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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$date, episode_days = 60) # indices
#>   [1] 51 65 58 14 10 39 37 52 60 27 70 33 39 63 23 30 51 56 66 11 34 57 38 65 65
#>  [26] 44 19 67 18 27 53 14 22 49 14 54  3 31 21  8 14 50  3 67  4 42  8 59 28 33
#>  [51] 48 24 62 31 68  6 30 31 36  1 46 48 60  2 46 35 28  4 67 71 40 42 21 70 24
#>  [76] 51 42 42 47 52 13 57 29  4 56 29 64  8 52  9 42 69 50  2 66 12 30 23 56 57
#> [101] 56 10 51 41 68  7 56 21 17 65 32 34 14 43 12 45 54 64 71 68 15 56  1 21 15
#> [126] 71 44 65 25  6 68 43 10 22 43 14 36 25 65  9 10  2 26 37  2  1 47 70 58  1
#> [151] 26 37 61  6 18 70 20 61 59 16 56  8  5 22 50 36 45 46 16 17 63 21 40 46  1
#> [176]  7 55 40 23 12 30  9  1 27  4 18 63 53 19 42  3 22 54 28 61 25 61 11 16 33
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#>   [1]  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE
#>  [13] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#>  [25] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>  [37] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [49] FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
#>  [61] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
#>  [73] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
#>  [85] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [97]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
#> [109] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [121] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE
#> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [145] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
#> [157]  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE
#> [169]  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
#> [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#> [193]  TRUE FALSE FALSE  TRUE 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>         <rsi> <rsi> <rsi> <rsi>
#> 1 2002-08-14 785317     51 F      ICU      B_ESCHR_COLI R     NA    NA    NA   
#> 2 2002-07-30 218912     76 F      ICU      B_ESCHR_COLI R     NA    NA    NA   
#> 3 2002-06-19 402950     53 F      Clinical B_STPHY_HMNS R     NA    S     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 964129  2013-09-11 C         TRUE       
#>  2 B46416  2016-08-24 C         FALSE      
#>  3 523893  2015-04-13 A         FALSE      
#>  4 F35553  2004-12-29 A         FALSE      
#>  5 419655  2004-02-21 C         FALSE      
#>  6 F54287  2010-07-05 B         FALSE      
#>  7 662978  2010-02-20 A         FALSE      
#>  8 966513  2013-11-12 A         FALSE      
#>  9 618515  2015-08-09 C         FALSE      
#> 10 778D04  2007-09-30 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 2013-09-11 964129          1 TRUE       
#>  2 ICU      2016-08-24 B46416          1 TRUE       
#>  3 Clinical 2015-04-13 523893          1 TRUE       
#>  4 ICU      2004-12-29 F35553          4 TRUE       
#>  5 ICU      2004-02-21 419655          1 TRUE       
#>  6 Clinical 2010-07-05 F54287          1 TRUE       
#>  7 Clinical 2010-02-20 662978          1 TRUE       
#>  8 Clinical 2013-11-12 966513          1 TRUE       
#>  9 Clinical 2015-08-09 618515          1 TRUE       
#> 10 ICU      2007-09-30 778D04          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          118             14            55            80
#> 2 ICU                61             13            42            48
#> 3 Outpatient          5              4             5             5
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] TRUE
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 [193]
#>    patient mo           ward     flag_episode
#>    <chr>   <mo>         <chr>    <lgl>       
#>  1 964129  B_ESCHR_COLI Clinical TRUE        
#>  2 B46416  B_STPHY_EPDR ICU      TRUE        
#>  3 523893  B_STPHY_AURS Clinical TRUE        
#>  4 F35553  B_SERRT_MRCS ICU      TRUE        
#>  5 419655  B_STRPT_PNMN ICU      TRUE        
#>  6 F54287  B_KLBSL_OXYT Clinical TRUE        
#>  7 662978  B_STPHY_CONS Clinical TRUE        
#>  8 966513  B_STPHY_HMNS Clinical TRUE        
#>  9 618515  B_STPHY_EPDR Clinical TRUE        
#> 10 778D04  B_STRPT_PNMN ICU      TRUE        
#> # … with 190 more rows
# }