Package 'CohortSurvival'

Add time and event status to a cohort table

Description

Add the columns needed by standard survival modelling functions, such as survival::Surv(), to an OMOP cohort table. This is a lower-level helper: it creates time and status but does not fit a Kaplan-Meier curve or return a summarised_result.

Usage

addCohortSurvival(
  x,
  cdm,
  outcomeCohortTable,
  outcomeCohortId = 1,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  followUpDays = Inf,
  name = NULL
)

Arguments

x	Cohort table to add survival information to.
cdm	CDM reference created by CDMConnector.
outcomeCohortTable	Name of the cohort table containing the outcome of interest.
outcomeCohortId	ID of event cohorts to include. Only one outcome (and so one ID) can be considered. It can either be a cohort_definition_id value or a cohort_name.
outcomeDateVariable	Variable containing date of outcome event. This is usually "cohort_start_date".
outcomeWashout	Washout time in days for the outcome. If an individual has an outcome during the washout period before target cohort entry, status and time will be set to NA. Use Inf for any prior outcome and 0 for no pre-index washout.
censorOnCohortExit	If TRUE, an individual's follow up will be censored at their target cohort exit.
censorOnDate	If not NULL, an individual's follow up will be censored at the given date. This can be a scalar Date or the name of a date column in x.
followUpDays	Number of days to follow up individuals (lower bound 1, upper bound Inf). Follow-up is censored at this value.
name	Name of the new table, if NULL a temporary table is returned.

Details

time is the number of days from target cohort entry to the first applicable event or censoring date. Censoring can occur at the end of observation, at target cohort exit when censorOnCohortExit = TRUE, at censorOnDate, or at followUpDays. status is 1 for people with the outcome event and 0 for censored records. Records with an outcome in the washout window are kept in the table with time and status set to NA, so they can be removed by downstream analyses.

Value

A cohort table with two additional columns. The time column contains the number of days to event or censoring. The status column indicates whether the patient had the event (1) or was censored (0).

Examples

cdm <- mockMGUS2cdm()
cdm$mgus_diagnosis <- cdm$mgus_diagnosis |>
  addCohortSurvival(
    cdm = cdm,
    outcomeCohortTable = "death_cohort",
    outcomeCohortId = 1
  )

cdm$mgus_diagnosis |>
  dplyr::select(subject_id, cohort_start_date, time, status) |>
  dplyr::collect()

---

Convert survival summarised results to a survival-specific format

Description

Convert the long omopgenerics::summarised_result returned by estimateSingleEventSurvival() or estimateCompetingRiskSurvival() into a wider survival_result object that is easier to inspect manually. The main object contains time-specific estimates when available. Event counts, summary statistics, and attrition are stored as attributes named "events", "summary", and "attrition".

Usage

asSurvivalResult(result)

Arguments

result

A summarised_result object.

Details

The plotting and table functions in CohortSurvival accept both formats. The original summarised_result is usually preferable for exporting, binding with other omopgenerics results, and reporting through visOmopResults.

Value

A survival_result object.

Examples

cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(
  cdm = cdm,
  targetCohortTable = "mgus_diagnosis",
  targetCohortId = 1,
  outcomeCohortTable = "death_cohort",
  outcomeCohortId = 1,
  eventGap = 7
) |>
asSurvivalResult()

---

Variables that can be used for faceting and colouring survival plots

Description

Variables that can be used for faceting and colouring survival plots

Usage

availableSurvivalGrouping(result, varying = FALSE)

Arguments

result	Survival results
varying	If FALSE (default), only variables with non-unique values will be returned, otherwise all available variables will be returned.

Examples

cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(cdm,
                                    targetCohortTable = "mgus_diagnosis",
                                    outcomeCohortTable = "death_cohort")
availableSurvivalGrouping(surv)

---

Estimate cumulative incidence with a competing outcome

Description

Estimate time-to-event probabilities for one or more target cohorts when an event of interest can be precluded by a competing outcome. The target cohort defines the population at risk and the index date for follow-up. The outcome cohort defines the event of interest and the competing outcome cohort defines the event that prevents the event of interest from subsequently occurring.

