Getting Started with OdysseusSurvivalModule

Introduction

OdysseusSurvivalModule supports a two-step workflow:

1. Build a subject-level survival dataset from OMOP CDM cohorts.
2. Estimate survival curves from that dataset with singleEventSurvival().

This vignette uses Eunomia as the example CDM and focuses on the package API as it exists today.

Package Surface

The exported function is singleEventSurvival(). The cohort-to-survival-data helper, addCohortSurvival(), is currently internal, so vignette examples call it with :::.

library(OdysseusSurvivalModule)
library(DatabaseConnector)
library(Eunomia)

Connect to Eunomia

connectionDetails <- Eunomia::getEunomiaConnectionDetails()
connection <- DatabaseConnector::connect(connectionDetails)

cdmDatabaseSchema <- Eunomia::getEunomiaCdmDatabaseSchema()
cohortDatabaseSchema <- Eunomia::getEunomiaResultsSchema()

Prerequisite: Target and Outcome Cohorts

addCohortSurvival() does not create cohorts from concept IDs. It expects existing target and outcome cohort tables. In a typical OHDSI workflow, those cohorts are created ahead of time with ATLAS, Circe, or custom SQL and stored in the cohort results schema.

For the examples below, assume a table named cohort exists in the Eunomia results schema and contains:

• target cohort definition 1
• outcome cohort definition 2

Build Survival Data

survivalData <- OdysseusSurvivalModule:::addCohortSurvival(
  connection = connection,
  cdmDatabaseSchema = cdmDatabaseSchema,
  cohortDatabaseSchema = cohortDatabaseSchema,
  targetCohortTable = "cohort",
  targetCohortId = 1,
  outcomeCohortTable = "cohort",
  outcomeCohortId = 2,
  outcomeDateVariable = "cohort_start_date",
  followUpDays = 365,
  includeAge = TRUE,
  includeGender = TRUE
)

head(survivalData)

The resulting data frame contains subject_id, time, and status, with optional age_years and gender columns when requested.

Fit a Kaplan-Meier Model

kmFit <- singleEventSurvival(
  survivalData = survivalData,
  timeScale = "days",
  model = "km",
  strata = "gender",
  confInt = 0.95
)

names(kmFit)
kmFit[["overall"]]$summary
kmFit[["gender=Female"]]$summary
kmFit$logrank_test_gender

The returned object is a named list. Each stratum entry contains:

• data: survival step data
• summary: aggregate statistics such as median and mean survival

When strata = "gender", the list includes entries such as gender=Female and gender=Male, plus overall.

Inspect the Returned Data

overall_curve <- kmFit[["overall"]]$data
female_curve <- kmFit[["gender=Female"]]$data

head(overall_curve)
head(female_curve)

Each data table contains stepwise survival information, including time, n_risk, n_event, survival, hazard, and cumulative event counts.

Plot a Returned Curve

singleEventSurvival() returns tabular survival output rather than a survfit object, so plotting is done from the returned data frame.

plot(
  kmFit[["overall"]]$data$time,
  kmFit[["overall"]]$data$survival,
  type = "s",
  xlab = "Time (days)",
  ylab = "Survival probability",
  main = "Overall Kaplan-Meier curve"
)

Fit Cox and Parametric Models

The model argument supports "km", "cox", "weibull", "exponential", "lognormal", and "loglogistic".

coxFit <- singleEventSurvival(
  survivalData = survivalData,
  timeScale = "days",
  model = "cox",
  covariates = c("age_years"),
  confInt = 0.95
)

weibullFit <- singleEventSurvival(
  survivalData = survivalData,
  timeScale = "days",
  model = "weibull",
  covariates = c("age_years")
)

coxFit[["overall"]]$summary
weibullFit[["overall"]]$summary

For Cox and parametric models, the function still returns survival summaries and step data. It does not return a coefficient table or hazard-ratio table.

Cleanup

DatabaseConnector::disconnect(connection)

Summary

The core workflow is:

1. Prepare target and outcome cohorts in Eunomia.
2. Build subject-level survival data with OdysseusSurvivalModule:::addCohortSurvival().
3. Fit survival models with singleEventSurvival().
4. Work with the returned data and summary components directly.