Summarise temporal trends in OMOP tables • OmopSketch

Introduction

In this vignette, we will explore the OmopSketch function summariseTrend(), which summarises temporal trends from OMOP CDM tables. This function allows you to visualise how key measures (such as number of records, number of persons, person-days, age, or sex distribution) change over time.

Create a mock cdm

Let’s start by loading essential packages and creating a mock CDM using the R package omock

library(omock)
library(OmopSketch)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(visOmopResults)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
#> ℹ Reading GiBleed tables.
#> ℹ Adding drug_strength table.
#> ℹ Creating local <cdm_reference> object.
#> ℹ Inserting <cdm_reference> into duckdb.

cdm
#> 
#> ── # OMOP CDM reference (duckdb) of GiBleed ────────────────────────────────────
#> • omop tables: care_site, cdm_source, concept, concept_ancestor, concept_class,
#> concept_relationship, concept_synonym, condition_era, condition_occurrence,
#> cost, death, device_exposure, domain, dose_era, drug_era, drug_exposure,
#> drug_strength, fact_relationship, location, measurement, metadata, note,
#> note_nlp, observation, observation_period, payer_plan_period, person,
#> procedure_occurrence, provider, relationship, source_to_concept_map, specimen,
#> visit_detail, visit_occurrence, vocabulary
#> • cohort tables: -
#> • achilles tables: -
#> • other tables: -

Summarise temporal trends

Let’s use summariseTrend() to get an overview of the content of the table over time. In this example, we’ll summarise yearly trends for condition_occurrence and drug_exposure tables, and also include observation_period as an episode table.

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = c("condition_occurrence", "drug_exposure"),
  episode = "observation_period",
  interval = "years",
)

summarisedResult |>
  glimpse()
#> Rows: 676
#> Columns: 13
#> $ result_id        <int> 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1,…
#> $ cdm_name         <chr> "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed"…
#> $ group_name       <chr> "omop_table", "omop_table", "omop_table", "omop_table…
#> $ group_level      <chr> "condition_occurrence", "condition_occurrence", "obse…
#> $ strata_name      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level     <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name    <chr> "Number of records", "Number of records", "Number of …
#> $ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ estimate_name    <chr> "count", "percentage", "count", "percentage", "count"…
#> $ estimate_type    <chr> "integer", "percentage", "integer", "percentage", "in…
#> $ estimate_value   <chr> "1", "0.00", "2", "0.04", "4", "0.01", "4", "0.01", "…
#> $ additional_name  <chr> "time_interval", "time_interval", "time_interval", "t…
#> $ additional_level <chr> "1908-01-01 to 1908-12-31", "1908-01-01 to 1908-12-31…

Notice that the output is in the summarised result format.

What are Event and Episode tables?

Event tables capture occurrences that happen at a single point in time (for example, a diagnosis, a prescription, or a measurement). For these tables, each record is linked to a time interval based only on its start date.
Episode describe periods that span over time (for example, observation periods or treatment eras). Each record contributes to every time interval between its start and end dates, reflecting its entire duration within the study period.

You can check whether a table was treated as an event or an episode table in the settings of the summarised result:

summarisedResult |>
  addSettings(settingsColumn = "type") |>
  glimpse()
#> Rows: 676
#> Columns: 14
#> $ result_id        <int> 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1,…
#> $ cdm_name         <chr> "GiBleed", "GiBleed", "GiBleed", "GiBleed", "GiBleed"…
#> $ group_name       <chr> "omop_table", "omop_table", "omop_table", "omop_table…
#> $ group_level      <chr> "condition_occurrence", "condition_occurrence", "obse…
#> $ strata_name      <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level     <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name    <chr> "Number of records", "Number of records", "Number of …
#> $ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ estimate_name    <chr> "count", "percentage", "count", "percentage", "count"…
#> $ estimate_type    <chr> "integer", "percentage", "integer", "percentage", "in…
#> $ estimate_value   <chr> "1", "0.00", "2", "0.04", "4", "0.01", "4", "0.01", "…
#> $ additional_name  <chr> "time_interval", "time_interval", "time_interval", "t…
#> $ additional_level <chr> "1908-01-01 to 1908-12-31", "1908-01-01 to 1908-12-31…
#> $ type             <chr> "event", "event", "episode", "episode", "event", "eve…

Outputs

You can choose what to summarise using the output argument. Options include:

“record”: Number of records (default value)
“person”: Number of distinct persons
“person-days”: Number of person-days (episode tables only)
“age”: Median age at start date of each interval
“sex”: Number of females

Records and subjects per year

For each time interval the results will include the number of records and number of individuals observed during that period. In addition to absolute counts, the function also reports the percentage of records and individuals within each interval relative to the total counts in the entire table.