Usage

estimateCompetingRiskSurvival(
  cdm,
  targetCohortTable,
  outcomeCohortTable,
  competingOutcomeCohortTable,
  targetCohortId = NULL,
  outcomeCohortId = NULL,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  competingOutcomeCohortId = NULL,
  competingOutcomeDateVariable = "cohort_start_date",
  competingOutcomeWashout = Inf,
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  weight = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  restrictedMeanFollowUp = NULL,
  minimumSurvivalDays = 1
)

Arguments

cdm	A CDM reference created by CDMConnector.
targetCohortTable	Name of the cohort table containing the target cohorts. The table must be present in cdm and contain standard OMOP cohort columns.
outcomeCohortTable	Name of the cohort table containing the outcome of interest.
competingOutcomeCohortTable	Name of the cohort table containing the competing outcome.
targetCohortId	Target cohorts to include. It can either be a cohort_definition_id value or a cohort_name. Multiple ids are allowed. If NULL, all non-empty cohorts in targetCohortTable are used.
outcomeCohortId	Outcome cohorts to include. It can either be a cohort_definition_id value or a cohort_name. Multiple ids are allowed. If NULL, all outcome cohorts in outcomeCohortTable are used.
outcomeDateVariable	Variable containing the outcome event date. This is usually "cohort_start_date", but another date column in the outcome cohort can be used.
outcomeWashout	Number of days before target cohort entry used to exclude people with a prior outcome. Inf excludes people with any prior outcome before index; 0 applies no pre-index washout.
competingOutcomeCohortId	Competing outcome cohorts to include. It can either be a cohort_definition_id value or a cohort_name. Multiple ids are allowed. If NULL, all competing outcome cohorts in competingOutcomeCohortTable are used.
competingOutcomeDateVariable	Variable containing the competing outcome event date.
competingOutcomeWashout	Number of days before target cohort entry used to exclude people with a prior competing outcome. Inf excludes people with any prior competing outcome before index; 0 applies no pre-index washout.
censorOnCohortExit	If TRUE, an individual's follow up will be censored at their target cohort exit date.
censorOnDate	If not NULL, an individual's follow up will be censored at the given date. This can be a scalar Date or the name of a date column in the target cohort table.
weight	If not NULL, the name of a numeric column in the target cohort table containing observation weights.
followUpDays	Number of days to follow up individuals (lower bound 1, upper bound Inf). Follow-up is censored at this value.
strata	A list of target cohort column names to stratify by. Each element can be one column name or a character vector of column names for a combined stratum, for example list("sex", c("age_group", "sex")).
eventGap	Days between time points for which to report survival events, which are grouped into the specified intervals.
estimateGap	Days between time points for which to report survival estimates. First day will be day zero with risk estimates provided for times up to the end of follow-up, with a gap in days equivalent to estimateGap.
restrictedMeanFollowUp	Number of days of follow-up to use when calculating restricted mean summaries. See Details.
minimumSurvivalDays	Minimum number of days required for the main cohort to contribute to the analysis.

Details

The estimates from competing-risk analyses should be interpreted as cumulative incidence probabilities for the outcome and competing outcome, not as ordinary Kaplan-Meier survival probabilities. The returned object is an omopgenerics::summarised_result containing cumulative incidence estimates, event counts, summary statistics, and attrition. Use asSurvivalResult() for a wider, survival-specific view.

restrictedMeanFollowUp defines the time horizon used for the restricted mean summary. If restrictedMeanFollowUp = NULL, the horizon is left to the underlying survival summary. In stratified analyses, this can use a common maximum follow-up time across the fitted curves. A stratum with shorter observed follow-up may therefore have its last estimate carried forward and integrated beyond its own maximum follow-up. This means restricted mean summaries can be larger than the observed follow-up time for that stratum, and comparisons across strata may be misleading. Set a common clinically meaningful value that is supported by follow-up in all groups when restricted means will be compared across cohorts or strata. If the requested horizon is beyond the available follow-up for a curve, the restricted mean is reported as missing.

