Function reference • PatientProfiles

Add individual patient characteristics

Add patient characteristics to a table in the OMOP Common Data Model

addAge(): Compute the age of the individuals at a certain date

addDateOfBirth(): Add a column with the individual birth date

addFutureObservation(): Compute the number of days till the end of the observation period at a certain date

addInObservation(): Indicate if a certain record is within the observation period

addPriorObservation(): Compute the number of days of prior observation in the current observation period at a certain date

addSex(): Compute the sex of the individuals

Add multiple individual patient characteristics

Add a set of patient characteristics to a table in the OMOP Common Data Model

addDemographics(): Compute demographic characteristics at a certain date

Add death information

Add patient death information to a table in the OMOP Common Data Model

addDeathDate(): Add date of death for individuals. Only death within the same observation period than `indexDate` will be observed.

addDeathDays(): Add days to death for individuals. Only death within the same observation period than `indexDate` will be observed.

addDeathFlag(): Add flag for death for individuals. Only death within the same observation period than `indexDate` will be observed.

Add a value from a cohort intersection

Add a variable indicating the intersection between a table in the OMOP Common Data Model and a cohort table.

addCohortIntersectCount(): It creates columns to indicate number of occurrences of intersection with a cohort

addCohortIntersectDate(): Date of cohorts that are present in a certain window

addCohortIntersectDays(): It creates columns to indicate the number of days between the current table and a target cohort

addCohortIntersectFlag(): It creates columns to indicate the presence of cohorts

Add multiple values from a cohort intersection

Add multiple variables indicating the intersection between a table in the OMOP Common Data Model and a cohort table.

addCohortIntersect(): Compute the intersect with a target cohort, you can compute the number of occurrences, a flag of presence, a certain date and/or the time difference

Add a value from a concept intersection

Add a variable indicating the intersection between a table in the OMOP Common Data Model and a concept.

addConceptIntersectCount(): It creates column to indicate the count overlap information between a table and a concept

addConceptIntersectDate(): It creates column to indicate the date overlap information between a table and a concept

addConceptIntersectDays(): It creates column to indicate the days of difference from an index date to a concept

addConceptIntersectFlag(): It creates column to indicate the flag overlap information between a table and a concept

Add multiple values from a concept intersection

Add multiple variables indicating the intersection between a table in the OMOP Common Data Model and a concept.

addConceptIntersect(): It creates columns to indicate overlap information between a table and a concept

Add a value from an omop standard table intersection

Add a variable indicating the intersection between a table in the OMOP Common Data Model and a standard omop table.

addTableIntersectCount(): Compute number of intersect with an omop table.

addTableIntersectDate(): Compute date of intersect with an omop table.

addTableIntersectDays(): Compute time to intersect with an omop table.

addTableIntersectField(): Intersecting the cohort with columns of an OMOP table of user's choice. It will add an extra column to the cohort, indicating the intersected entries with the target columns in a window of the user's choice.

addTableIntersectFlag(): Compute a flag intersect with an omop table.

Add multiple values from an omop standard table intersection

Add multiple variables indicating the intersection between a table in the OMOP Common Data Model and a standard omop table.

addTableIntersect(): Compute the intersect with an omop table, you can compute the number of occurrences, a flag of presence, a certain date, the time difference and/or obtain a certain column.

Add multiple values from table intersection

Add multiple variables indicating the intersection between two tablea in the OMOP Common Data Model.

addIntersect(): It creates columns to indicate overlap information between two tables

Add multiple columns from large scale characteristics

Add multiple variables indicating the intersection between concepts in tables in the OMOP Common Data Model.

addLargeScaleCharacteristics(): This function is used to add columns with the large scale characteristics of a cohort table.

Summarise patient characteristics

Function that allow the user to summarise patient characteristics (characteristics must be added priot the use of the function)

summariseResult(): Summarise variables using a set of estimate functions. The output will be a formatted summarised_result object.

Summarise patient characteristic standard function

Function that allow the user to summarise patient characteristics based on some paramters

summariseCharacteristics(): Summarise characteristics of individuals

summariseCohortCounts(): Summarise counts for each different cohort. You can add a list of stratifications.

summariseCohortIntersect(): Summarise cohort intersection information

summariseConceptIntersect(): Summarise concept intersect with a cohort_table

summariseDemographics(): Summarise demographics of individuals

summariseTableIntersect(): Summarise table intersection information

Summarise patient large scale characteristics

Summarise large scale characteristics

summariseLargeScaleCharacteristics(): This function is used to summarise the large scale characteristics of a cohort table

Summarise overlap between cohorts

Summarise overlap between cohorts in the same table

summariseCohortOverlap(): Summarise cohort overlap

Summarise time between cohorts

Summarise time between patients entry dates in cohorts

summariseCohortTiming(): Summarise cohort timing

Tidy functions to display results in a more easy to manage table

Functions to modify the summarised_result objects into more easy to manage tibbles

Format summarised_result into a more visual and exportable format (gt, flextables, …)

Functions to format the summarised_result objects into gt and flextables object that latter can be exported as html, docx, pdf, … Ideal for shinyapps and reports.

formatCharacteristics(): Format a summarised_characteristics object into a visual table.

tableCharacteristics(): Format a summarised_characteristics object into a visual table.

tableCohortIntersect(): Format a summariseCohortIntersect result into a visual table.

tableCohortOverlap(): Format a summariseOverlapCohort result into a visual table.

tableCohortTiming(): Format a summariseCohortTiming result into a visual table.

tableDemographics(): Format a summariseDemographics result into a visual table.

tableLargeScaleCharacteristics(): Format a summarised_large_scale_characteristics object into a visual table.

tableTableIntersect(): Format a summariseTableIntersect result into a visual table.

Generate ggplots from summarised_result objects

Functions to generate ggplots from the different summarised_result objects.

plotCharacteristics(): Create a ggplot from the output of summariseCharacteristics. `r lifecycle::badge("deprecated")`

plotCohortIntersect(): Plot summariseCohortIntersect output.

plotCohortOverlap(): Plot the result of summariseCohortOverlap.

plotCohortTiming(): Plot summariseCohortTiming results.

plotDemographics(): Plot summariseDemographics output.

plotLargeScaleCharacteristics(): create a ggplot from the output of summariseLargeScaleCharacteristics.

plotTableIntersect(): Plot summariseTableIntersect output.

Other functions

Helper functions

addCategories(): Categorize a numeric variable

addCdmName(): Add cdm name

addCohortName(): Add cohort name for each cohort_definition_id

assertNameStyle(): Assert whether a nameStyle contains the needed information.

availableEstimates(): Show the available estimates that can be used for the different variable_type supported.

availableFunctions(): Show the available functions for the 4 classifications of data that are supported (numeric, date, binary and categorical)

endDateColumn(): Get the name of the end date column for a certain table in the cdm

gtCharacteristics(): Create a gt table from a summarisedCharacteristics object.

gtResult(): Create a gt table from a summary object.

mockPatientProfiles(): It creates a mock database for testing PatientProfiles package

optionsTableCharacteristics(): Additional arguments for the function tableCharacteristics.

optionsTableCohortOverlap(): Additional arguments for the function tableCohortOverlap.

optionsTableCohortTiming(): Additional arguments for the function tableCohortTiming.

sourceConceptIdColumn(): Get the name of the source concept_id column for a certain table in the cdm

standardConceptIdColumn(): Get the name of the standard concept_id column for a certain table in the cdm

startDateColumn(): Get the name of the start date column for a certain table in the cdm

variableTypes(): Classify the variables between 5 types: "numeric", "categorical", "binary", "date", or NA.