Package 'CohortSurvival' reference manual

Title:	Estimate Survival from Common Data Model Cohorts
Description:	Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts.
Authors:	Edward Burn [aut, cre] , Kim Lopez [aut] , Marti Catala [ctb] , Xintong Li [ctb] , Danielle Newby [ctb]
Maintainer:	Edward Burn <[email protected]>
License:	Apache License (>= 2)
Version:	0.5.2
Built:	2024-09-28 07:21:56 UTC
Source:	CRAN

Add survival information to a cohort table

Description

Add survival information to a cohort table

Usage

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

Arguments

`x`	cohort table to add survival information
`cdm`	CDM reference
`outcomeCohortTable`	The outcome cohort table of interest.
`outcomeCohortId`	ID of event cohorts to include. Only one outcome (and so one ID) can be considered.
`outcomeDateVariable`	Variable containing date of outcome event
`outcomeWashout`	Washout time in days for the outcome
`censorOnCohortExit`	If TRUE, an individual's follow up will be censored at their cohort exit
`censorOnDate`	if not NULL, an individual's follow up will be censored at the given date
`followUpDays`	Number of days to follow up individuals (lower bound 1, upper bound Inf)

Value

Two additional columns will be added to x. The "time" column will contain number of days to censoring. The "status" column will indicate whether the patient had the event (value: 1), or did not have the event (value: 0)

Examples



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

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

Add competing risk survival information to a cohort table

Description

Add competing risk survival information to a cohort table

Usage

addCompetingRiskCohortSurvival(
  x,
  cdm,
  outcomeCohortTable,
  outcomeCohortId = 1,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  outcomeCensorOnCohortExit = FALSE,
  outcomeCensorOnDate = NULL,
  outcomeFollowUpDays = Inf,
  competingOutcomeCohortTable,
  competingOutcomeCohortId = 1,
  competingOutcomeDateVariable = "cohort_start_date",
  competingOutcomeWashout = Inf,
  competingOutcomeCensorOnCohortExit = FALSE,
  competingOutcomeCensorOnDate = NULL,
  competingOutcomeFollowUpDays = Inf
)
addCompetingRiskCohortSurvival(
  x,
  cdm,
  outcomeCohortTable,
  outcomeCohortId = 1,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  outcomeCensorOnCohortExit = FALSE,
  outcomeCensorOnDate = NULL,
  outcomeFollowUpDays = Inf,
  competingOutcomeCohortTable,
  competingOutcomeCohortId = 1,
  competingOutcomeDateVariable = "cohort_start_date",
  competingOutcomeWashout = Inf,
  competingOutcomeCensorOnCohortExit = FALSE,
  competingOutcomeCensorOnDate = NULL,
  competingOutcomeFollowUpDays = Inf
)

Arguments

`x`	cohort table to add survival information
`cdm`	CDM reference
`outcomeCohortTable`	The outcome cohort table of interest.
`outcomeCohortId`	ID of event cohorts to include. Only one outcome (and so one ID) can be considered.
`outcomeDateVariable`	Variable containing date of outcome event
`outcomeWashout`	Washout time in days for the outcome
`outcomeCensorOnCohortExit`	If TRUE, an individual's follow up will be censored at their cohort exit
`outcomeCensorOnDate`	if not NULL, an individual's follow up will be censored at the given date
`outcomeFollowUpDays`	Number of days to follow up individuals (lower bound 1, upper bound Inf)
`competingOutcomeCohortTable`	The outcome cohort table of interest.
`competingOutcomeCohortId`	ID of event cohorts to include. Only one outcome (and so one ID) can be considered.
`competingOutcomeDateVariable`	Variable containing date of competing outcome event
`competingOutcomeWashout`	Washout time in days for the competing outcome
`competingOutcomeCensorOnCohortExit`	If TRUE, an individual's follow up will be censored at their cohort exit
`competingOutcomeCensorOnDate`	if not NULL, an individual's follow up will be censored at the given date
`competingOutcomeFollowUpDays`	Number of days to follow up individuals (lower bound 1, upper bound Inf)

Value

Two additional columns will be added to x. The "time" column will contain number of days to censoring. The "status" column will indicate whether the patient had the outcome event (value: 1), competing event (value:2) or did not have the event/is censored (value: 0)