Value

An omopgenerics::summarised_result object with result types survival_estimates, survival_events, survival_summary, and survival_attrition when available.

Examples

cdm <- mockMGUS2cdm()
surv <- estimateCompetingRiskSurvival(
  cdm = cdm,
  targetCohortTable = "mgus_diagnosis",
  targetCohortId = 1,
  outcomeCohortTable = "progression",
  outcomeCohortId = 1,
  competingOutcomeCohortTable = "death_cohort",
  competingOutcomeCohortId = 1,
  eventGap = 7
)

---

Estimate survival for a single event of interest

Description

Estimate Kaplan-Meier survival for one or more target cohorts and outcome cohorts in an OMOP Common Data Model reference. The target cohort defines the population at risk and the index date for follow-up. The outcome cohort defines the event of interest.

Usage

estimateSingleEventSurvival(
  cdm,
  targetCohortTable,
  outcomeCohortTable,
  targetCohortId = NULL,
  outcomeCohortId = NULL,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  weight = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  restrictedMeanFollowUp = NULL,
  minimumSurvivalDays = 1
)

Arguments

cdm	A CDM reference created by CDMConnector.
targetCohortTable	Name of the cohort table containing the target cohorts. The table must be present in cdm and contain standard OMOP cohort columns.
outcomeCohortTable	Name of the cohort table containing the outcome cohorts. The table must be present in cdm and contain standard OMOP cohort columns.
targetCohortId	Target cohorts to include. It can either be a cohort_definition_id value or a cohort_name. Multiple ids are allowed. If NULL, all non-empty cohorts in targetCohortTable are used.
outcomeCohortId	Outcome cohorts to include. It can either be a cohort_definition_id value or a cohort_name. Multiple ids are allowed. If NULL, all outcome cohorts in outcomeCohortTable are used.
outcomeDateVariable	Variable containing the outcome event date. This is usually "cohort_start_date", but another date column in the outcome cohort can be used.
outcomeWashout	Number of days before target cohort entry used to exclude people with a prior outcome. Inf excludes people with any prior outcome before index; 0 applies no pre-index washout.
censorOnCohortExit	If TRUE, an individual's follow up will be censored at their target cohort exit date.
censorOnDate	If not NULL, an individual's follow up will be censored at the given date. This can be a scalar Date or the name of a date column in the target cohort table.
weight	If not NULL, the name of a numeric column in the target cohort table containing observation weights to use in the Kaplan-Meier estimation.
followUpDays	Number of days to follow up individuals (lower bound 1, upper bound Inf). Follow-up is censored at this value.
strata	A list of target cohort column names to stratify by. Each element can be one column name or a character vector of column names for a combined stratum, for example list("sex", c("age_group", "sex")).
eventGap	Days between time points for which to report survival events, which are grouped into the specified intervals.
estimateGap	Days between time points for which to report survival estimates. First day will be day zero with risk estimates provided for times up to the end of follow-up, with a gap in days equivalent to estimateGap.
restrictedMeanFollowUp	Number of days of follow-up to use when calculating restricted mean survival. See Details.
minimumSurvivalDays	Minimum number of days required for the main cohort to contribute to the analysis.

Details

The returned object is an omopgenerics::summarised_result containing survival estimates, event counts, summary statistics, and attrition. Use asSurvivalResult() when you want a wider, survival-specific view for manual inspection or downstream modelling.

restrictedMeanFollowUp defines the time horizon used for the restricted mean survival time. It is calculated as the area under the survival curve up to that horizon. If restrictedMeanFollowUp = NULL, the horizon is left to the underlying survival summary. In stratified analyses, this can use a common maximum follow-up time across the fitted curves. A stratum with shorter observed follow-up may therefore have its last survival estimate carried forward and integrated beyond its own maximum follow-up. This means restricted mean survival can be larger than the observed follow-up time for that stratum, and comparisons across strata may be misleading. Set a common clinically meaningful value that is supported by follow-up in all groups when restricted mean survival will be compared across cohorts or strata. If the requested horizon is beyond the available follow-up for a curve, the restricted mean is reported as missing.

