Summarise observation period
2 Introduction
In this vignette, we will explore the OmopSketch functions
designed to provide an overview of the observation_period
table. Specifically, there are six key functions that facilitate
this:
summariseObservationPeriod(),plotObservationPeriod()andtableObservationPeriod(): Use them to get some overall statistics describing theobservation_periodtablesummariseInObservation(),plotInObservation(),tableInObservation(): Use them to summarise the trend in the number of records, individuals, person-days and females in observation during specific intervals of time and how the median age varies.
2.1 Create a mock cdm
Let’s see an example of its functionalities. To start with, we will load essential packages and create a mock cdm using the mockOmopSketch() database.
library(dplyr)
library(OmopSketch)
# Connect to mock database
cdm <- mockOmopSketch()3 Summarise observation periods
Let’s now use the summariseObservationPeriod() function
from the OmopSketch package to help us have an overview of one of the
observation_period table, including some statistics such as
the Number of subjects and Duration in days
for each observation period (e.g., 1st, 2nd)
summarisedResult <- summariseObservationPeriod(cdm$observation_period)
summarisedResult
#> # A tibble: 3,102 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 mockOmopSketch observation_pe… all overall overall
#> 2 1 mockOmopSketch observation_pe… all overall overall
#> 3 1 mockOmopSketch observation_pe… all overall overall
#> 4 1 mockOmopSketch observation_pe… all overall overall
#> 5 1 mockOmopSketch observation_pe… all overall overall
#> 6 1 mockOmopSketch observation_pe… all overall overall
#> 7 1 mockOmopSketch observation_pe… all overall overall
#> 8 1 mockOmopSketch observation_pe… all overall overall
#> 9 1 mockOmopSketch observation_pe… all overall overall
#> 10 1 mockOmopSketch observation_pe… all overall overall
#> # ℹ 3,092 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> # estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> # additional_name <chr>, additional_level <chr>Notice that the output is in the summarised result format.
We can use the arguments to specify which statistics we want to
perform. For example, use the argument estimates to
indicate which estimates you are interested regarding the
Duration in days of the observation period.
summarisedResult <- summariseObservationPeriod(cdm$observation_period,
estimates = c("mean", "sd", "q05", "q95")
)
summarisedResult |>
filter(variable_name == "Duration in days") |>
select(group_level, variable_name, estimate_name, estimate_value)
#> # A tibble: 8 × 4
#> group_level variable_name estimate_name estimate_value
#> <chr> <chr> <chr> <chr>
#> 1 all Duration in days mean 4735.76
#> 2 all Duration in days sd 4363.77402714669
#> 3 all Duration in days q05 145
#> 4 all Duration in days q95 13017
#> 5 1st Duration in days mean 4735.76
#> 6 1st Duration in days sd 4363.77402714669
#> 7 1st Duration in days q05 145
#> 8 1st Duration in days q95 13017Additionally, you can stratify the results by sex and age groups, and specify a date range of interest:
summarisedResult <- summariseObservationPeriod(cdm$observation_period,
estimates = c("mean", "sd", "q05", "q95"),
sex = TRUE,
ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf)),
dateRange = as.Date(c("1970-01-01", "2010-01-01"))
)
summarisedResult |>
select(group_level, variable_name, strata_level, estimate_name, estimate_value) |>
glimpse()
#> Rows: 135
#> Columns: 5
#> $ group_level <chr> "all", "all", "all", "all", "all", "all", "all", "all",…
#> $ variable_name <chr> "Number records", "Number subjects", "Records per perso…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall", …
#> $ estimate_name <chr> "count", "count", "mean", "sd", "q05", "q95", "mean", "…
#> $ estimate_value <chr> "99", "74", "1", "0", "1", "1", "3664.89898989899", "28…Notice that, by default, the “overall” group will be also included, as well as crossed strata (that means, sex == “Female” and ageGroup == “>35”).
