_EPDR Clinical   TRUE        
#> # … with 190 more rows
# }