Value

An omopgenerics::summarised_result object with result types survival_estimates, survival_events, survival_summary, and survival_attrition when available.

Examples

cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(
  cdm = cdm,
  targetCohortTable = "mgus_diagnosis",
  targetCohortId = 1,
  outcomeCohortTable = "death_cohort",
  outcomeCohortId = 1,
  eventGap = 7
)

---

Create mock CDM reference with survival::mgus2 dataset

Description

Create mock CDM reference with survival::mgus2 dataset

Usage

mockMGUS2cdm()

Value

CDM reference containing data from the survival::mgus2 dataset

Examples

cdm <- mockMGUS2cdm()
cdm$person

---

Additional arguments for the function tableSurvival()

Description

It provides a list of allowed inputs for .option argument in tableSurvival and their given default value.

Usage

optionsTableSurvival()

Value

The default .options named list.

Examples

{
optionsTableSurvival()
}

---

Plot survival or cumulative incidence results

Description

Plot the time-specific estimates returned by estimateSingleEventSurvival() or estimateCompetingRiskSurvival(). Single-event results are plotted as survival probabilities by default and can be displayed as cumulative failure with cumulativeFailure = TRUE. Competing-risk results are cumulative incidence estimates and therefore require cumulativeFailure = TRUE.

Usage

plotSurvival(
  result,
  ribbon = TRUE,
  facet = NULL,
  colour = NULL,
  cumulativeFailure = FALSE,
  riskTable = FALSE,
  riskInterval = 30,
  logLog = FALSE,
  timeScale = "days",
  type = NULL,
  style = NULL
)

Arguments

result	Survival results. A summarised_result or survival_result is accepted.
ribbon	If TRUE, add a ribbon using the confidence interval columns.
facet	Variables to use for facets.
colour	Variables to use for colours.
cumulativeFailure	Whether to plot the cumulative failure probability instead of the survival probability.
riskTable	Whether to print risk table below the plot.
riskInterval	Interval of time to print risk table below the plot. This should be compatible with the eventGap used when estimating the result.
logLog	If TRUE, the survival probabilities are transformed using the log-log formula.
timeScale	The scale of time in the x-axis. Can be "days", "months", or "years".
type	Character string specifying the desired plot type. See visOmopResults::scatterPlot() for supported types. If type = NULL, the default backend plot type is used.
style	A character string defining the visual theme to apply to the plot. You can set this to NULL to apply the standard ggplot2 default style, or provide a name for one of the package's pre-defined styles. Refer to the plotStyle() function for all available style pre-defined themes. For further customization, you can always modify the returned ggplot object directly.

Details

facet and colour should refer to columns available after converting the result with asSurvivalResult(), for example target_cohort, outcome, competing_outcome, variable, or strata columns such as sex.

Value

A plot of survival probabilities or cumulative incidence probabilities over time.

Examples

cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(cdm,
                                    targetCohortTable = "mgus_diagnosis",
                                    outcomeCohortTable = "death_cohort")
plotSurvival(surv)

---

Table with survival events

Description

riskTable() is kept for backwards compatibility. Use tableSurvivalEvents() in new code.

Usage

riskTable(
  x,
  eventGap = NULL,
  header = c("estimate"),
  type = "gt",
  groupColumn = NULL,
  hide = c("result_id", "estimate_type"),
  style = NULL,
  .options = list()
)

