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README.md
In CohortSurvival: Estimate Survival from Common Data Model Cohorts
CohortSurvival 
CohortSurvival contains functions for extracting and summarising
survival data using the OMOP common data model.
Installation
You can install CohortSurvival like so:
install.packages("CohortSurvival")
Example usage
Create a reference to data in the OMOP CDM format
The CohortSurvival package is designed to work with data in the OMOP CDM
format, so our first step is to create a reference to the data using the
CDMConnector package.
For this example we´ll use a cdm reference containing the MGUS2 dataset
from the survival package (which we transformed into a set of OMOP CDM
style cohort tables). The MGUS2 dataset contains survival data of 1341
sequential patients with monoclonal gammopathy of undetermined
significance (MGUS). For more information see ?survival::mgus2.
library(CDMConnector)
library(CohortSurvival)
library(dplyr)
library(ggplot2)
cdm <- CohortSurvival::mockMGUS2cdm()
In our cdm reference we have three cohort tables of interest: 1) MGUS
diagnosis cohort
cdm$mgus_diagnosis %>%
glimpse()
#> Rows: ??
#> Columns: 10
#> Database: DuckDB v1.1.3 [lopezk@Windows 10 x64:R 4.3.2/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15…
#> $ cohort_start_date <date> 1981-01-01, 1968-01-01, 1980-01-01, 1977-01-01, …
#> $ cohort_end_date <date> 1981-01-01, 1968-01-01, 1980-01-01, 1977-01-01, …
#> $ age <dbl> 88, 78, 94, 68, 90, 90, 89, 87, 86, 79, 86, 89, 8…
#> $ sex <fct> F, F, M, M, F, M, F, F, F, F, M, F, M, F, M, F, F…
#> $ hgb <dbl> 13.1, 11.5, 10.5, 15.2, 10.7, 12.9, 10.5, 12.3, 1…
#> $ creat <dbl> 1.30, 1.20, 1.50, 1.20, 0.80, 1.00, 0.90, 1.20, 0…
#> $ mspike <dbl> 0.5, 2.0, 2.6, 1.2, 1.0, 0.5, 1.3, 1.6, 2.4, 2.3,…
#> $ age_group <chr> ">=70", ">=70", ">=70", "<70", ">=70", ">=70", ">…
2) MGUS progression to multiple myeloma cohort
cdm$progression %>%
glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.3 [lopezk@Windows 10 x64:R 4.3.2/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id <int> 56, 81, 83, 111, 124, 127, 147, 163, 165, 167, 18…
#> $ cohort_start_date <date> 1978-01-30, 1985-01-15, 1974-08-17, 1993-01-14, …
#> $ cohort_end_date <date> 1978-01-30, 1985-01-15, 1974-08-17, 1993-01-14, …
3) Death cohort
cdm$death_cohort %>%
glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.3 [lopezk@Windows 10 x64:R 4.3.2/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 1…
#> $ cohort_start_date <date> 1981-01-31, 1968-01-26, 1980-02-16, 1977-04-03, …
#> $ cohort_end_date <date> 1981-01-31, 1968-01-26, 1980-02-16, 1977-04-03, …
MGUS diagnosis to death
We can get survival estimates for death following MGUS diagnosis like
so:
MGUS_death <- estimateSingleEventSurvival(cdm,
targetCohortTable = "mgus_diagnosis",
outcomeCohortTable = "death_cohort"
)
MGUS_death %>%
glimpse()
#> Rows: 1,354
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock…
#> $ group_name <chr> "target_cohort", "target_cohort", "target_cohort", "t…
#> $ group_level <chr> "mgus_diagnosis", "mgus_diagnosis", "mgus_diagnosis",…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "outcome", "outcome", "outcome", "outcome", "outcome"…
#> $ variable_level <chr> "death_cohort", "death_cohort", "death_cohort", "deat…
#> $ estimate_name <chr> "estimate", "estimate_95CI_lower", "estimate_95CI_upp…
#> $ estimate_type <chr> "numeric", "numeric", "numeric", "numeric", "numeric"…
#> $ estimate_value <chr> "1", "1", "1", "0.9697", "0.9607", "0.9787", "0.9494"…
#> $ additional_name <chr> "time", "time", "time", "time", "time", "time", "time…
#> $ additional_level <chr> "0", "0", "0", "1", "1", "1", "2", "2", "2", "3", "3"…
Now that we have our results, we can quickly create a plot summarising
survival over time.