Examples



cdm <- mockMGUS2cdm()
crsurvivaldata <- cdm$mgus_diagnosis %>%
  addCompetingRiskCohortSurvival(
    cdm = cdm,
    outcomeCohortTable = "progression",
    outcomeCohortId = 1,
    competingOutcomeCohortTable = "death_cohort",
    competingOutcomeCohortId = 1
  )
  

cdm <- mockMGUS2cdm()
crsurvivaldata <- cdm$mgus_diagnosis %>%
  addCompetingRiskCohortSurvival(
    cdm = cdm,
    outcomeCohortTable = "progression",
    outcomeCohortId = 1,
    competingOutcomeCohortTable = "death_cohort",
    competingOutcomeCohortId = 1
  )

A tidy implementation of the summarised_characteristics object.

Description

A tidy implementation of the summarised_characteristics object.

Usage

asSurvivalResult(result)
asSurvivalResult(result)

Arguments

result

A summarised_characteristics object.

Value

A tibble with a tidy version of the summarised_characteristics object.

Examples


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


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

Estimate performance of estimateSurvival function for benchmarking

Description

Estimate performance of estimateSurvival function for benchmarking

Usage

benchmarkCohortSurvival(
  cdm,
  targetSize,
  outcomeSize,
  outcomeDateVariable = "cohort_start_date",
  competingOutcomeSize = NULL,
  competingOutcomeDateVariable = "cohort_start_date",
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  minCellCount = 5,
  returnParticipants = FALSE
)
benchmarkCohortSurvival(
  cdm,
  targetSize,
  outcomeSize,
  outcomeDateVariable = "cohort_start_date",
  competingOutcomeSize = NULL,
  competingOutcomeDateVariable = "cohort_start_date",
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  minCellCount = 5,
  returnParticipants = FALSE
)

Arguments

`cdm`	CDM reference
`targetSize`	number of people in the target cohort table
`outcomeSize`	number of people in the outcome cohort table
`outcomeDateVariable`	Variable containing date of outcome event
`competingOutcomeSize`	number of people in the competing outcome cohort table
`competingOutcomeDateVariable`	Variable containing date of competing event
`censorOnCohortExit`	If TRUE, an individual's follow up will be censored at their cohort exit
`censorOnDate`	if not NULL, an individual's follow up will be censored at the given date
`followUpDays`	Number of days to follow up individuals (lower bound 1, upper bound Inf)
`strata`	strata
`eventGap`	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 eventGap.
`estimateGap`	vector of time points at which to give survival estimates, if NULL estimates at all times are calculated
`minCellCount`	The minimum number of events to reported, below which results will be obscured. If 0, all results will be reported.
`returnParticipants`	Either TRUE or FALSE. If TRUE, references to participants from the analysis will be returned allowing for further analysis.

Value

tibble with performance of estimateSurvival function information, according to the selected input parameters

Examples


cdm <- mockMGUS2cdm()
cdm$condition_occurrence <- cdm$death_cohort %>%
dplyr::rename("condition_start_date" = "cohort_start_date",
             "condition_end_date" = "cohort_end_date") %>%
             dplyr::compute()
surv_timings <- benchmarkCohortSurvival(
cdm, targetSize = 100, outcomeSize = 20)


cdm <- mockMGUS2cdm()
cdm$condition_occurrence <- cdm$death_cohort %>%
dplyr::rename("condition_start_date" = "cohort_start_date",
             "condition_end_date" = "cohort_end_date") %>%
             dplyr::compute()
surv_timings <- benchmarkCohortSurvival(
cdm, targetSize = 100, outcomeSize = 20)

Estimate survival for a given event and competing risk of interest using cohorts in the OMOP Common Data Model

Description

Estimate survival for a given event and competing risk of interest using cohorts in the OMOP Common Data Model

