CohortCharacteristics

A R package to Characterise cohorts

2025-06-26

CohortCharacteristics

Website

Cohort characteristics is on cran:

install.packages("CohortCharacteristics")

You can also install the development version from our github repo:

remotes::install_github("darwin-eu/CohortCharacteristics")

The documentation and vignettes of the packages can be found in our page: https://darwin-eu.github.io/CohortCharacteristics/

Let’s get started

First we going to need to create some cohorts for this session. Below’s code create three different cohort tables from the synthetic database: sinusitis, new_sinusitis, conditions and medications and saved it inside the cdm object.

Let’s get started

Let’s get started

datasetName <- "GiBleed"
dbdir <- here(paste0(datasetName, ".duckdb"))
con <- dbConnect(drv = duckdb(dbdir = dbdir))

cdm <- cdmFromCon(
  con = con,
  cdmSchema = "main",
  writeSchema = "main",
  writePrefix = "test_", # customise with your package
  cdmName = datasetName
)

Let’s get started

cdm$sinusitis <- conceptCohort(
  cdm = cdm,
  name = "sinusitis",
  conceptSet = list(
    bacterial_sinusitis = 4294548L, 
    viral_sinusitis = 40481087L, 
    chronic_sinusitis = 257012L, 
    any_sinusitis = c(4294548L, 40481087L, 257012L)
  ),
  exit = "event_start_date"
)
cdm$new_sinusitis <- cdm$sinusitis |>
  requireSex(sex = "Female", name = "new_sinusitis") |>
  requireAge(ageRange = c(0, 18))

cdm$conditions <- conceptCohort(
  cdm = cdm, 
  conceptSet = list(
    myocardial_infarction = c(4329847),
    fracture = c(4048695, 4142905, 4278672, 4237458, 4230399, 40480160, 4066995, 4059173, 4134304),
    allergy = c(4084167, 40486433, 4280726, 4048171),
    infection =  c(4116491, 433125, 4056621, 40481087, 4112343),
    pneumonia = c(255848),
    asthma = c(4051466, 317009)
  ),
  exit = "event_start_date",
  name = "conditions"
)

cdm$medications <- conceptCohort(
  cdm = cdm, 
  conceptSet = list(
    antineoplastic_and_immunomodulating_agents = c(1118088, 1118084, 40224132, 19010482, 40224805, 19007333, 1500211, 1305058, 1503184, 19134077, 1549786),
    musculoskeletal_system = c(1118088, 1557272, 40162359, 1124300, 1115008, 40173590, 1118084, 42707627, 19019273, 19019979, 19078461, 19003953, 1112807, 1115171, 1177480),
    antiinfectives_for_systemic_use = c(19129655, 1728416, 920293, 19074841, 920300, 920334, 19074843, 19075001, 19073183, 19073188, 1713671, 1729720, 19006318, 1778162, 46275444, 1717327, 1738521, 1741122, 1759842, 1713332, 1746114, 1768849, 46233710, 19133873, 46233988, 19133905),
    nervous_system = c(708298, 701322, 723013, 1129625, 1110410, 753626, 1124957, 1102527, 1125315, 782043, 791967, 1119510, 19078219, 757627, 40220386, 740275, 40223774, 1154029, 1127078, 1127433, 40222846, 19057271, 40223768, 45892894, 705944, 715997, 19078924, 19076374, 19077572, 40229134, 19059056, 19016749, 40236446, 19074679, 742185, 40231925, 1112807, 35605858, 40162522, 782047, 19039298, 19059528, 836654, 836659, 19115351, 19023398, 19002770, 19123231, 19133768, 40165015),
    dermatologicals = c(1129625, 1149380, 1124300, 836654, 1595799, 975125, 19008572),
    respiratory_system = c(1129625, 1149196, 1149380, 1150770, 1150836, 1153428, 1107830, 1110410, 738818, 1124957, 40169216, 1125443, 1119510, 1137529, 1154615, 1154343, 40223821, 19019979, 19112599, 40223834, 43012036, 40229134, 19029476, 19078461, 40232448, 1177480, 1192710, 1343916, 1150771, 1150837, 1107882, 975125, 1174888, 40169281, 40228214, 40228230, 19125062)
  ),
  name = "medications"
)

Workflow

We have three types of functions:

  • summarise: these functions produce an standardised output to summarise a cohort. This standard output is called summarised_result.

  • plot: these functions produce plots (currently, only ggplot, but working to implement plotly) from a summarised_result object.

  • table: these functions produce tables (gt and flextable) from a summarised_result object.

