Single outcome event of interest

Set up

Let’s first load the packages required.

library(CDMConnector)
library(CohortSurvival)
library(dplyr)
library(ggplot2)

We’ll create a cdm reference to use our example MGUS2 survival dataset. In practice you would use the CDMConnector package to connect to your data mapped to the OMOP CDM.

cdm <- CohortSurvival::mockMGUS2cdm()

In this vignette we’ll first estimate survival following a diagnosis of MGUS, with death our outcome of interest.

We would typically need to define study cohorts ourselves, but in the case of our example data we already have these cohorts available. You can see for our diagnosis cohort we also have a number of additional features recorded for individuals which we’ll use for stratification.

cdm$mgus_diagnosis %>% 
  glimpse()
#> Rows: ??
#> Columns: 10
#> Database: DuckDB v1.0.0 [eburn@Windows 10 x64:R 4.4.0/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id           <dbl> 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", ">…

cdm$death_cohort %>% 
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.0.0 [eburn@Windows 10 x64:R 4.4.0/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ subject_id           <dbl> 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, …

Overall survival

First, we can estimate survival for the cohort overall like so. Note that the output will be in a summarised result format.

MGUS_death <- estimateSingleEventSurvival(cdm,
  targetCohortTable = "mgus_diagnosis",
  outcomeCohortTable = "death_cohort"
)
MGUS_death %>% 
  glimpse()
#> Rows: 1,318
#> 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> "cohort", "cohort", "cohort", "cohort", "cohort", "co…
#> $ 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> "survival_probability", "survival_probability", "surv…
#> $ 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 &&& outcome", "time &&& outcome", "time &&& out…
#> $ additional_level <chr> "0 &&& death_cohort", "0 &&& death_cohort", "0 &&& de…
class(MGUS_death)
#> [1] "summarised_result" "omop_result"       "tbl_df"           
#> [4] "tbl"               "data.frame"

We can though convert the result to be in a survival format using asSurvivalResult()

MGUS_death %>% 
  asSurvivalResult() %>%
  glimpse()
#> Rows: 1,275
#> Columns: 13
#> $ result_id      <int> 1, 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",…
#> $ cohort         <chr> "mgus_diagnosis", "mgus_diagnosis", "mgus_diagnosis", "…
#> $ outcome        <chr> "death_cohort", "death_cohort", "death_cohort", "death_…
#> $ strata_name    <chr> "overall", "overall", "overall", "overall", "overall", …
#> $ strata_level   <chr> "overall", "overall", "overall", "overall", "overall", …
#> $ variable_name  <chr> "survival_probability", "survival_probability", "surviv…
#> $ variable_level <chr> "death_cohort", "death_cohort", "death_cohort", "death_…
#> $ estimate_name  <chr> "estimate", "estimate_95CI_lower", "estimate_95CI_upper…
#> $ estimate_value <dbl> 1.0000, 1.0000, 1.0000, 0.9697, 0.9607, 0.9787, 0.9494,…
#> $ time           <dbl> 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6…
#> $ result_type    <chr> "survival", "survival", "survival", "survival", "surviv…
#> $ analysis_type  <chr> "single_event", "single_event", "single_event", "single…

As we can see above our results have been outputted in long format. We can plot these results like so.

plotSurvival(MGUS_death)

Our returned results also have attributes containing information that summarises survival.

tableSurvival(MGUS_death)
CDM name Cohort Outcome name Number records Number events Median survival (95% CI) Restricted mean survival (SE)
mock Mgus diagnosis Death cohort 1,384 963 98.00 (92.00, 103.00) 133.00 (4.00)

With stratification

To estimate survival for particular strata of interest we need these features to have been added to the target cohort table. Once we have them defined, and as seen above we already have a number of example characteristics added to our diagnosis cohort, we can add stratifications like so.

MGUS_death <- estimateSingleEventSurvival(cdm,
  targetCohortTable = "mgus_diagnosis",
  outcomeCohortTable = "death_cohort",
  strata = list(c("age_group"),
                c("sex"),
                c("age_group", "sex"))
)

As we can see as well as results for each strata, we’ll always also have overall results returned.

plotSurvival(MGUS_death,
             facet = "strata_name",
             colour = "strata_level")

And we also now have summary statistics for each of the strata as well as overall.

tableSurvival(MGUS_death)
CDM name Cohort Age group Sex Outcome name Number records Number events Median survival (95% CI) Restricted mean survival (SE)
mock Mgus diagnosis Overall Overall Death cohort 1,384 963 98.00 (92.00, 103.00) 133.00 (4.00)
<70 Overall Death cohort 574 293 180.00 (158.00, 206.00) 197.00 (8.00)
>=70 Overall Death cohort 810 670 71.00 (66.00, 77.00) 86.00 (3.00)
Overall F Death cohort 631 423 108.00 (100.00, 121.00) 143.00 (6.00)
M Death cohort 753 540 88.00 (79.00, 97.00) 125.00 (6.00)
<70 F Death cohort 240 109 215.00 (179.00, 260.00) 220.00 (13.00)
M Death cohort 334 184 158.00 (139.00, 189.00) 183.00 (10.00)
>=70 F Death cohort 391 314 82.00 (75.00, 94.00) 96.00 (4.00)
M Death cohort 419 356 61.00 (54.00, 70.00) 80.00 (5.00)

Summarising participants

If we set returnParticipants as TRUE then we will also be able to access the individuals that contributed to the analysis.

MGUS_death <- estimateSingleEventSurvival(cdm,
  targetCohortTable = "mgus_diagnosis",
  outcomeCohortTable = "death_cohort",
  returnParticipants = TRUE
)
survivalParticipants(MGUS_death)
#> # A tibble: 1,384 × 6
#>    cohort_definition_id subject_id cohort_start_date cohort_end_date exposure_id
#>                   <int>      <dbl> <date>            <date>                <int>
#>  1                    1          1 1981-01-01        1981-01-01                1
#>  2                    1          2 1968-01-01        1968-01-01                1
#>  3                    1          4 1977-01-01        1977-01-01                1
#>  4                    1          5 1973-01-01        1973-01-01                1
#>  5                    1          6 1990-01-01        1990-01-01                1
#>  6                    1         10 1981-01-01        1981-01-01                1
#>  7                    1         11 1972-01-01        1972-01-01                1
#>  8                    1         12 1983-01-01        1983-01-01                1
#>  9                    1         13 1968-01-01        1968-01-01                1
#> 10                    1         15 1979-01-01        1979-01-01                1
#> # ℹ 1,374 more rows
#> # ℹ 1 more variable: outcome_id <int>

Disconnect from the cdm database connection

cdm_disconnect(cdm)