Usage

estimateCompetingRiskSurvival(
  cdm,
  targetCohortTable,
  targetCohortId = NULL,
  outcomeCohortTable,
  outcomeCohortId = NULL,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  competingOutcomeCohortTable,
  competingOutcomeCohortId = NULL,
  competingOutcomeDateVariable = "cohort_start_date",
  competingOutcomeWashout = Inf,
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  restrictedMeanFollowUp = NULL,
  minimumSurvivalDays = 1,
  minCellCount = 5,
  returnParticipants = FALSE
)
estimateCompetingRiskSurvival(
  cdm,
  targetCohortTable,
  targetCohortId = NULL,
  outcomeCohortTable,
  outcomeCohortId = NULL,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  competingOutcomeCohortTable,
  competingOutcomeCohortId = NULL,
  competingOutcomeDateVariable = "cohort_start_date",
  competingOutcomeWashout = Inf,
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  restrictedMeanFollowUp = NULL,
  minimumSurvivalDays = 1,
  minCellCount = 5,
  returnParticipants = FALSE
)

Arguments

`cdm`	CDM reference
`targetCohortTable`	targetCohortTable
`targetCohortId`	targetCohortId
`outcomeCohortTable`	The outcome cohort table of interest.
`outcomeCohortId`	ID of event cohorts to include. Only one outcome (and so one ID) can be considered.
`outcomeDateVariable`	Variable containing date of outcome event
`outcomeWashout`	Washout time in days for the outcome
`competingOutcomeCohortTable`	The competing outcome cohort table of interest.
`competingOutcomeCohortId`	ID of event cohorts to include. Only one competing outcome (and so one ID) can be considered.
`competingOutcomeDateVariable`	Variable containing date of competing outcome event
`competingOutcomeWashout`	Washout time in days for the competing outcome
`censorOnCohortExit`	If TRUE, an individual's follow up will be censored at their cohort exit
`censorOnDate`	if not NULL, an individual's follow up will be censored at the given date
`followUpDays`	Number of days to follow up individuals (lower bound 1, upper bound Inf)
`strata`	strata
`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 eventGap.
`restrictedMeanFollowUp`	number of days of follow-up to take into account when calculating restricted mean for all cohorts
`minimumSurvivalDays`	Minimum number of days required for the main cohort to have survived
`minCellCount`	The minimum number of events to reported, below which results will be obscured. If 0, all results will be reported.
`returnParticipants`	Either TRUE or FALSE. If TRUE, references to participants from the analysis will be returned allowing for further analysis.

Value

tibble with survival information for desired cohort, including: time, people at risk, survival probability, cumulative incidence, 95 CIs, strata and outcome. A tibble with the number of events is outputted as an attribute of the output

Examples


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


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 given event of interest using cohorts in the OMOP Common Data Model

Description

Estimate survival for a given event of interest using cohorts in the OMOP Common Data Model

Usage

estimateSingleEventSurvival(
  cdm,
  targetCohortTable,
  targetCohortId = NULL,
  outcomeCohortTable,
  outcomeCohortId = NULL,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  restrictedMeanFollowUp = NULL,
  minimumSurvivalDays = 1,
  minCellCount = 5,
  returnParticipants = FALSE
)
estimateSingleEventSurvival(
  cdm,
  targetCohortTable,
  targetCohortId = NULL,
  outcomeCohortTable,
  outcomeCohortId = NULL,
  outcomeDateVariable = "cohort_start_date",
  outcomeWashout = Inf,
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  restrictedMeanFollowUp = NULL,
  minimumSurvivalDays = 1,
  minCellCount = 5,
  returnParticipants = FALSE
)

Arguments

`cdm`	CDM reference
`targetCohortTable`	targetCohortTable
`targetCohortId`	targetCohortId
`outcomeCohortTable`	The outcome cohort table of interest.
`outcomeCohortId`	ID of event cohorts to include. Only one outcome (and so one ID) can be considered.
`outcomeDateVariable`	Variable containing date of outcome event
`outcomeWashout`	Washout time in days for the outcome
`censorOnCohortExit`	If TRUE, an individual's follow up will be censored at their cohort exit
`censorOnDate`	if not NULL, an individual's follow up will be censored at the given date
`followUpDays`	Number of days to follow up individuals (lower bound 1, upper bound Inf)
`strata`	strata
`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 eventGap.
`restrictedMeanFollowUp`	number of days of follow-up to take into account when calculating restricted mean for all cohorts
`minimumSurvivalDays`	Minimum number of days required for the main cohort to have survived
`minCellCount`	The minimum number of events to reported, below which results will be obscured. If 0, all results will be reported.
`returnParticipants`	Either TRUE or FALSE. If TRUE, references to participants from the analysis will be returned allowing for further analysis.