Arguments

x	Result from estimateSingleEventSurvival or estimateCompetingRiskSurvival.
eventGap	Event gap defining the times at which to report the risk table information. Must be one of the eventGap inputs used for the estimation function. If NULL, all available are reported.
header	A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. Elements in header can be: Any of the columns returned by tableColumns(result) to create a header for these columns. Any other input to create an overall header.
type	Character string specifying the desired output table format. See tableType() for supported table types. If type = NULL, global options (set via setGlobalTableOptions()) will be used if available; otherwise, a default 'gt' table is created.
groupColumn	Columns to use as group labels, to see options use tableColumns(result). By default, the name of the new group will be the tidy* column names separated by ";". To specify a custom group name, use a named list such as: list("newGroupName" = c("variable_name", "variable_level")). *tidy: The tidy format applied to column names replaces "_" with a space and converts to sentence case. Use rename to customise specific column names.
hide	Columns to drop from the output table. By default, result_id and estimate_type are always dropped.
style	Defines the visual formatting of the table. This argument can be provided in one of the following ways: Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See tableStyle() for available options. YAML file path: Provide the path to an existing .yml file defining a new style. List of custom R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type. If style = NULL, the function will use global options (see setGlobalTableOptions()) or an existing ⁠_brand.yml⁠ file (if found); otherwise, the default style is applied. For more details, see the Styles vignette on the package website.
.options	A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A tibble containing the risk table information (n_risk, n_events, n_censor) for all times within the event gap specified.

Examples

cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(cdm,
                                    targetCohortTable = "mgus_diagnosis",
                                    outcomeCohortTable = "death_cohort")
riskTable(surv)

---

Table with survival summary

Description

Create a formatted table from the summary and, optionally, time-specific estimate result types returned by estimateSingleEventSurvival() or estimateCompetingRiskSurvival(). For single-event analyses, time-specific rows are survival probabilities. For competing-risk analyses, they are cumulative incidence probabilities for the outcome and competing outcome.

Usage

tableSurvival(
  x,
  times = NULL,
  timeScale = "days",
  header = c("estimate"),
  estimates = c("median_survival", "restricted_mean_survival"),
  type = "gt",
  groupColumn = NULL,
  hide = c("result_id", "estimate_type"),
  style = NULL,
  .options = list()
)

Arguments

x	Result from estimateSingleEventSurvival() or estimateCompetingRiskSurvival(). A summarised_result or survival_result is accepted.
times	Times at which to report estimates in the summary table. These must match available estimate times after applying timeScale; unavailable times are omitted with a message.
timeScale	Time unit to report survival in: days, months, or years.
header	A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. Elements in header can be: Any of the columns returned by tableColumns(result) to create a header for these columns. Any other input to create an overall header.
estimates	Character vector specifying which estimates to include in the table. Options include: "median_survival", "restricted_mean_survival", "q0_survival", "q05_survival", "q25_survival", "q75_survival", "q95_survival", "q100_survival". By default it includes c("median_survival", "restricted_mean_survival").
type	Character string specifying the desired output table format. See tableType() for supported table types. If type = NULL, global options (set via setGlobalTableOptions()) will be used if available; otherwise, a default 'gt' table is created.
groupColumn	Columns to use as group labels, to see options use tableColumns(result). By default, the name of the new group will be the tidy* column names separated by ";". To specify a custom group name, use a named list such as: list("newGroupName" = c("variable_name", "variable_level")). *tidy: The tidy format applied to column names replaces "_" with a space and converts to sentence case. Use rename to customise specific column names.
hide	Columns to drop from the output table. By default, result_id and estimate_type are always dropped.
style	Defines the visual formatting of the table. This argument can be provided in one of the following ways: Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See tableStyle() for available options. YAML file path: Provide the path to an existing .yml file defining a new style. List of custom R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type. If style = NULL, the function will use global options (see setGlobalTableOptions()) or an existing ⁠_brand.yml⁠ file (if found); otherwise, the default style is applied. For more details, see the Styles vignette on the package website.
.options	A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Details

Restricted mean survival is taken from the estimation output. Its interpretation depends on the restrictedMeanFollowUp value used when the survival result was estimated; use a common value there when comparing restricted means across groups or strata.

Value

A formatted table containing a summary of observed survival or cumulative incidence in the required units.