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  output = c("record", "person"),
  interval = "years"
)

summarisedResult |>
  select(group_level, variable_name, additional_level, estimate_name, estimate_value)
#> # A tibble: 452 × 5
#>    group_level       variable_name additional_level estimate_name estimate_value
#>    <chr>             <chr>         <chr>            <chr>         <chr>         
#>  1 condition_occurr… Number of re… 1908-01-01 to 1… count         1             
#>  2 condition_occurr… Number of su… 1908-01-01 to 1… count         1             
#>  3 condition_occurr… Number of re… 1908-01-01 to 1… percentage    0.00          
#>  4 condition_occurr… Number of su… 1908-01-01 to 1… percentage    0.04          
#>  5 condition_occurr… Number of re… 1909-01-01 to 1… count         4             
#>  6 condition_occurr… Number of su… 1909-01-01 to 1… count         4             
#>  7 condition_occurr… Number of re… 1909-01-01 to 1… percentage    0.01          
#>  8 condition_occurr… Number of su… 1909-01-01 to 1… percentage    0.15          
#>  9 condition_occurr… Number of re… 1910-01-01 to 1… count         16            
#> 10 condition_occurr… Number of su… 1910-01-01 to 1… count         13            
#> # ℹ 442 more rows

Person-days

When an episode table is specified, you can include “person-days” in the output to summarise total follow-up time across intervals. The results will show both the number of person-days in each interval and the percentage of person-days relative to the total accumulated across the entire table.

summarisedResult <- summariseTrend(
  cdm = cdm,
  episode = "observation_period",
  output = "person-days",
  interval = "years"
)

summarisedResult |>
  select(group_level, variable_name, additional_level, estimate_name, estimate_value)
#> # A tibble: 226 × 5
#>    group_level       variable_name additional_level estimate_name estimate_value
#>    <chr>             <chr>         <chr>            <chr>         <chr>         
#>  1 observation_peri… Person-days   1908-01-01 to 1… count         175           
#>  2 observation_peri… Person-days   1908-01-01 to 1… percentage    0.00          
#>  3 observation_peri… Person-days   1909-01-01 to 1… count         4636          
#>  4 observation_peri… Person-days   1909-01-01 to 1… percentage    0.01          
#>  5 observation_peri… Person-days   1910-01-01 to 1… count         9946          
#>  6 observation_peri… Person-days   1910-01-01 to 1… percentage    0.01          
#>  7 observation_peri… Person-days   1911-01-01 to 1… count         13031         
#>  8 observation_peri… Person-days   1911-01-01 to 1… percentage    0.02          
#>  9 observation_peri… Person-days   1912-01-01 to 1… count         16986         
#> 10 observation_peri… Person-days   1912-01-01 to 1… percentage    0.02          
#> # ℹ 216 more rows

Note: The function will automatically skip “person-days” for event tables.

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "visit_occurrence",
  output = "person-days",
  interval = "years"
)
#> → The number of person-days is not computed for event tables
summarisedResult
#> # A tibble: 0 × 13
#> # ℹ 13 variables: result_id <int>, cdm_name <chr>, group_name <chr>,
#> #   group_level <chr>, strata_name <chr>, strata_level <chr>,
#> #   variable_name <chr>, variable_level <chr>, estimate_name <chr>,
#> #   estimate_type <chr>, estimate_value <chr>, additional_name <chr>,
#> #   additional_level <chr>

Age

When “age” is included in the output argument, the function reports the median age of individuals for each time interval. For every record, age is calculated either at the start of the time interval or at the record’s start date, whichever comes first. This allows you to examine how the age distribution of individuals evolves over time for a given event or episode table.