Contents

summariseCharacteristics

To get summarise characteristics for your cohort, pipe in below to your cohort table.

 results <- cdm$sinusitis |>
  summariseCharacteristics()

Its return the cohort characteristics in a summarised_result object format.

glimpse(results)
Rows: 176
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBl…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "any_sinusitis", "any_sinusitis", "any_sinusitis", "any_sinusitis", "any_sinusitis", "any_sin…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Number records", "Number subjects", "Cohort start date", "Cohort start date", "Cohort start …
$ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Female", "Female…
$ estimate_name    <chr> "count", "count", "min", "q25", "median", "q75", "max", "min", "q25", "median", "q75", "max",…
$ estimate_type    <chr> "integer", "integer", "date", "date", "date", "date", "date", "date", "date", "date", "date",…
$ estimate_value   <chr> "19032", "2688", "1908-10-30", "1972-08-22", "1988-06-09", "2003-09-27", "2019-06-25", "1908-…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…

tableCharacteristics

To get a tidy table from the summarised_result object.

tableCharacteristics(result = results, header = "cohort_name")
CDM name Variable name Variable level Estimate name
Cohort name
any_sinusitis bacterial_sinusitis chronic_sinusitis viral_sinusitis
GiBleed Number records - N 19,032 939 825 17,268
Number subjects - N 2,688 786 812 2,686
Cohort start date - Median [Q25 - Q75] 1988-06-09 [1972-08-22 - 2003-09-27] 1988-03-18 [1972-11-06 - 2002-01-21] 1985-11-07 [1971-10-25 - 2000-03-29] 1988-07-28 [1972-09-14 - 2003-12-24]
Range 1908-10-30 to 2019-06-25 1912-05-27 to 2019-06-07 1911-12-12 to 2019-05-22 1908-10-30 to 2019-06-25
Cohort end date - Median [Q25 - Q75] 1988-06-09 [1972-08-22 - 2003-09-27] 1988-03-18 [1972-11-06 - 2002-01-21] 1985-11-07 [1971-10-25 - 2000-03-29] 1988-07-28 [1972-09-14 - 2003-12-24]
Range 1908-10-30 to 2019-06-25 1912-05-27 to 2019-06-07 1911-12-12 to 2019-05-22 1908-10-30 to 2019-06-25
Age - Median [Q25 - Q75] 29 [14 - 45] 30 [14 - 45] 26 [12 - 41] 29 [14 - 45]
Mean (SD) 31.06 (20.46) 30.84 (19.94) 28.33 (19.70) 31.20 (20.51)
Range 0 to 109 0 to 96 0 to 109 0 to 108
Sex Female N (%) 9,745 (51.20%) 494 (52.61%) 427 (51.76%) 8,824 (51.10%)
Male N (%) 9,287 (48.80%) 445 (47.39%) 398 (48.24%) 8,444 (48.90%)
Prior observation - Median [Q25 - Q75] 10,878 [5,393 - 16,518] 11,017 [5,322 - 16,448] 9,747 [4,688 - 15,280] 10,930 [5,444 - 16,569]
Mean (SD) 11,525.45 (7,472.05) 11,444.45 (7,279.79) 10,530.56 (7,190.97) 11,577.39 (7,492.56)
Range 35 to 40,034 64 to 35,280 96 to 40,034 35 to 39,801
Future observation - Median [Q25 - Q75] 10,744 [5,234 - 16,446] 10,786 [5,830 - 16,367] 11,769 [6,476 - 16,921] 10,707 [5,160 - 16,428]
Mean (SD) 11,393.21 (7,499.90) 11,579.94 (7,614.42) 12,088.90 (7,393.57) 11,349.81 (7,497.31)
Range 0 to 40,093 0 to 39,104 0 to 39,045 0 to 40,093
Days in cohort - Median [Q25 - Q75] 1 [1 - 1] 1 [1 - 1] 1 [1 - 1] 1 [1 - 1]
Mean (SD) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00)
Range 1 to 1 1 to 1 1 to 1 1 to 1

Commonly used arguement for summarisedCharacteristics

  • strata to create stratification for the results.

  • cohortId filter by cohort Id.

  • ageGroup A list of age groups to return count for.