Value

Examples


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


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

To create a death cohort

Description

To create a death cohort

Usage

generateDeathCohortSet(cdm, name, cohortTable = NULL, cohortId = NULL)
generateDeathCohortSet(cdm, name, cohortTable = NULL, cohortId = NULL)

Arguments

`cdm`	CDM reference
`name`	name for the created death cohort table
`cohortTable`	name of the cohort table to create a death cohort for
`cohortId`	name of the cohort table to create a death cohort for

Value

A cohort table with a death cohort in cdm

Examples


library(CDMConnector)
library(CohortSurvival)
observation_period <- dplyr::tibble(
  observation_period_id = c(1, 2, 3, 4, 5,6),
  person_id = c(1, 2, 3, 4, 5,6),
  observation_period_start_date = c(
    rep(as.Date("1980-07-20"),6)
  ),
  observation_period_end_date = c(
    rep(as.Date("2023-05-20"),6)
  ),
  period_type_concept_id = c(rep(0,6))
)

deathTable <- dplyr::tibble(
  person_id = c(1,2,3),
  death_date = c(as.Date("2020-01-01"),
                 as.Date("2020-01-02"),
                 as.Date("2020-01-01")))

person <- dplyr::tibble(
  person_id = c(1, 2, 3, 4, 5),
  year_of_birth = c(rep("1990", 5)),
  month_of_birth = c(rep("02", 5)),
  day_of_birth = c(rep("11", 5)),
  gender_concept_id = c(rep(0,5)),
  ethnicity_concept_id = c(rep(0,5)),
  race_concept_id = c(rep(0,5))
)

cdm <- omopgenerics::cdmFromTables(
  tables = list(
    person = person,
    observation_period = observation_period,
    death = deathTable
  ),
  cdmName = "mock_es"
)
 db <- DBI::dbConnect(duckdb::duckdb(), ":memory:")
cdm2 = CDMConnector::copy_cdm_to(db,
                                 cdm,
                                 schema = "main")

attr(cdm2, "cdm_schema") <- "main"
attr(cdm2, "write_schema") <- "main"

cdm2 <- generateDeathCohortSet(cdm=cdm2,
                               name = "death_cohort")

library(CDMConnector)
library(CohortSurvival)
observation_period <- dplyr::tibble(
  observation_period_id = c(1, 2, 3, 4, 5,6),
  person_id = c(1, 2, 3, 4, 5,6),
  observation_period_start_date = c(
    rep(as.Date("1980-07-20"),6)
  ),
  observation_period_end_date = c(
    rep(as.Date("2023-05-20"),6)
  ),
  period_type_concept_id = c(rep(0,6))
)

deathTable <- dplyr::tibble(
  person_id = c(1,2,3),
  death_date = c(as.Date("2020-01-01"),
                 as.Date("2020-01-02"),
                 as.Date("2020-01-01")))

person <- dplyr::tibble(
  person_id = c(1, 2, 3, 4, 5),
  year_of_birth = c(rep("1990", 5)),
  month_of_birth = c(rep("02", 5)),
  day_of_birth = c(rep("11", 5)),
  gender_concept_id = c(rep(0,5)),
  ethnicity_concept_id = c(rep(0,5)),
  race_concept_id = c(rep(0,5))
)

cdm <- omopgenerics::cdmFromTables(
  tables = list(
    person = person,
    observation_period = observation_period,
    death = deathTable
  ),
  cdmName = "mock_es"
)
 db <- DBI::dbConnect(duckdb::duckdb(), ":memory:")
cdm2 = CDMConnector::copy_cdm_to(db,
                                 cdm,
                                 schema = "main")

attr(cdm2, "cdm_schema") <- "main"
attr(cdm2, "write_schema") <- "main"

cdm2 <- generateDeathCohortSet(cdm=cdm2,
                               name = "death_cohort")

Create mock CDM reference with survival::mgus2 dataset

Description

Create mock CDM reference with survival::mgus2 dataset

Usage

mockMGUS2cdm()
mockMGUS2cdm()

Value

CDM reference containing data from the survival::mgus2 dataset

Examples

 
cdm <- mockMGUS2cdm()
cdm$person

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()
optionsTableSurvival()

Value

The default .options named list.