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  output = "age",
  interval = "years"
)

summarisedResult |>
  select(variable_name, additional_level, estimate_name, estimate_value)
#> # A tibble: 113 × 4
#>    variable_name additional_level         estimate_name estimate_value
#>    <chr>         <chr>                    <chr>         <chr>         
#>  1 Age           1908-01-01 to 1908-12-31 median        0             
#>  2 Age           1909-01-01 to 1909-12-31 median        0             
#>  3 Age           1910-01-01 to 1910-12-31 median        1             
#>  4 Age           1911-01-01 to 1911-12-31 median        1             
#>  5 Age           1912-01-01 to 1912-12-31 median        1             
#>  6 Age           1913-01-01 to 1913-12-31 median        1             
#>  7 Age           1914-01-01 to 1914-12-31 median        3             
#>  8 Age           1915-01-01 to 1915-12-31 median        3             
#>  9 Age           1916-01-01 to 1916-12-31 median        4             
#> 10 Age           1917-01-01 to 1917-12-31 median        5             
#> # ℹ 103 more rows

Sex output

When “sex” is included in the output argument, the function counts the number of females in each time interval. It also provides the percentage of females relative to the total number of individuals in the entire table. This output is particularly useful for exploring changes in the sex distribution of records over time.

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  output = "sex",
  interval = "years"
)
summarisedResult |>
  select(variable_name, additional_level, estimate_name, estimate_value)
#> # A tibble: 226 × 4
#>    variable_name     additional_level         estimate_name estimate_value
#>    <chr>             <chr>                    <chr>         <chr>         
#>  1 Number of females 1908-01-01 to 1908-12-31 count         1             
#>  2 Number of females 1908-01-01 to 1908-12-31 percentage    0.04          
#>  3 Number of females 1909-01-01 to 1909-12-31 count         4             
#>  4 Number of females 1909-01-01 to 1909-12-31 percentage    0.15          
#>  5 Number of females 1910-01-01 to 1910-12-31 count         12            
#>  6 Number of females 1910-01-01 to 1910-12-31 percentage    0.45          
#>  7 Number of females 1911-01-01 to 1911-12-31 count         9             
#>  8 Number of females 1911-01-01 to 1911-12-31 percentage    0.33          
#>  9 Number of females 1912-01-01 to 1912-12-31 count         16            
#> 10 Number of females 1912-01-01 to 1912-12-31 percentage    0.59          
#> # ℹ 216 more rows

Intervals

The argument `interval`` controls the temporal granularity of the results. Possible values are “overall” (default, no stratification by time), “years”, “quarters”, and “months”.

For example, to see quarterly trends:

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  interval = "quarters",
  output = "record"
)

summarisedResult |>
  select(additional_level, estimate_value)
#> # A tibble: 884 × 2
#>    additional_level         estimate_value
#>    <chr>                    <chr>         
#>  1 1908-10-01 to 1908-12-31 1             
#>  2 1908-10-01 to 1908-12-31 0.00          
#>  3 1909-04-01 to 1909-06-30 2             
#>  4 1909-04-01 to 1909-06-30 0.00          
#>  5 1909-07-01 to 1909-09-30 2             
#>  6 1909-07-01 to 1909-09-30 0.00          
#>  7 1910-01-01 to 1910-03-31 4             
#>  8 1910-01-01 to 1910-03-31 0.01          
#>  9 1910-04-01 to 1910-06-30 4             
#> 10 1910-04-01 to 1910-06-30 0.01          
#> # ℹ 874 more rows

Stratify by age and sex

You can use the arguments ageGroup and sex to stratify the results.

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  interval = "years",
  output = c("record", "age", "sex"),
  ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf)),
  sex = TRUE
)

summarisedResult |>
  select(variable_name, strata_level, estimate_name, estimate_value)
#> # A tibble: 3,318 × 4
#>    variable_name     strata_level   estimate_name estimate_value
#>    <chr>             <chr>          <chr>         <chr>         
#>  1 Number of records overall        count         1             
#>  2 Number of records <35            count         1             
#>  3 Number of records Female         count         1             
#>  4 Number of records Female &&& <35 count         1             
#>  5 Number of females overall        count         1             
#>  6 Number of females <35            count         1             
#>  7 Age               overall        median        0             
#>  8 Age               <35            median        0             
#>  9 Age               Female         median        0             
#> 10 Age               Female &&& <35 median        0             
#> # ℹ 3,308 more rows

By default, the output includes the “overall” group as well as combined strata (e.g., Female and >=35). Note that for output = "sex", sex stratification is not applied because a single estimate summarising the female population is returned.

In-observation stratification

When inObservation = TRUE, the results will indicate whether each record occurred within the subject’s observation period. This can be useful for identifying data quality issues or assessing completeness.