3.1 Tidy the summarised object
tableObservationPeriod() will help you to create a table
(see supported types with: visOmopResults::tableType()). By default it
creates a gt table.
summarisedResult <- summariseObservationPeriod(cdm$observation_period,
estimates = c("mean", "sd", "q05", "q95"),
sex = TRUE
)
summarisedResult |>
tableObservationPeriod()
#> ℹ <median> [<q25> - <q75>] has not been formatted.| Observation period ordinal | Variable name | Estimate name |
CDM name
|
|---|---|---|---|
| mockOmopSketch | |||
| overall | |||
| all | Number records | N | 100 |
| Number subjects | N | 100 | |
| Records per person | mean (sd) | 1.00 (0.00) | |
| Duration in days | mean (sd) | 4,735.76 (4,363.77) | |
| 1st | Number subjects | N | 100 |
| Duration in days | mean (sd) | 4,735.76 (4,363.77) | |
| Female | |||
| all | Number records | N | 59 |
| Number subjects | N | 59 | |
| Records per person | mean (sd) | 1.00 (0.00) | |
| Duration in days | mean (sd) | 4,630.83 (4,556.62) | |
| 1st | Number subjects | N | 59 |
| Duration in days | mean (sd) | 4,630.83 (4,556.62) | |
| Male | |||
| all | Number records | N | 41 |
| Number subjects | N | 41 | |
| Records per person | mean (sd) | 1.00 (0.00) | |
| Duration in days | mean (sd) | 4,886.76 (4,121.24) | |
| 1st | Number subjects | N | 41 |
| Duration in days | mean (sd) | 4,886.76 (4,121.24) | |
3.2 Visualise the results
Finally, we can visualise the result using
plotObservationPeriod().
summarisedResult <- summariseObservationPeriod(cdm$observation_period)
plotObservationPeriod(summarisedResult,
variableName = "Number subjects",
plotType = "barplot"
)
Note that either Number subjects or
Duration in days can be plotted. For
Number of subjects, the plot type can be
barplot, whereas for Duration in days, the
plot type can be barplot, boxplot, or
densityplot.”
Additionally, if results were stratified by sex or age group, we can
further use facet or colour arguments to
highlight the different results in the plot. To help us identify by
which variables we can colour or facet by, we can use visOmopResult
package.
summarisedResult <- summariseObservationPeriod(cdm$observation_period,
sex = TRUE
)
plotObservationPeriod(summarisedResult,
variableName = "Duration in days",
plotType = "boxplot",
facet = "sex"
)
summarisedResult <- summariseObservationPeriod(cdm$observation_period,
sex = TRUE,
ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf))
)
plotObservationPeriod(summarisedResult,
colour = "sex",
facet = "age_group"
)
4 Summarise in observation
OmopSketch can also help you to summarise the number of records in observation during specific intervals of time.
summarisedResult <- summariseInObservation(cdm$observation_period,
interval = "years"
)
summarisedResult |>
select(variable_name, estimate_name, estimate_value, additional_name, additional_level)
#> # A tibble: 136 × 5
#> variable_name estimate_name estimate_value additional_name additional_level
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Number records… count 1 time_interval 1952-01-01 to 1…
#> 2 Number records… count 2 time_interval 1953-01-01 to 1…
#> 3 Number records… count 2 time_interval 1954-01-01 to 1…
#> 4 Number records… count 2 time_interval 1955-01-01 to 1…
#> 5 Number records… count 2 time_interval 1956-01-01 to 1…
#> 6 Number records… count 2 time_interval 1957-01-01 to 1…
#> 7 Number records… count 2 time_interval 1958-01-01 to 1…
#> 8 Number records… count 2 time_interval 1959-01-01 to 1…
#> 9 Number records… count 2 time_interval 1960-01-01 to 1…
#> 10 Number records… count 2 time_interval 1961-01-01 to 1…
#> # ℹ 126 more rowsNote that you can adjust the time interval period using the
interval argument, which can be set to either “years”,
“quarters”, “months” or “overall” (default value).