  • tableIntersect count/flag/date/days

  • cohortIntersect count/flag/date/days

  • conceptIntersectcount/flag/date/days

  • link

ageGroup example

cdm$sinusitis |>
  summariseCharacteristics(ageGroup = list(c(0,10),c(11,18), c(19,150))) |>
  tableCharacteristics(header = "cohort_name")

ageGroup example

CDM name Variable name Variable level Estimate name
Cohort name
any_sinusitis bacterial_sinusitis chronic_sinusitis viral_sinusitis
GiBleed Number records - N 19,032 939 825 17,268
Number subjects - N 2,688 786 812 2,686
Cohort start date - Median [Q25 - Q75] 1988-06-09 [1972-08-22 - 2003-09-27] 1988-03-18 [1972-11-06 - 2002-01-21] 1985-11-07 [1971-10-25 - 2000-03-29] 1988-07-28 [1972-09-14 - 2003-12-24]
Range 1908-10-30 to 2019-06-25 1912-05-27 to 2019-06-07 1911-12-12 to 2019-05-22 1908-10-30 to 2019-06-25
Cohort end date - Median [Q25 - Q75] 1988-06-09 [1972-08-22 - 2003-09-27] 1988-03-18 [1972-11-06 - 2002-01-21] 1985-11-07 [1971-10-25 - 2000-03-29] 1988-07-28 [1972-09-14 - 2003-12-24]
Range 1908-10-30 to 2019-06-25 1912-05-27 to 2019-06-07 1911-12-12 to 2019-05-22 1908-10-30 to 2019-06-25
Age - Median [Q25 - Q75] 29 [14 - 45] 30 [14 - 45] 26 [12 - 41] 29 [14 - 45]
Mean (SD) 31.06 (20.46) 30.84 (19.94) 28.33 (19.70) 31.20 (20.51)
Range 0 to 109 0 to 96 0 to 109 0 to 108
Age group 0 to 10 N (%) 3,566 (18.74%) 184 (19.60%) 176 (21.33%) 3,206 (18.57%)
11 to 18 N (%) 2,491 (13.09%) 113 (12.03%) 129 (15.64%) 2,249 (13.02%)
19 to 150 N (%) 12,975 (68.17%) 642 (68.37%) 520 (63.03%) 11,813 (68.41%)
Sex Female N (%) 9,745 (51.20%) 494 (52.61%) 427 (51.76%) 8,824 (51.10%)
Male N (%) 9,287 (48.80%) 445 (47.39%) 398 (48.24%) 8,444 (48.90%)
Prior observation - Median [Q25 - Q75] 10,878 [5,393 - 16,518] 11,017 [5,322 - 16,448] 9,747 [4,688 - 15,280] 10,930 [5,444 - 16,569]
Mean (SD) 11,525.45 (7,472.05) 11,444.45 (7,279.79) 10,530.56 (7,190.97) 11,577.39 (7,492.56)
Range 35 to 40,034 64 to 35,280 96 to 40,034 35 to 39,801
Future observation - Median [Q25 - Q75] 10,744 [5,234 - 16,446] 10,786 [5,830 - 16,367] 11,769 [6,476 - 16,921] 10,707 [5,160 - 16,428]
Mean (SD) 11,393.21 (7,499.90) 11,579.94 (7,614.42) 12,088.90 (7,393.57) 11,349.81 (7,497.31)
Range 0 to 40,093 0 to 39,104 0 to 39,045 0 to 40,093
Days in cohort - Median [Q25 - Q75] 1 [1 - 1] 1 [1 - 1] 1 [1 - 1] 1 [1 - 1]
Mean (SD) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00)
Range 1 to 1 1 to 1 1 to 1 1 to 1

strata example

result <- cdm$sinusitis |> 
  addAge(ageGroup = list(c(0,10),c(11,18), c(19,150))) |>
  summariseCharacteristics(cohortId = 1, strata = list("age_group"))
tableCharacteristics(result = result, header = "age_group")