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  interval = "overall",
  output = "record",
  inObservation = TRUE
)

summarisedResult |>
  select(variable_name, strata_name, strata_level, estimate_name, estimate_value)
#> # A tibble: 6 × 5
#>   variable_name     strata_name    strata_level estimate_name estimate_value
#>   <chr>             <chr>          <chr>        <chr>         <chr>         
#> 1 Number of records overall        overall      count         65332         
#> 2 Number of records in_observation FALSE        count         85            
#> 3 Number of records in_observation TRUE         count         65247         
#> 4 Number of records overall        overall      percentage    100.00        
#> 5 Number of records in_observation FALSE        percentage    0.13          
#> 6 Number of records in_observation TRUE         percentage    99.87

Date Range

You can restrict the study period using the dateRange argument.

summarisedResult <- summariseTrend(
  cdm = cdm,
  event = "drug_exposure",
  dateRange = as.Date(c("1990-01-01", "2010-01-01"))
)

summarisedResult |>
  settings() |>
  glimpse()
#> Rows: 1
#> Columns: 12
#> $ result_id          <int> 1
#> $ result_type        <chr> "summarise_trend"
#> $ package_name       <chr> "OmopSketch"
#> $ package_version    <chr> "1.0.0.900"
#> $ group              <chr> "omop_table"
#> $ strata             <chr> ""
#> $ additional         <chr> ""
#> $ min_cell_count     <chr> "0"
#> $ interval           <chr> "overall"
#> $ study_period_end   <chr> "2010-01-01"
#> $ study_period_start <chr> "1990-01-01"
#> $ type               <chr> "event"

Tidy the summarised object with tableTrend

tableTrend() helps you convert a summarised result into a nicely formatted table for reporting or inspection (for example gt (default), flextable, reactable, or DT::datatable). It formats time intervals, strata and estimate columns so the results are easy to read and export.

result <- summariseTrend(
  cdm = cdm,
  event = "condition_occurrence",
  episode = "drug_exposure",
  output = "age",
  interval = "years"
)
tableTrend(result = result)