summarisedResult <- summariseInObservation(cdm$observation_period,
interval = "months"
)
summarisedResult |>
select(variable_name, estimate_name, estimate_value, additional_name, additional_level)
#> # A tibble: 1,630 × 5
#> variable_name estimate_name estimate_value additional_name additional_level
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Number records… count 1 time_interval 1952-02-01 to 1…
#> 2 Number records… count 1 time_interval 1952-03-01 to 1…
#> 3 Number records… count 1 time_interval 1952-04-01 to 1…
#> 4 Number records… count 1 time_interval 1952-05-01 to 1…
#> 5 Number records… count 1 time_interval 1952-06-01 to 1…
#> 6 Number records… count 1 time_interval 1952-07-01 to 1…
#> 7 Number records… count 1 time_interval 1952-08-01 to 1…
#> 8 Number records… count 1 time_interval 1952-09-01 to 1…
#> 9 Number records… count 1 time_interval 1952-10-01 to 1…
#> 10 Number records… count 1 time_interval 1952-11-01 to 1…
#> # ℹ 1,620 more rowsAlong with the number of records in observation, you can also
calculate the number of person-days by setting the output
argument to c(“record”, “person-days”).
summarisedResult <- summariseInObservation(cdm$observation_period,
output = c("record", "person-days"))
summarisedResult |>
select(variable_name, estimate_name, estimate_value, additional_name, additional_level)
#> # A tibble: 4 × 5
#> variable_name estimate_name estimate_value additional_name additional_level
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Number person-d… count 473576 overall overall
#> 2 Number records … count 100 overall overall
#> 3 Number person-d… percentage 100.00 overall overall
#> 4 Number records … percentage 100.00 overall overallWe can further stratify our counts by sex (setting argument
sex = TRUE) or by age (providing an age group). Notice that
in both cases, the function will automatically create a group called
overall with all the sex groups and all the age groups. We can
also define a date range of interest to filter the
observation_period table accordingly.
summarisedResult <- summariseInObservation(cdm$observation_period,
output = c("record", "person-days"),
interval = "quarters",
sex = TRUE,
ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf)),
dateRange = as.Date(c("1970-01-01", "2010-01-01")))
summarisedResult |>
select(strata_level, variable_name, estimate_name, estimate_value, additional_name, additional_level)
#> # A tibble: 1,676 × 6
#> strata_level variable_name estimate_name estimate_value additional_name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Male &&& <35 Number person-da… count 90 time_interval
#> 2 Male Number person-da… count 90 time_interval
#> 3 Female Number person-da… count 90 time_interval
#> 4 Female &&& <35 Number person-da… count 90 time_interval
#> 5 Female Number records i… count 1 time_interval
#> 6 Male Number records i… count 1 time_interval
#> 7 Female &&& <35 Number records i… count 1 time_interval
#> 8 Male &&& <35 Number records i… count 1 time_interval
#> 9 Male &&& <35 Number person-da… count 92 time_interval
#> 10 Male Number person-da… count 92 time_interval
#> # ℹ 1,666 more rows
#> # ℹ 1 more variable: additional_level <chr>You can include additional output metrics by them to the output argument:
If output = "person", the trend in the number of
individuals in observation is returned.
summarisedResult <- summariseInObservation(cdm$observation_period,
output = c("person"),
interval = "years",
sex = TRUE,
ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf)),
)
summarisedResult |>
select(strata_level, variable_name, estimate_name, estimate_value, additional_name, additional_level)
#> # A tibble: 986 × 6
#> strata_level variable_name estimate_name estimate_value additional_name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Female &&& <35 Number subjects … count 1 time_interval
#> 2 Female Number subjects … count 1 time_interval
#> 3 Female &&& <35 Number subjects … count 2 time_interval
#> 4 Female Number subjects … count 2 time_interval
#> 5 Female &&& <35 Number subjects … count 2 time_interval
#> 6 Female Number subjects … count 2 time_interval
#> 7 Female Number subjects … count 2 time_interval
#> 8 Female &&& <35 Number subjects … count 2 time_interval
#> 9 Female Number subjects … count 2 time_interval
#> 10 Female &&& <35 Number subjects … count 2 time_interval
#> # ℹ 976 more rows
#> # ℹ 1 more variable: additional_level <chr>If output = "sex", the trend in the number of females in
observation is returned. If sex = TRUE is specified, this
stratification is ignored.