Examples

cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(cdm,
                                    targetCohortTable = "mgus_diagnosis",
                                    outcomeCohortTable = "death_cohort")
tableSurvival(surv, times = c(50,100,365))

---

Display the attrition of a survival result in a visual table

Description

Display the attrition of a survival result in a visual table

Usage

tableSurvivalAttrition(
  result,
  type = "gt",
  header = "variable_name",
  groupColumn = c("cdm_name", "target_cohort", "variable_level"),
  hide = c("estimate_name"),
  style = NULL,
  .options = list()
)

Arguments

result	A summarised_result object obtained either from estimateSingleEventSurvival() or estimateCompetingRiskSurvival().
type	Character string specifying the desired output table format. See tableType() for supported table types. If type = NULL, global options (set via setGlobalTableOptions()) will be used if available; otherwise, a default 'gt' table is created.
header	A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. Elements in header can be: Any of the columns returned by tableColumns(result) to create a header for these columns. Any other input to create an overall header.
groupColumn	Columns to use as group labels, to see options use tableColumns(result). By default, the name of the new group will be the tidy* column names separated by ";". To specify a custom group name, use a named list such as: list("newGroupName" = c("variable_name", "variable_level")). *tidy: The tidy format applied to column names replaces "_" with a space and converts to sentence case. Use rename to customise specific column names.
hide	Columns to drop from the output table. By default, result_id and estimate_type are always dropped.
style	Defines the visual formatting of the table. This argument can be provided in one of the following ways: Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See tableStyle() for available options. YAML file path: Provide the path to an existing .yml file defining a new style. List of custom R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type. If style = NULL, the function will use global options (see setGlobalTableOptions()) or an existing ⁠_brand.yml⁠ file (if found); otherwise, the default style is applied. For more details, see the Styles vignette on the package website.
.options	A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A visual table

Examples

library(CohortSurvival)

cdm <- mockMGUS2cdm()

surv <- estimateSingleEventSurvival(
  cdm = cdm,
  targetCohortTable = "mgus_diagnosis",
  outcomeCohortTable = "death_cohort"
)

tableSurvivalAttrition(surv)

---

Table with survival events

Description

Create a formatted table of the number at risk, number of events, and number censored by time interval. The available intervals are controlled by eventGap when the survival result is estimated.

Usage

tableSurvivalEvents(
  x,
  eventGap = NULL,
  header = c("estimate"),
  type = "gt",
  groupColumn = NULL,
  hide = c("result_id", "estimate_type"),
  style = NULL,
  .options = list()
)

Arguments

x	Result from estimateSingleEventSurvival or estimateCompetingRiskSurvival.
eventGap	Event gap defining the times at which to report the risk table information. Must be one of the eventGap inputs used for the estimation function. If NULL, all available are reported.
header	A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. Elements in header can be: Any of the columns returned by tableColumns(result) to create a header for these columns. Any other input to create an overall header.
type	Character string specifying the desired output table format. See tableType() for supported table types. If type = NULL, global options (set via setGlobalTableOptions()) will be used if available; otherwise, a default 'gt' table is created.
groupColumn	Columns to use as group labels, to see options use tableColumns(result). By default, the name of the new group will be the tidy* column names separated by ";". To specify a custom group name, use a named list such as: list("newGroupName" = c("variable_name", "variable_level")). *tidy: The tidy format applied to column names replaces "_" with a space and converts to sentence case. Use rename to customise specific column names.
hide	Columns to drop from the output table. By default, result_id and estimate_type are always dropped.
style	Defines the visual formatting of the table. This argument can be provided in one of the following ways: Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See tableStyle() for available options. YAML file path: Provide the path to an existing .yml file defining a new style. List of custom R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type. If style = NULL, the function will use global options (see setGlobalTableOptions()) or an existing ⁠_brand.yml⁠ file (if found); otherwise, the default style is applied. For more details, see the Styles vignette on the package website.
.options	A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A tibble containing the risk table information (n_risk, n_events, n_censor) for all times within the event gap specified.

Examples

cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(cdm,
                                    targetCohortTable = "mgus_diagnosis",
                                    outcomeCohortTable = "death_cohort")
tableSurvivalEvents(surv)

---