Summary of Age by years in condition_occurrence, drug_exposure tables
Variable name	Time interval	Estimate name	Database name
Variable name	Time interval	Estimate name	GiBleed
event; condition_occurrence
Age	1908-01-01 to 1908-12-31	Median	0.00
	1909-01-01 to 1909-12-31	Median	0.00
	1910-01-01 to 1910-12-31	Median	1.00
	1911-01-01 to 1911-12-31	Median	1.00
	1912-01-01 to 1912-12-31	Median	1.00
	1913-01-01 to 1913-12-31	Median	1.00
	1914-01-01 to 1914-12-31	Median	3.00
	1915-01-01 to 1915-12-31	Median	3.00
	1916-01-01 to 1916-12-31	Median	4.00
	1917-01-01 to 1917-12-31	Median	5.00
	1918-01-01 to 1918-12-31	Median	6.00
	1919-01-01 to 1919-12-31	Median	6.00
	1920-01-01 to 1920-12-31	Median	6.00
	1921-01-01 to 1921-12-31	Median	4.00
	1922-01-01 to 1922-12-31	Median	4.00
	1923-01-01 to 1923-12-31	Median	5.00
	1924-01-01 to 1924-12-31	Median	4.00
	1925-01-01 to 1925-12-31	Median	5.00
	1926-01-01 to 1926-12-31	Median	6.00
	1927-01-01 to 1927-12-31	Median	7.00
	1928-01-01 to 1928-12-31	Median	8.00
	1929-01-01 to 1929-12-31	Median	9.00
	1930-01-01 to 1930-12-31	Median	9.00
	1931-01-01 to 1931-12-31	Median	9.00
	1932-01-01 to 1932-12-31	Median	11.00
	1933-01-01 to 1933-12-31	Median	11.00
	1934-01-01 to 1934-12-31	Median	13.00
	1935-01-01 to 1935-12-31	Median	11.00
	1936-01-01 to 1936-12-31	Median	11.00
	1937-01-01 to 1937-12-31	Median	7.00
	1938-01-01 to 1938-12-31	Median	11.00
	1939-01-01 to 1939-12-31	Median	6.00
	1940-01-01 to 1940-12-31	Median	5.00
	1941-01-01 to 1941-12-31	Median	5.00
	1942-01-01 to 1942-12-31	Median	7.00
	1943-01-01 to 1943-12-31	Median	7.00
	1944-01-01 to 1944-12-31	Median	8.00
	1945-01-01 to 1945-12-31	Median	7.00
	1946-01-01 to 1946-12-31	Median	7.00
	1947-01-01 to 1947-12-31	Median	8.00
	1948-01-01 to 1948-12-31	Median	9.00
	1949-01-01 to 1949-12-31	Median	8.00
	1950-01-01 to 1950-12-31	Median	6.00
	1951-01-01 to 1951-12-31	Median	6.00
	1952-01-01 to 1952-12-31	Median	6.00
	1953-01-01 to 1953-12-31	Median	7.00
	1954-01-01 to 1954-12-31	Median	7.00
	1955-01-01 to 1955-12-31	Median	8.00
	1956-01-01 to 1956-12-31	Median	7.00
	1957-01-01 to 1957-12-31	Median	9.00
	1958-01-01 to 1958-12-31	Median	7.00
	1959-01-01 to 1959-12-31	Median	8.00
	1960-01-01 to 1960-12-31	Median	8.00
	1961-01-01 to 1961-12-31	Median	8.00
	1962-01-01 to 1962-12-31	Median	8.00
	1963-01-01 to 1963-12-31	Median	9.00
	1964-01-01 to 1964-12-31	Median	9.00
	1965-01-01 to 1965-12-31	Median	9.00
	1966-01-01 to 1966-12-31	Median	10.00
	1967-01-01 to 1967-12-31	Median	10.00
	1968-01-01 to 1968-12-31	Median	11.00
	1969-01-01 to 1969-12-31	Median	11.00
	1970-01-01 to 1970-12-31	Median	11.00
	1971-01-01 to 1971-12-31	Median	12.00
	1972-01-01 to 1972-12-31	Median	13.00
	1973-01-01 to 1973-12-31	Median	12.00
	1974-01-01 to 1974-12-31	Median	13.00
	1975-01-01 to 1975-12-31	Median	14.00
	1976-01-01 to 1976-12-31	Median	15.00
	1977-01-01 to 1977-12-31	Median	16.00
	1978-01-01 to 1978-12-31	Median	17.00
	1979-01-01 to 1979-12-31	Median	17.00
	1980-01-01 to 1980-12-31	Median	20.00
	1981-01-01 to 1981-12-31	Median	19.00
	1982-01-01 to 1982-12-31	Median	21.00
	1983-01-01 to 1983-12-31	Median	22.00
	1984-01-01 to 1984-12-31	Median	22.00
	1985-01-01 to 1985-12-31	Median	24.00
	1986-01-01 to 1986-12-31	Median	25.00
	1987-01-01 to 1987-12-31	Median	27.00
	1988-01-01 to 1988-12-31	Median	27.00
	1989-01-01 to 1989-12-31	Median	29.00
	1990-01-01 to 1990-12-31	Median	29.00
	1991-01-01 to 1991-12-31	Median	30.00
	1992-01-01 to 1992-12-31	Median	33.00
	1993-01-01 to 1993-12-31	Median	33.00
	1994-01-01 to 1994-12-31	Median	34.00
	1995-01-01 to 1995-12-31	Median	35.00
	1996-01-01 to 1996-12-31	Median	36.00
	1997-01-01 to 1997-12-31	Median	37.00
	1998-01-01 to 1998-12-31	Median	37.00
	1999-01-01 to 1999-12-31	Median	38.00
	2000-01-01 to 2000-12-31	Median	38.00
	2001-01-01 to 2001-12-31	Median	39.00
	2002-01-01 to 2002-12-31	Median	40.00
	2003-01-01 to 2003-12-31	Median	41.00
	2004-01-01 to 2004-12-31	Median	41.00
	2005-01-01 to 2005-12-31	Median	41.00
	2006-01-01 to 2006-12-31	Median	42.00
	2007-01-01 to 2007-12-31	Median	42.00
	2008-01-01 to 2008-12-31	Median	43.00
	2009-01-01 to 2009-12-31	Median	45.00
	2010-01-01 to 2010-12-31	Median	46.00
	2011-01-01 to 2011-12-31	Median	48.00
	2012-01-01 to 2012-12-31	Median	48.00
	2013-01-01 to 2013-12-31	Median	49.00
	2014-01-01 to 2014-12-31	Median	50.00
	2015-01-01 to 2015-12-31	Median	51.00
	2016-01-01 to 2016-12-31	Median	52.00
	2017-01-01 to 2017-12-31	Median	54.00
