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Let’s first load the packages required.
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.
The CohortSurvival package does not have implemented functionality to
do more complex survival analyses than Kaplar Meier curves, like Cox
Proportional Hazards modelling. However, the format the data has to be
in to be inputted to well-known modelling functions from packages like
survival or cmprskcan be retrieved from OMOP
data with some in-built functions in this package. Let’s see how to do
it in both single event and competing risk survival settings.
To get the time and status information we
need for the coxph function in the package
survival, for instance, we only need to call
addCohortSurvival. The stratification variables need to be
columns previously added to the cohort by the user.
input_survival_single <- cdm$mgus_diagnosis %>%
addCohortSurvival(
cdm = cdm,
outcomeCohortTable = "death_cohort",
outcomeCohortId = 1
)
input_survival_single %>%
glimpse()
#> Rows: ??
#> Columns: 13
#> 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 <int> 1, 2, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18…
#> $ cohort_start_date <date> 1981-01-01, 1968-01-01, 1973-01-01, 1990-01-01, …
#> $ cohort_end_date <date> 1981-01-01, 1968-01-01, 1973-01-01, 1990-01-01, …
#> $ age <dbl> 88, 78, 90, 90, 87, 79, 86, 89, 87, 80, 85, 90, 9…
#> $ sex <fct> F, F, F, M, F, F, M, F, M, F, M, F, F, M, M, M, F…
#> $ hgb <dbl> 13.1, 11.5, 10.7, 12.9, 12.3, 9.4, 11.8, 11.3, 11…
#> $ creat <dbl> 1.30, 1.20, 0.80, 1.00, 1.20, 1.10, 1.00, 1.30, 1…
#> $ mspike <dbl> 0.5, 2.0, 1.0, 0.5, 1.6, 2.3, 2.3, 1.2, 1.3, 1.3,…
#> $ age_group <chr> ">=70", ">=70", ">=70", ">=70", ">=70", ">=70", "…
#> $ days_to_exit <int> 30, 25, 8, 4, 2, 136, 2, 108, 10, 14, 18, 43, 34,…
#> $ status <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ time <dbl> 30, 25, 8, 4, 2, 136, 2, 108, 10, 14, 18, 43, 34,…This information should be enough to call any advanced function, like:
survival::coxph(survival::Surv(time, status) ~ age + sex, data = input_survival_single)
#> Call:
#> survival::coxph(formula = survival::Surv(time, status) ~ age +
#> sex, data = input_survival_single)
#>
#> coef exp(coef) se(coef) z p
#> age 0.061622 1.063561 0.003402 18.114 < 2e-16
#> sexM 0.358258 1.430835 0.065693 5.454 4.94e-08
#>
#> Likelihood ratio test=391.2 on 2 df, p=< 2.2e-16
#> n= 1384, number of events= 963
survival::survdiff(survival::Surv(time, status) ~ sex, data = input_survival_single)
#> Call:
#> survival::survdiff(formula = survival::Surv(time, status) ~ sex,
#> data = input_survival_single)
#>
#> N Observed Expected (O-E)^2/E (O-E)^2/V
#> sex=F 631 423 471 4.88 9.67
#> sex=M 753 540 492 4.67 9.67
#>
#> Chisq= 9.7 on 1 degrees of freedom, p= 0.002For competing risk, there is a similar function that adds
time and status information to the cohort of
interest. We only need to specify which are the outcome and competing
outcome of interest. We can also choose other options such as follow up
time, censoring on a specific date, washout periods, or others.
input_survival_cr <- cdm$mgus_diagnosis %>%
addCompetingRiskCohortSurvival(
cdm = cdm,
outcomeCohortTable = "progression",
outcomeCohortId = 1,
competingOutcomeCohortTable = "death_cohort",
competingOutcomeCohortId = 1
) %>%
glimpse()
#> Rows: 1,384
#> Columns: 13
#> $ 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, 163, 165, 167, 186, 190, 195, 20…
#> $ cohort_start_date <date> 1978-01-01, 1985-01-01, 1974-01-01, 1993-01-01, …
#> $ cohort_end_date <date> 1978-01-01, 1985-01-01, 1974-01-01, 1993-01-01, …
#> $ age <dbl> 78, 91, 77, 79, 57, 80, 54, 76, 78, 54, 79, 67, 7…
#> $ sex <fct> M, F, F, F, F, M, F, F, M, F, M, M, F, F, F, M, F…
#> $ hgb <dbl> 10.3, 5.9, 12.2, 11.7, 12.2, 11.0, 11.1, 12.8, 17…
#> $ creat <dbl> 3.00, 1.00, 0.80, 0.90, 0.80, 1.40, 1.10, 0.90, 1…
#> $ mspike <dbl> 1.9, 0.0, 2.3, 2.0, 1.9, 2.0, 2.0, 1.9, 0.9, 1.8,…
#> $ age_group <chr> ">=70", ">=70", ">=70", ">=70", "<70", ">=70", "<…
#> $ days_to_exit <int> 44, 21, 233, 21, 260, 85, 172, 104, 101, 171, 11,…
#> $ time <dbl> 29, 14, 228, 13, 201, 76, 118, 51, 101, 153, 8, 1…
#> $ status <fct> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1…We can use the package cmprsk to fit a Fine and Gray
model to the competing risk data. We first change our sex
covariate to numeric.
input_survival_cr <- input_survival_cr %>%
dplyr::mutate(sex = dplyr::if_else(sex == "M", 0, 1))
covs <- data.frame(input_survival_cr$age, input_survival_cr$sex)
names(covs) <- c("age", "sex")
summary(cmprsk::crr(ftime = input_survival_cr$time,
fstatus = input_survival_cr$status,
cov1 = covs,
failcode = 1,
cencode = 0))
#> Competing Risks Regression
#>
#> Call:
#> cmprsk::crr(ftime = input_survival_cr$time, fstatus = input_survival_cr$status,
#> cov1 = covs, failcode = 1, cencode = 0)
#>
#> coef exp(coef) se(coef) z p-value
#> age -0.0192 0.981 0.00585 -3.28 0.001
#> sex 0.2871 1.333 0.19309 1.49 0.140
#>
#> exp(coef) exp(-coef) 2.5% 97.5%
#> age 0.981 1.02 0.970 0.992
#> sex 1.333 0.75 0.913 1.945
#>
#> Num. cases = 1384
#> Pseudo Log-likelihood = -726
#> Pseudo likelihood ratio test = 8.32 on 2 df,These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.