summarisedResult <- summariseInObservation(cdm$observation_period,
output = c("sex"),
interval = "years",
sex = TRUE,
ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf)),
)
summarisedResult |>
select(strata_level, variable_name, estimate_name, estimate_value, additional_name, additional_level)
#> # A tibble: 342 × 6
#> strata_level variable_name estimate_name estimate_value additional_name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 <35 Number females in … count 1 time_interval
#> 2 overall Number females in … count 1 time_interval
#> 3 <35 Number females in … count 2 time_interval
#> 4 overall Number females in … count 2 time_interval
#> 5 <35 Number females in … count 2 time_interval
#> 6 overall Number females in … count 2 time_interval
#> 7 overall Number females in … count 2 time_interval
#> 8 <35 Number females in … count 2 time_interval
#> 9 <35 Number females in … count 2 time_interval
#> 10 overall Number females in … count 2 time_interval
#> # ℹ 332 more rows
#> # ℹ 1 more variable: additional_level <chr>If output = "age, the trend in the median age of the
population in observation is calculated. If ageGroup and
interval are both specified, the age is computed at the
beginning of the interval or of the observation period, whichever is
more recent.
summarisedResult <- summariseInObservation(cdm$observation_period,
output = c("age"),
interval = "years",
ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf)),
)
#> Warning: Missing values are always removed in SQL aggregation functions.
#> Use `na.rm = TRUE` to silence this warning
#> This warning is displayed once every 8 hours.
summarisedResult |>
select(strata_level, variable_name, estimate_name, estimate_value, additional_name, additional_level)
#> # A tibble: 171 × 6
#> strata_level variable_name estimate_name estimate_value additional_name
#> <chr> <chr> <chr> <chr> <chr>
#> 1 overall Median age in obse… median 2 time_interval
#> 2 <35 Median age in obse… median 2 time_interval
#> 3 <35 Median age in obse… median 1 time_interval
#> 4 overall Median age in obse… median 1 time_interval
#> 5 <35 Median age in obse… median 2 time_interval
#> 6 overall Median age in obse… median 2 time_interval
#> 7 overall Median age in obse… median 3 time_interval
#> 8 <35 Median age in obse… median 3 time_interval
#> 9 <35 Median age in obse… median 4 time_interval
#> 10 overall Median age in obse… median 4 time_interval
#> # ℹ 161 more rows
#> # ℹ 1 more variable: additional_level <chr>4.1 Tidy the summarised object
tableInObservartion() will help you to create a table of
type gt, reactable or datatable. By default it
creates a gt table.
summarisedResult <- summariseInObservation(cdm$observation_period,
output = c("person", "person-days", "sex"),
sex = TRUE)
summarisedResult |>
tableInObservation(type = "gt")| Variable name | Estimate name | Sex |
Database name
|
|---|---|---|---|
| mockOmopSketch | |||
| Number person-days | N (%) | Female | 273219 (57.69%) |
| Number subjects in observation | N (%) | Female | 59 (59.00%) |
| Number person-days | N (%) | Male | 200357 (42.31%) |
| Number subjects in observation | N (%) | Male | 41 (41.00%) |
| Number females in observation | N (%) | overall | 59 (59.00%) |
| Number person-days | N (%) | overall | 473576 (100.00%) |
| Number subjects in observation | N (%) | overall | 100 (100.00%) |
4.2 Visualise the results
Finally, we can visualise the trend using
plotInObservation().
summarisedResult <- summariseInObservation(cdm$observation_period,
interval = "years"
)
plotInObservation(summarisedResult)
#> `result_id` is not present in result.
#> `result_id` is not present in result.
Notice that one output at a time can be plotted. If more outputs have been included in the summarised result, you will have to filter to only include one variable at time.
Additionally, if results were stratified by sex or age group, we can
further use facet or colour arguments to
highlight the different results in the plot. To help us identify by
which variables we can colour or facet by, we can use visOmopResult
package.
summarisedResult <- summariseInObservation(cdm$observation_period,
interval = "years",
output = c("record", "age"),
sex = TRUE,
ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf)))
plotInObservation(summarisedResult |>
filter(variable_name == "Median age in observation"),
colour = "sex",
facet = "age_group")
#> `result_id` is not present in result.
#> `result_id` is not present in result.
Finally, disconnect from the cdm
PatientProfiles::mockDisconnect(cdm = cdm)