	2018-01-01 to 2018-12-31	Median	55.00
	2019-01-01 to 2019-12-31	Median	56.00
	overall	Median	29.00
episode; drug_exposure
Age	1909-01-01 to 1909-12-31	Median	0.00
	1910-01-01 to 1910-12-31	Median	1.00
	1911-01-01 to 1911-12-31	Median	1.00
	1912-01-01 to 1912-12-31	Median	1.00
	1913-01-01 to 1913-12-31	Median	1.00
	1914-01-01 to 1914-12-31	Median	3.00
	1915-01-01 to 1915-12-31	Median	3.00
	1916-01-01 to 1916-12-31	Median	4.00
	1917-01-01 to 1917-12-31	Median	4.00
	1918-01-01 to 1918-12-31	Median	6.00
	1919-01-01 to 1919-12-31	Median	6.00
	1920-01-01 to 1920-12-31	Median	5.00
	1921-01-01 to 1921-12-31	Median	4.00
	1922-01-01 to 1922-12-31	Median	3.00
	1923-01-01 to 1923-12-31	Median	3.00
	1924-01-01 to 1924-12-31	Median	4.00
	1925-01-01 to 1925-12-31	Median	5.00
	1926-01-01 to 1926-12-31	Median	7.00
	1927-01-01 to 1927-12-31	Median	7.00
	1928-01-01 to 1928-12-31	Median	6.00
	1929-01-01 to 1929-12-31	Median	7.00
	1930-01-01 to 1930-12-31	Median	8.00
	1931-01-01 to 1931-12-31	Median	9.00
	1932-01-01 to 1932-12-31	Median	11.00
	1933-01-01 to 1933-12-31	Median	10.00
	1934-01-01 to 1934-12-31	Median	11.00
	1935-01-01 to 1935-12-31	Median	11.00
	1936-01-01 to 1936-12-31	Median	8.00
	1937-01-01 to 1937-12-31	Median	4.00
	1938-01-01 to 1938-12-31	Median	14.00
	1939-01-01 to 1939-12-31	Median	3.00
	1940-01-01 to 1940-12-31	Median	5.00
	1941-01-01 to 1941-12-31	Median	4.00
	1942-01-01 to 1942-12-31	Median	5.00
	1943-01-01 to 1943-12-31	Median	6.00
	1944-01-01 to 1944-12-31	Median	7.00
	1945-01-01 to 1945-12-31	Median	5.00
	1946-01-01 to 1946-12-31	Median	4.00
	1947-01-01 to 1947-12-31	Median	5.00
	1948-01-01 to 1948-12-31	Median	4.00
	1949-01-01 to 1949-12-31	Median	5.00
	1950-01-01 to 1950-12-31	Median	4.00
	1951-01-01 to 1951-12-31	Median	4.00
	1952-01-01 to 1952-12-31	Median	4.00
	1953-01-01 to 1953-12-31	Median	5.00
	1954-01-01 to 1954-12-31	Median	4.00
	1955-01-01 to 1955-12-31	Median	2.00
	1956-01-01 to 1956-12-31	Median	2.00
	1957-01-01 to 1957-12-31	Median	3.00
	1958-01-01 to 1958-12-31	Median	3.00
	1959-01-01 to 1959-12-31	Median	3.00
	1960-01-01 to 1960-12-31	Median	2.00
	1961-01-01 to 1961-12-31	Median	3.00
	1962-01-01 to 1962-12-31	Median	4.00
	1963-01-01 to 1963-12-31	Median	4.00
	1964-01-01 to 1964-12-31	Median	4.00
	1965-01-01 to 1965-12-31	Median	4.00
	1966-01-01 to 1966-12-31	Median	4.00
	1967-01-01 to 1967-12-31	Median	4.00
	1968-01-01 to 1968-12-31	Median	4.00
	1969-01-01 to 1969-12-31	Median	4.00
	1970-01-01 to 1970-12-31	Median	4.00
	1971-01-01 to 1971-12-31	Median	4.00
	1972-01-01 to 1972-12-31	Median	4.00
	1973-01-01 to 1973-12-31	Median	4.00
	1974-01-01 to 1974-12-31	Median	5.00
	1975-01-01 to 1975-12-31	Median	7.00
	1976-01-01 to 1976-12-31	Median	7.00
	1977-01-01 to 1977-12-31	Median	4.00
	1978-01-01 to 1978-12-31	Median	5.00
	1979-01-01 to 1979-12-31	Median	10.00
	1980-01-01 to 1980-12-31	Median	11.00
	1981-01-01 to 1981-12-31	Median	14.00
	1982-01-01 to 1982-12-31	Median	15.00
	1983-01-01 to 1983-12-31	Median	16.00
	1984-01-01 to 1984-12-31	Median	13.00
	1985-01-01 to 1985-12-31	Median	19.00
	1986-01-01 to 1986-12-31	Median	21.00
	1987-01-01 to 1987-12-31	Median	22.00
	1988-01-01 to 1988-12-31	Median	22.00
	1989-01-01 to 1989-12-31	Median	22.00
	1990-01-01 to 1990-12-31	Median	22.00
	1991-01-01 to 1991-12-31	Median	23.00
	1992-01-01 to 1992-12-31	Median	31.00
	1993-01-01 to 1993-12-31	Median	30.00
	1994-01-01 to 1994-12-31	Median	31.00
	1995-01-01 to 1995-12-31	Median	31.00
	1996-01-01 to 1996-12-31	Median	35.00
	1997-01-01 to 1997-12-31	Median	37.00
	1998-01-01 to 1998-12-31	Median	38.00
	1999-01-01 to 1999-12-31	Median	38.00
	2000-01-01 to 2000-12-31	Median	40.00
	2001-01-01 to 2001-12-31	Median	41.00
	2002-01-01 to 2002-12-31	Median	41.00
	2003-01-01 to 2003-12-31	Median	42.00
	2004-01-01 to 2004-12-31	Median	41.00
	2005-01-01 to 2005-12-31	Median	42.00
	2006-01-01 to 2006-12-31	Median	49.00
	2007-01-01 to 2007-12-31	Median	48.00
	2008-01-01 to 2008-12-31	Median	44.00
	2009-01-01 to 2009-12-31	Median	47.00
	2010-01-01 to 2010-12-31	Median	50.00
	2011-01-01 to 2011-12-31	Median	50.00
	2012-01-01 to 2012-12-31	Median	50.00
	2013-01-01 to 2013-12-31	Median	50.00
	2014-01-01 to 2014-12-31	Median	51.00
	2015-01-01 to 2015-12-31	Median	51.00
	2016-01-01 to 2016-12-31	Median	51.00
	2017-01-01 to 2017-12-31	Median	51.00
	2018-01-01 to 2018-12-31	Median	51.00
	2019-01-01 to 2019-12-31	Median	51.00
	overall	Median	24.00