Examples

{
optionsTableSurvival()
}


{
optionsTableSurvival()
}

Plot survival results

Description

Plot survival results

Usage

plotSurvival(
  result,
  x = "time",
  xscale = "days",
  ylim = c(0, NA),
  cumulativeFailure = FALSE,
  ribbon = TRUE,
  facet = NULL,
  colour = NULL,
  riskTable = FALSE,
  riskInterval = 30
)
plotSurvival(
  result,
  x = "time",
  xscale = "days",
  ylim = c(0, NA),
  cumulativeFailure = FALSE,
  ribbon = TRUE,
  facet = NULL,
  colour = NULL,
  riskTable = FALSE,
  riskInterval = 30
)

Arguments

`result`	Survival results
`x`	Variable to plot on x axis
`xscale`	X axis scale. Can be "days" or "years".
`ylim`	Limits for the Y axis
`cumulativeFailure`	whether to plot the cumulative failure probability instead of the survival probability
`ribbon`	If TRUE, the plot will join points using a ribbon
`facet`	Variables to use for facets
`colour`	Variables to use for colours
`riskTable`	Whether to print risk table below the plot
`riskInterval`	Interval of time to print risk table below the plot

Value

A plot of survival probabilities over time

Examples


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


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

Participants contributing to a survival analysis

Description

Participants contributing to a survival analysis

Usage

survivalParticipants(result)
survivalParticipants(result)

Arguments

result

Result object

Value

References to the study participants contributing to a given analysis

Examples


cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(cdm,
                                    targetCohortTable = "mgus_diagnosis",
                                    outcomeCohortTable = "death_cohort",
                                    returnParticipants = TRUE)
survivalParticipants(surv)

cdm <- mockMGUS2cdm()
surv <- estimateSingleEventSurvival(cdm,
                                    targetCohortTable = "mgus_diagnosis",
                                    outcomeCohortTable = "death_cohort",
                                    returnParticipants = TRUE)
survivalParticipants(surv)

Table with survival summary

Description

Table with survival summary

Usage

tableSurvival(
  x,
  times = NULL,
  timeScale = "days",
  splitStrata = TRUE,
  header = c("estimate"),
  type = "gt",
  groupColumn = NULL,
  .options = list()
)
tableSurvival(
  x,
  times = NULL,
  timeScale = "days",
  splitStrata = TRUE,
  header = c("estimate"),
  type = "gt",
  groupColumn = NULL,
  .options = list()
)

Arguments

`x`	Result from estimateSingleEventSurvival or estimateCompetingRiskSurvival
`times`	Times at which to report survival in the summary table
`timeScale`	Time unit to report survival in: days, months or years
`splitStrata`	If TRUE strata will be split into columns, otherwise "strata_name" and "strata_level" columns will be kept.
`header`	A vector containing which elements should go into the header. Allowed are: cdm_name, group, strata, additional, variable, estimate, and settings.
`type`	Type of desired formatted table, possibilities: "gt", "flextable", and "tibble".
`groupColumn`	Columns to use as group labels.
`.options`	Named list with additional formatting options. CohortSurvival::optionsTableSurvival() shows allowed arguments and their default values.

Value

A tibble containing a summary of observed survival in the required units

Examples


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

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

Package 'CohortSurvival'

Help Index

Add survival information to a cohort table

Description

Usage

Arguments

Value

Examples

Add competing risk survival information to a cohort table

Description

Usage

Arguments

Value

Examples

A tidy implementation of the summarised_characteristics object.

Description

Usage

Arguments

Value

Examples

Estimate performance of estimateSurvival function for benchmarking

Description

Usage

Arguments

Value

Examples

Estimate survival for a given event and competing risk of interest using cohorts in the OMOP Common Data Model

Description

Usage

Arguments

Value

Examples

Estimate survival for a given event of interest using cohorts in the OMOP Common Data Model

Description

Usage

Arguments

Value

Examples

To create a death cohort

Description

Usage

Arguments

Value

Examples

Create mock CDM reference with survival::mgus2 dataset

Description

Usage

Value

Examples

Additional arguments for the function tableSurvival()

Description

Usage

Value

Examples

Plot survival results

Description

Usage

Arguments

Value

Examples

Participants contributing to a survival analysis

Description

Usage

Arguments

Value

Examples

Table with survival summary

Description

Usage

Arguments

Value

Examples