plotSurvival(MGUS_death)
Among others, we can check the survival summary of our target and
outcome cohorts of interest:
tableSurvival(MGUS_death, times = c(30,60,180))
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CDM name
Target cohort
Outcome name
Estimate name
Number records
Number events
Median survival (95% CI)
Restricted mean survival (SE)
30 days survival estimate
60 days survival estimate
180 days survival estimate
mock
mgus_diagnosis
death_cohort
1,384
963
98.00 (92.00, 103.00)
133.00 (4.00)
79.39 (77.28, 81.55)
66.15 (63.70, 68.70)
25.90 (23.35, 28.72)
As well as estimating survival for our cohort overall, we can also
estimate survival by strata. These strata are based on variables that
have been added to our target cohort previously as columns.
MGUS_death <- estimateSingleEventSurvival(cdm,
targetCohortTable = "mgus_diagnosis",
outcomeCohortTable = "death_cohort",
strata = list(c("age_group"),
c("sex"),
c("age_group", "sex"))
)
plotSurvival(MGUS_death,
colour = "strata_level",
facet= "strata_name")
Estimating survival accounting for a competing risk
The package also allows to estimate survival of both an outcome and
competing risk outcome together. We can then stratify, see information
on events or summarise the estimates in the same way we did for the
single event survival analysis.
MGUS_death_prog <- estimateCompetingRiskSurvival(cdm,
targetCohortTable = "mgus_diagnosis",
outcomeCohortTable = "progression",
competingOutcomeCohortTable = "death_cohort"
)
plotSurvival(MGUS_death_prog, cumulativeFailure = TRUE,
colour = "variable_level")
As with single event survival, we can stratify our competing risk
analysis.
MGUS_death_prog <- estimateCompetingRiskSurvival(cdm,
targetCohortTable = "mgus_diagnosis",
outcomeCohortTable = "progression",
competingOutcomeCohortTable = "death_cohort",
strata = list(c("age_group", "sex"))
)
plotSurvival(MGUS_death_prog %>%
dplyr::filter(strata_name != "overall"),
cumulativeFailure = TRUE,
facet = "strata_level",
colour = "variable_level")
Disconnect from the cdm database connection
cdmDisconnect(cdm)
Additional information
You can check additional functionality of the package on its
website.
Try the CohortSurvival package in your browser
Any scripts or data that you put into this service are public.
CohortSurvival documentation built on June 16, 2025, 5:10 p.m.
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CohortSurvival
CohortSurvival contains functions for extracting and summarising survival data using the OMOP common data model.
Installation
You can install CohortSurvival like so:
install.packages("CohortSurvival")
Example usage
Create a reference to data in the OMOP CDM format
The CohortSurvival package is designed to work with data in the OMOP CDM format, so our first step is to create a reference to the data using the CDMConnector package.
For this example we´ll use a cdm reference containing the MGUS2 dataset
from the survival package (which we transformed into a set of OMOP CDM
style cohort tables). The MGUS2 dataset contains survival data of 1341
sequential patients with monoclonal gammopathy of undetermined
significance (MGUS). For more information see ?survival::mgus2.
library(CDMConnector)
library(CohortSurvival)
library(dplyr)
library(ggplot2)
cdm <- CohortSurvival::mockMGUS2cdm()
In our cdm reference we have three cohort tables of interest: 1) MGUS diagnosis cohort
cdm$mgus_diagnosis %>%
glimpse()
#> Rows: ??