Visualise trends with plotTrend

plotTrend() builds a ggplot2 visualisation from a summarised result.

result <- summariseTrend(
  cdm = cdm,
  event = "measurement", 
  interval = "quarters",
  sex = TRUE, 
  ageGroup = list(c(0, 17), c(18, Inf)),
  dateRange = as.Date(c("2010-01-01", "2019-12-31"))
)
#> → The observation period in the cdm ends in 2019-07-03

plotTrend(
  result = result,
  colour = "sex",
  facet = "age_group"
)

When the result includes several outputs (for example, records, persons, or person-days), the function defaults to plotting the number of records. You can override this by setting the output argument to the measure you want to visualise.

result <- summariseTrend(cdm,
  event = "measurement",
  interval = "quarters",
  output = c("sex", "record"),
  dateRange = as.Date(c("2010-01-01", "2019-12-31"))
)
#> → The observation period in the cdm ends in 2019-07-03
plotTrend(
  result = result,
  output = "sex"
)

You can also specify facet (formula or column) and colour. Valid column names are the tidied result columns (see visOmopResults::tidyColumns())

result <- summariseTrend(cdm,
  event = "measurement",
  interval = "quarters",
  sex = TRUE,
  inObservation = TRUE,
  dateRange = as.Date(c("2010-01-01", "2019-12-31"))
)
#> → The observation period in the cdm ends in 2019-07-03
plotTrend(
  result = result,
  facet = omop_table ~ sex,
  colour = "in_observation"
)

Disconnect from CDM

Finally, disconnect from the mock CDM.

cdmDisconnect(cdm = cdm)