strata example

CDM name Cohort name Variable name Variable level Estimate name
Age group
overall 0 to 10 11 to 18 19 to 150
GiBleed any_sinusitis Number records - N 19,032 3,566 2,491 12,975
Number subjects - N 2,688 1,979 1,580 2,638
Cohort start date - Median [Q25 - Q75] 1988-06-09 [1972-08-22 - 2003-09-27] 1966-07-03 [1955-05-24 - 1976-04-29] 1976-03-09 [1964-07-27 - 1985-04-18] 1997-12-25 [1985-02-16 - 2008-08-03]
Range 1908-10-30 to 2019-06-25 1908-10-30 to 1996-11-08 1920-07-14 to 2004-01-31 1928-05-28 to 2019-06-25
Cohort end date - Median [Q25 - Q75] 1988-06-09 [1972-08-22 - 2003-09-27] 1966-07-03 [1955-05-24 - 1976-04-29] 1976-03-09 [1964-07-27 - 1985-04-18] 1997-12-25 [1985-02-16 - 2008-08-03]
Range 1908-10-30 to 2019-06-25 1908-10-30 to 1996-11-08 1920-07-14 to 2004-01-31 1928-05-28 to 2019-06-25
Age - Median [Q25 - Q75] 29 [14 - 45] 5 [2 - 8] 14 [12 - 16] 39 [29 - 51]
Mean (SD) 31.06 (20.46) 5.03 (3.14) 14.45 (2.31) 41.41 (16.25)
Range 0 to 109 0 to 10 11 to 18 19 to 109
Sex Female N (%) 9,745 (51.20%) 1,800 (50.48%) 1,286 (51.63%) 6,659 (51.32%)
Male N (%) 9,287 (48.80%) 1,766 (49.52%) 1,205 (48.37%) 6,316 (48.68%)
Prior observation - Median [Q25 - Q75] 10,878 [5,393 - 16,518] 2,044 [1,010 - 3,014] 5,456 [4,718 - 6,200] 14,299 [10,606 - 18,916]
Mean (SD) 11,525.45 (7,472.05) 2,018.21 (1,153.26) 5,460.94 (851.13) 15,302.68 (5,934.54)
Range 35 to 40,034 35 to 4,013 4,018 to 6,939 6,938 to 40,034
Future observation - Median [Q25 - Q75] 10,744 [5,234 - 16,446] 18,964 [15,335 - 22,726] 15,373 [12,090 - 19,364] 7,267 [3,506 - 11,844]
Mean (SD) 11,393.21 (7,499.90) 19,541.70 (5,584.28) 16,149.03 (5,556.48) 8,240.66 (5,956.70)
Range 0 to 40,093 7,780 to 40,093 5,140 to 36,138 0 to 33,207
Days in cohort - Median [Q25 - Q75] 1 [1 - 1] 1 [1 - 1] 1 [1 - 1] 1 [1 - 1]
Mean (SD) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00)
Range 1 to 1 1 to 1 1 to 1 1 to 1

intersection with another table

you can get the count/flag/date/days with other tables

result <- cdm$sinusitis |>
  summariseCharacteristics(
    tableIntersectCount = list(
      "Number of visits prior year" = list(
        tableName = "visit_occurrence", window = c(-365, 0)
      )
    ),
    cohortIntersectFlag = list(
      "Conditions any time prior" = list(
        targetCohortTable = "conditions", window = c(-Inf, 0)
      ),
      "Medications prior year" = list(
        targetCohortTable = "medications", window = c(-365, 0)
      )
    )
  )

tableCharacteristics(result = result, header = "cohort_name")