#> Columns: 10
#> Database: DuckDB v1.1.3 [lopezk@Windows 10 x64:R 4.3.2/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15…
#> $ cohort_start_date <date> 1981-01-01, 1968-01-01, 1980-01-01, 1977-01-01, …
#> $ cohort_end_date <date> 1981-01-01, 1968-01-01, 1980-01-01, 1977-01-01, …
#> $ age <dbl> 88, 78, 94, 68, 90, 90, 89, 87, 86, 79, 86, 89, 8…
#> $ sex <fct> F, F, M, M, F, M, F, F, F, F, M, F, M, F, M, F, F…
#> $ hgb <dbl> 13.1, 11.5, 10.5, 15.2, 10.7, 12.9, 10.5, 12.3, 1…
#> $ creat <dbl> 1.30, 1.20, 1.50, 1.20, 0.80, 1.00, 0.90, 1.20, 0…
#> $ mspike <dbl> 0.5, 2.0, 2.6, 1.2, 1.0, 0.5, 1.3, 1.6, 2.4, 2.3,…
#> $ age_group <chr> ">=70", ">=70", ">=70", "<70", ">=70", ">=70", ">…
2) MGUS progression to multiple myeloma cohort
cdm$progression %>%
glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.3 [lopezk@Windows 10 x64:R 4.3.2/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id <int> 56, 81, 83, 111, 124, 127, 147, 163, 165, 167, 18…
#> $ cohort_start_date <date> 1978-01-30, 1985-01-15, 1974-08-17, 1993-01-14, …
#> $ cohort_end_date <date> 1978-01-30, 1985-01-15, 1974-08-17, 1993-01-14, …
3) Death cohort
cdm$death_cohort %>%
glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.3 [lopezk@Windows 10 x64:R 4.3.2/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 1…
#> $ cohort_start_date <date> 1981-01-31, 1968-01-26, 1980-02-16, 1977-04-03, …
#> $ cohort_end_date <date> 1981-01-31, 1968-01-26, 1980-02-16, 1977-04-03, …
MGUS diagnosis to death
We can get survival estimates for death following MGUS diagnosis like so:
MGUS_death <- estimateSingleEventSurvival(cdm,
targetCohortTable = "mgus_diagnosis",
outcomeCohortTable = "death_cohort"
)
MGUS_death %>%
glimpse()
#> Rows: 1,354
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock…
#> $ group_name <chr> "target_cohort", "target_cohort", "target_cohort", "t…
#> $ group_level <chr> "mgus_diagnosis", "mgus_diagnosis", "mgus_diagnosis",…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "outcome", "outcome", "outcome", "outcome", "outcome"…
#> $ variable_level <chr> "death_cohort", "death_cohort", "death_cohort", "deat…
#> $ estimate_name <chr> "estimate", "estimate_95CI_lower", "estimate_95CI_upp…
#> $ estimate_type <chr> "numeric", "numeric", "numeric", "numeric", "numeric"…
#> $ estimate_value <chr> "1", "1", "1", "0.9697", "0.9607", "0.9787", "0.9494"…
#> $ additional_name <chr> "time", "time", "time", "time", "time", "time", "time…
#> $ additional_level <chr> "0", "0", "0", "1", "1", "1", "2", "2", "2", "3", "3"…
Now that we have our results, we can quickly create a plot summarising survival over time.
plotSurvival(MGUS_death)
Among others, we can check the survival summary of our target and outcome cohorts of interest:
tableSurvival(MGUS_death, times = c(30,60,180))
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CDM name
Target cohort
Outcome name
Estimate name
Number records
Number events
Median survival (95% CI)
Restricted mean survival (SE)
30 days survival estimate
60 days survival estimate
180 days survival estimate
mock
mgus_diagnosis
death_cohort
1,384
963
98.00 (92.00, 103.00)
133.00 (4.00)
79.39 (77.28, 81.55)
66.15 (63.70, 68.70)
25.90 (23.35, 28.72)
As well as estimating survival for our cohort overall, we can also estimate survival by strata. These strata are based on variables that have been added to our target cohort previously as columns.
MGUS_death <- estimateSingleEventSurvival(cdm,
targetCohortTable = "mgus_diagnosis",
outcomeCohortTable = "death_cohort",
strata = list(c("age_group"),
c("sex"),
c("age_group", "sex"))
)
plotSurvival(MGUS_death,
colour = "strata_level",
facet= "strata_name")
Estimating survival accounting for a competing risk
The package also allows to estimate survival of both an outcome and competing risk outcome together. We can then stratify, see information on events or summarise the estimates in the same way we did for the single event survival analysis.
MGUS_death_prog <- estimateCompetingRiskSurvival(cdm,
targetCohortTable = "mgus_diagnosis",
outcomeCohortTable = "progression",
competingOutcomeCohortTable = "death_cohort"
)
plotSurvival(MGUS_death_prog, cumulativeFailure = TRUE,
colour = "variable_level")
As with single event survival, we can stratify our competing risk analysis.
MGUS_death_prog <- estimateCompetingRiskSurvival(cdm,
targetCohortTable = "mgus_diagnosis",
outcomeCohortTable = "progression",
competingOutcomeCohortTable = "death_cohort",
strata = list(c("age_group", "sex"))
)
plotSurvival(MGUS_death_prog %>%
dplyr::filter(strata_name != "overall"),
cumulativeFailure = TRUE,
facet = "strata_level",
colour = "variable_level")
Disconnect from the cdm database connection
cdmDisconnect(cdm)
Additional information
You can check additional functionality of the package on its website.
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