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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$date, episode_days = 60) # indices
#>   [1] 36 10 11 17 57 60 18  7  5 18  8 60 31 28 16 41  3 43 48 61  2 46 35 51  2
#>  [26] 28 44 45  3 49 25 56 44 31 16  1  4 32 57  6 57 62 19 50 14 55 38 55 24 35
#>  [51]  5 15  7 36 38 28 20  5 24 12 63 39 17 13 35 20 30 20 13 44 13 23 21 36 45
#>  [76] 43 52 53 50 35 24 32  5 23 40 16 63 44 17 63 13 16 51 36 60 12  1  8 10 26
#> [101] 25 56 55 60  1 37 23 30 20 58 11 25  8 21 29 27 10 54 59 59 17 12 11 44 33
#> [126] 48 54 18 39  8 52 26 40 58 33  7 16  8 16 49 12 15 61  1 28 48 18  1  4 19
#> [151]  2 38 37 36  9 58 35 37 57 42 16 14 29 30 34 35  5 62 48 12 11 55 26 57 35
#> [176] 47 31 24 60 37 31 56 52 41 61 33 26 47 11  8 55 13 59 62 35 34 11 22 38  8
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#>   [1] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [13] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>  [25]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [37] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
#>  [49] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
#>  [61] FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [73]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE
#>  [85]  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#>  [97] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
#> [109] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
#> [121] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [145] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
#> [157] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
#> [169]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE
#> [181] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE
#> [193] FALSE  TRUE  TRUE  TRUE FALSE  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>         <rsi> <rsi> <rsi> <rsi>
#> 1 2002-07-23 F35553     51 M      ICU   B_STPHY_AURS R     NA    S     R    
#> 2 2002-07-24 F35553     51 M      ICU   B_STPHY_AURS R     NA    S     R    
#> # … 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 207325  2011-05-08 C         FALSE      
#>  2 304508  2004-05-09 B         TRUE       
#>  3 619705  2004-07-29 A         TRUE       
#>  4 3CF3C4  2006-06-26 B         FALSE      
#>  5 A79917  2016-05-21 C         FALSE      
#>  6 F5F794  2017-02-18 B         FALSE      
#>  7 698578  2006-11-13 A         FALSE      
#>  8 438785  2003-10-03 B         FALSE      
#>  9 A26548  2003-04-13 C         FALSE      
#> 10 418311  2006-11-10 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 [186]
#>    ward     date       patient new_index new_logical
#>    <chr>    <date>     <chr>       <dbl> <lgl>      
#>  1 ICU      2011-05-08 207325          1 TRUE       
#>  2 Clinical 2004-05-09 304508          1 TRUE       
#>  3 ICU      2004-07-29 619705          1 TRUE       
#>  4 Clinical 2006-06-26 3CF3C4          1 TRUE       
#>  5 Clinical 2016-05-21 A79917          1 TRUE       
#>  6 ICU      2017-02-18 F5F794          1 TRUE       
#>  7 Clinical 2006-11-13 698578          1 TRUE       
#>  8 ICU      2003-10-03 438785          1 TRUE       
#>  9 ICU      2003-04-13 A26548          1 TRUE       
#> 10 ICU      2006-11-10 418311          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          126             14            52            77
#> 2 ICU                47             12            30            35
#> 3 Outpatient         13              7            12            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] 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 [192]
#>    patient mo           ward     flag_episode
#>    <chr>   <mo>         <chr>    <lgl>       
#>  1 207325    UNKNOWN    ICU      TRUE        
#>  2 304508  B_STRPT_PNMN Clinical TRUE        
#>  3 619705  B_STRPT_ANGN ICU      TRUE        
#>  4 3CF3C4  B_STPHY_CONS Clinical TRUE        
#>  5 A79917  B_ESCHR_COLI Clinical TRUE        
#>  6 F5F794  B_STPHY_AURS ICU      TRUE        
#>  7 698578  B_ESCHR_COLI Clinical TRUE        
#>  8 438785  B_STPHY_EPDR ICU      TRUE        
#>  9 A26548  B_STPHY_CONS ICU      TRUE        
#> 10 418311  B_STPHY_CONS ICU      TRUE        
#> # … with 190 more rows
# }