intersection with another table

you can get the count/flag/date//days with other tables

CDM name Variable name Variable level Estimate name
Cohort name
any_sinusitis bacterial_sinusitis chronic_sinusitis viral_sinusitis
GiBleed Number records - N 19,032 939 825 17,268
Number subjects - N 2,688 786 812 2,686
Cohort start date - Median [Q25 - Q75] 1988-06-09 [1972-08-22 - 2003-09-27] 1988-03-18 [1972-11-06 - 2002-01-21] 1985-11-07 [1971-10-25 - 2000-03-29] 1988-07-28 [1972-09-14 - 2003-12-24]
Range 1908-10-30 to 2019-06-25 1912-05-27 to 2019-06-07 1911-12-12 to 2019-05-22 1908-10-30 to 2019-06-25
Cohort end date - Median [Q25 - Q75] 1988-06-09 [1972-08-22 - 2003-09-27] 1988-03-18 [1972-11-06 - 2002-01-21] 1985-11-07 [1971-10-25 - 2000-03-29] 1988-07-28 [1972-09-14 - 2003-12-24]
Range 1908-10-30 to 2019-06-25 1912-05-27 to 2019-06-07 1911-12-12 to 2019-05-22 1908-10-30 to 2019-06-25
Age - Median [Q25 - Q75] 29 [14 - 45] 30 [14 - 45] 26 [12 - 41] 29 [14 - 45]
Mean (SD) 31.06 (20.46) 30.84 (19.94) 28.33 (19.70) 31.20 (20.51)
Range 0 to 109 0 to 96 0 to 109 0 to 108
Sex Female N (%) 9,745 (51.20%) 494 (52.61%) 427 (51.76%) 8,824 (51.10%)
Male N (%) 9,287 (48.80%) 445 (47.39%) 398 (48.24%) 8,444 (48.90%)
Prior observation - Median [Q25 - Q75] 10,878 [5,393 - 16,518] 11,017 [5,322 - 16,448] 9,747 [4,688 - 15,280] 10,930 [5,444 - 16,569]
Mean (SD) 11,525.45 (7,472.05) 11,444.45 (7,279.79) 10,530.56 (7,190.97) 11,577.39 (7,492.56)
Range 35 to 40,034 64 to 35,280 96 to 40,034 35 to 39,801
Future observation - Median [Q25 - Q75] 10,744 [5,234 - 16,446] 10,786 [5,830 - 16,367] 11,769 [6,476 - 16,921] 10,707 [5,160 - 16,428]
Mean (SD) 11,393.21 (7,499.90) 11,579.94 (7,614.42) 12,088.90 (7,393.57) 11,349.81 (7,497.31)
Range 0 to 40,093 0 to 39,104 0 to 39,045 0 to 40,093
Days in cohort - Median [Q25 - Q75] 1 [1 - 1] 1 [1 - 1] 1 [1 - 1] 1 [1 - 1]
Mean (SD) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00) 1.00 (0.00)
Range 1 to 1 1 to 1 1 to 1 1 to 1
Number of visits prior year - Median [Q25 - Q75] 0.00 [0.00 - 0.00] 0.00 [0.00 - 0.00] 0.00 [0.00 - 0.00] 0.00 [0.00 - 0.00]
Mean (SD) 0.01 (0.07) 0.00 (0.07) 0.01 (0.08) 0.01 (0.07)
Range 0.00 to 1.00 0.00 to 1.00 0.00 to 1.00 0.00 to 1.00
Conditions any time prior Myocardial infarction N (%) 21 (0.11%) 1 (0.11%) 0 (0.00%) 20 (0.12%)
Fracture N (%) 7,723 (40.58%) 370 (39.40%) 324 (39.27%) 7,029 (40.71%)
Allergy N (%) 1,398 (7.35%) 79 (8.41%) 61 (7.39%) 1,258 (7.29%)
Infection N (%) 18,838 (98.98%) 845 (89.99%) 725 (87.88%) 17,268 (100.00%)
Asthma N (%) 586 (3.08%) 22 (2.34%) 24 (2.91%) 540 (3.13%)
Pneumonia N (%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%)
Medications prior year Dermatologicals N (%) 102 (0.54%) 3 (0.32%) 1 (0.12%) 98 (0.57%)
Respiratory system N (%) 610 (3.21%) 27 (2.88%) 24 (2.91%) 559 (3.24%)
Antiinfectives for systemic use N (%) 4,680 (24.59%) 247 (26.30%) 148 (17.94%) 4,285 (24.81%)
Nervous system N (%) 2,238 (11.76%) 115 (12.25%) 95 (11.52%) 2,028 (11.74%)
Musculoskeletal system N (%) 748 (3.93%) 39 (4.15%) 31 (3.76%) 678 (3.93%)
Antineoplastic and immunomodulating agents N (%) 249 (1.31%) 17 (1.81%) 12 (1.45%) 220 (1.27%)

Your turn

Can you characterise the new_sinusitis table in the cdm object and see whats the different in terms of patient characteristics compare to the sinusitis cohort?

💡 Click to see solution 
result <- summariseCharacteristics(cdm$new_sinusitis)

tableCharacteristics(result = result, header = "cohort_name")

Plots

you can obtain a plot of the variable you want from the summariseCharacteristics using the plotCharacteristics function

result <- cdm$sinusitis |>
  summariseCharacteristics() |>
  filter(variable_name == "Age" & strata_level == "overall")

plotCharacteristics(result = result, plotStyle = "boxplot")

Plots

result <- cdm$sinusitis |>
  summariseCharacteristics() |>
  filter(variable_name == "Age" & estimate_name == "median")

plotCharacteristics(result = result,
                    plotStyle = "barplot", 
                    facet = "cohort_name",
                    colour = "cdm_name")

Plots

these are ggplot object and are compatible with the usual ggplot command for editing.

result <- cdm$sinusitis |>
  summariseCharacteristics() |>
  filter(variable_name == "Age" & estimate_name == "median")

plotCharacteristics(result = result, 
                    plotStyle = "barplot", 
                    facet = "cohort_name",
                    colour = "cdm_name") +
  ggtitle("Median age of the cohort")

Plot cohort Attrition

Another useful function is plotCohortAttrition and summariseCohortAttrition, it return the attrition flowchart of your cohorts.

result <- summariseCohortAttrition(cohort = cdm$sinusitis, cohortId = 1) 

plotCohortAttrition(result = result, type = "png")

Your turn

Can you create a bar plot for gender the new_sinusitis table in the cdm object?

💡 Click to see solution 
result <- cdm$new_sinusitis |>
  summariseCharacteristics() |>
  filter(variable_name == "Sex" & estimate_name == "count")

plotCharacteristics(result = result,
                    plotStyle = "barplot",
                    facet = "cohort_name",
                    colour = "variable_level")

SummariseLargeScaleCharacteristics

Sometimes we might want to summarise all clinical events for the cohorts for different time window, we can do this with SummariseLargeScaleCharacteristics

result <- cdm$sinusitis |>
  summariseLargeScaleCharacteristics(
    window = list(c(-Inf, -1), c(1, Inf)),
    eventInWindow = "condition_occurrence",
    minimumFrequency = 0.05
  )

glimpse(result)
Rows: 338
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBl…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "any_sinusitis", "any_sinusitis", "any_sinusitis", "any_sinusitis", "any_sinusitis", "any_sin…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Acute bronchitis", "Acute bronchitis", "Acute viral pharyngitis", "Acute viral pharyngitis",…
$ variable_level   <chr> "-inf to -1", "-inf to -1", "-inf to -1", "-inf to -1", "-inf to -1", "-inf to -1", "-inf to …
$ estimate_name    <chr> "count", "percentage", "count", "percentage", "count", "percentage", "count", "percentage", "…
$ estimate_type    <chr> "integer", "percentage", "integer", "percentage", "integer", "percentage", "integer", "percen…
$ estimate_value   <chr> "13558", "71.24", "14471", "76.04", "1049", "5.51", "1423", "7.48", "992", "5.21", "973", "5.…
$ additional_name  <chr> "concept_id", "concept_id", "concept_id", "concept_id", "concept_id", "concept_id", "concept_…
$ additional_level <chr> "260139", "260139", "4112343", "4112343", "4310024", "4310024", "440086", "440086", "375671",…

SummariseLargeScaleCharacteristics

tableLargeScaleCharacteristics for tidy table

tableTopLargeScaleCharacteristics(result = result, topConcepts = 5)
Cohort name
any_sinusitis
bacterial_sinusitis
chronic_sinusitis
viral_sinusitis
Top
Window
-inf to -1 1 to inf -inf to -1 1 to inf -inf to -1 1 to inf -inf to -1 1 to inf
1 Viral sinusitis (40481087)
16045 (84.3%)		 Viral sinusitis (40481087)
16099 (85.1%)		 Viral sinusitis (40481087)
794 (84.6%)		 Viral sinusitis (40481087)
803 (85.9%)		 Viral sinusitis (40481087)
669 (81.1%)		 Viral sinusitis (40481087)
714 (86.8%)		 Viral sinusitis (40481087)
14582 (84.5%)		 Viral sinusitis (40481087)
14582 (85.0%)
2 Acute viral pharyngitis (4112343)
14471 (76.0%)		 Acute viral pharyngitis (4112343)
14034 (74.2%)		 Acute viral pharyngitis (4112343)
707 (75.3%)		 Acute viral pharyngitis (4112343)
684 (73.2%)		 Acute viral pharyngitis (4112343)
595 (72.1%)		 Acute viral pharyngitis (4112343)
636 (77.3%)		 Acute viral pharyngitis (4112343)
13169 (76.3%)		 Acute viral pharyngitis (4112343)
12714 (74.1%)
3 Acute bronchitis (260139)
13558 (71.2%)		 Acute bronchitis (260139)
13070 (69.1%)		 Acute bronchitis (260139)
673 (71.7%)		 Acute bronchitis (260139)
657 (70.3%)		 Acute bronchitis (260139)
575 (69.7%)		 Acute bronchitis (260139)
602 (73.2%)		 Acute bronchitis (260139)
12310 (71.3%)		 Acute bronchitis (260139)
11811 (68.8%)
4 Otitis media (372328)
13396 (70.4%)		 Osteoarthritis (80180)
12059 (63.8%)		 Otitis media (372328)
646 (68.8%)		 Osteoarthritis (80180)
584 (62.5%)		 Otitis media (372328)
560 (67.9%)		 Osteoarthritis (80180)
554 (67.3%)		 Otitis media (372328)
12190 (70.6%)		 Osteoarthritis (80180)
10921 (63.6%)
5 Laceration of foot (4109685)
1793 (9.4%)		 Esophagitis (30753)
1867 (9.9%)		 Fracture of clavicle (4237458)
93 (9.9%)		 Escherichia coli urinary tract infection (4116491)
90 (9.6%)		 Sinusitis (4283893)
503 (61.0%)		 Fracture of forearm (4278672)
82 (10.0%)		 Laceration of forearm (4155034)
1644 (9.5%)		 Diverticular disease (4266809)
1712 (10.0%)

SummariseCohortOverlap

When creating multiple cohort, we might be interested in the overlap of individuals between those cohorts. SummariseCohortOverlap does this for you.

result <- cdm$sinusitis |>
  summariseCohortOverlap()

glimpse(result)
Rows: 72
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBl…
$ group_name       <chr> "cohort_name_reference &&& cohort_name_comparator", "cohort_name_reference &&& cohort_name_co…
$ group_level      <chr> "any_sinusitis &&& bacterial_sinusitis", "any_sinusitis &&& bacterial_sinusitis", "any_sinusi…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Only in reference cohort", "In both cohorts", "Only in comparator cohort", "Only in referenc…
$ variable_level   <chr> "Subjects", "Subjects", "Subjects", "Subjects", "Subjects", "Subjects", "Subjects", "Subjects…
$ estimate_name    <chr> "count", "count", "count", "count", "count", "count", "count", "count", "count", "count", "co…
$ estimate_type    <chr> "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "inte…
$ estimate_value   <chr> "1902", "786", "0", "1876", "812", "0", "2", "2686", "0", "0", "786", "1902", "320", "466", "…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…

SummariseCohortOverlap

Again can get a table with tableCohortOverlap

Cohort name reference Cohort name comparator Estimate name
Variable name
Only in reference cohort In both cohorts Only in comparator cohort
GiBleed
any_sinusitis bacterial_sinusitis N (%) 1,902 (70.76%) 786 (29.24%) 0 (0.00%)
chronic_sinusitis N (%) 1,876 (69.79%) 812 (30.21%) 0 (0.00%)
viral_sinusitis N (%) 2 (0.07%) 2,686 (99.93%) 0 (0.00%)
bacterial_sinusitis chronic_sinusitis N (%) 320 (28.27%) 466 (41.17%) 346 (30.57%)
viral_sinusitis N (%) 1 (0.04%) 785 (29.21%) 1,901 (70.75%)
chronic_sinusitis viral_sinusitis N (%) 2 (0.07%) 810 (30.13%) 1,876 (69.79%)

plotCohortOverlap

Again can get a plot with plotCohortOverlap

plotCohortOverlap(result = result, uniqueCombinations = TRUE)

SummariseCohortTiming

When creating multiple cohort, we might be interested in timing of entry between cohorts. SummariseCohortTiming does this for you.

result <- cdm$sinusitis |>
  summariseCohortTiming(restrictToFirstEntry = TRUE)

glimpse(result)
Rows: 12,372
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBl…
$ group_name       <chr> "cohort_name_reference &&& cohort_name_comparator", "cohort_name_reference &&& cohort_name_co…
$ group_level      <chr> "any_sinusitis &&& bacterial_sinusitis", "any_sinusitis &&& bacterial_sinusitis", "any_sinusi…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "number records", "number subjects", "days_between_cohort_entries", "days_between_cohort_entr…
$ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, "density_001", "density_002", "density_003", "density_004", "dens…
$ estimate_name    <chr> "count", "count", "min", "q25", "median", "q75", "max", "density_x", "density_x", "density_x"…
$ estimate_type    <chr> "integer", "integer", "integer", "integer", "integer", "integer", "integer", "numeric", "nume…
$ estimate_value   <chr> "786", "786", "0", "1598", "7240", "12924", "33143", "-5039.28091171978", "-4954.69859895375"…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…

SummariseCohortOverlap

Again can get a table with tableCohortTiming

tableCohortTiming(result = result,
                  timeScale = "years", 
                  uniqueCombinations = FALSE)
Cohort name reference Cohort name comparator Variable name Estimate name Estimate value
GiBleed
any_sinusitis bacterial_sinusitis number records N 786
number subjects N 786
years_between_cohort_entries Median [Q25 - Q75] 19.82 [4.38 - 35.38]
Range 0.00 to 90.74
chronic_sinusitis number records N 812
number subjects N 812
years_between_cohort_entries Median [Q25 - Q75] 17.70 [3.36 - 35.00]
Range 0.00 to 101.63
viral_sinusitis number records N 2,686
number subjects N 2,686
years_between_cohort_entries Median [Q25 - Q75] 0.00 [0.00 - 0.00]
Range 0.00 to 47.65
bacterial_sinusitis any_sinusitis number records N 786
number subjects N 786
years_between_cohort_entries Median [Q25 - Q75] -19.82 [-35.38 - -4.38]
Range -90.74 to 0.00
chronic_sinusitis number records N 466
number subjects N 466
years_between_cohort_entries Median [Q25 - Q75] 0.10 [0.06 - 0.19]
Range -71.77 to 90.55
viral_sinusitis number records N 785
number subjects N 785
years_between_cohort_entries Median [Q25 - Q75] -19.73 [-35.17 - -3.89]
Range -90.74 to 47.65
chronic_sinusitis any_sinusitis number records N 812
number subjects N 812
years_between_cohort_entries Median [Q25 - Q75] -17.70 [-35.00 - -3.36]
Range -101.63 to 0.00
bacterial_sinusitis number records N 466
number subjects N 466
years_between_cohort_entries Median [Q25 - Q75] -0.10 [-0.19 - -0.06]
Range -90.55 to 71.77
viral_sinusitis number records N 810
number subjects N 810
years_between_cohort_entries Median [Q25 - Q75] -17.64 [-34.40 - -3.07]
Range -101.63 to 47.54
viral_sinusitis any_sinusitis number records N 2,686
number subjects N 2,686
years_between_cohort_entries Median [Q25 - Q75] 0.00 [0.00 - 0.00]
Range -47.65 to 0.00
bacterial_sinusitis number records N 785
number subjects N 785
years_between_cohort_entries Median [Q25 - Q75] 19.73 [3.89 - 35.17]
Range -47.65 to 90.74
chronic_sinusitis number records N 810
number subjects N 810
years_between_cohort_entries Median [Q25 - Q75] 17.64 [3.07 - 34.40]
Range -47.54 to 101.63

plotCohortOverlap

Again can get a plot with plotCohortTiming

plotCohortTiming(
  result = result,
  plotType = "boxplot",
  timeScale = "years",
  uniqueCombinations = FALSE
)

plotCohortOverlap

Or return a densityplot

plotCohortTiming(
  result = result,
  plotType = "densityplot",
  timeScale = "years",
  uniqueCombinations = FALSE
)

Your Turn

Can you get the large scale characteristics for drug exposure table with for the new_sinusitis cohort table with time window anytime prior.

💡 Click to see solution 
result <- cdm$new_sinusitis |>
  summariseLargeScaleCharacteristics(
    window = list(c(-Inf, -1)),
    eventInWindow = "drug_exposure",
    minimumFrequency = 0.05
  )
tableTopLargeScaleCharacteristics(result = result, topConcepts = 5)
Top
Cohort name
any_sinusitis bacterial_sinusitis chronic_sinusitis viral_sinusitis
1 poliovirus vaccine, inactivated (40213160)
2311 (74.9%)		 poliovirus vaccine, inactivated (40213160)
125 (77.6%)		 poliovirus vaccine, inactivated (40213160)
124 (78.0%)		 poliovirus vaccine, inactivated (40213160)
2062 (74.5%)
2 Aspirin 81 MG Oral Tablet (19059056)
1697 (55.0%)		 Aspirin 81 MG Oral Tablet (19059056)
91 (56.5%)		 Doxycycline Monohydrate 50 MG Oral Tablet (46233988)
15 (9.4%)		 Aspirin 81 MG Oral Tablet (19059056)
1528 (55.2%)
3 varicella virus vaccine (40213251)
290 (9.4%)		 varicella virus vaccine (40213251)
16 (9.9%)		 Ibuprofen 100 MG Oral Tablet (19019979)
14 (8.8%)		 varicella virus vaccine (40213251)
256 (9.3%)
4 Ibuprofen 100 MG Oral Tablet (19019979)
272 (8.8%)		 Ibuprofen 100 MG Oral Tablet (19019979)
15 (9.3%)		 {7 (Inert Ingredients 1 MG Oral Tablet) / 21 (Mestranol 0.05 MG / Norethindrone 1 MG Oral Tablet) } Pack [Norinyl 1+50 28 Day] (19128065)
11 (6.9%)		 Ibuprofen 100 MG Oral Tablet (19019979)
243 (8.8%)
5 {7 (Inert Ingredients 1 MG Oral Tablet) / 21 (Mestranol 0.05 MG / Norethindrone 1 MG Oral Tablet) } Pack [Norinyl 1+50 28 Day] (19128065)
171 (5.5%)		 Amoxicillin 250 MG Oral Capsule (19073183)
9 (5.6%)		 Cefaclor 250 MG Oral Capsule (19074843)
8 (5.0%)		 {7 (Inert Ingredients 1 MG Oral Tablet) / 21 (Mestranol 0.05 MG / Norethindrone 1 MG Oral Tablet) } Pack [Norinyl 1+50 28 Day] (19128065)
153 (5.5%)

CohortCharacteristics

👉 Packages website
👉 CRAN link
👉 Manual

📧 marti.catalasabate@ndorms.ox.ac.uk