Package 'OdysseusPathwayModule'

Description

Takes the output of executeCohortPathways and a cohort name mapping, decodes the bitmask-encoded combo IDs into human-readable event names, and builds a directed igraph graph representing event transitions across pathway steps.

Nodes in the graph represent unique (event, step) combinations. Edges represent directed transitions between consecutive steps, weighted by patient counts and annotated with transition probabilities.

The returned object includes:

• graph: A directed, weighted igraph object. Vertex attributes: name, eventName, step, count, share. Edge attributes: weight (= patient count), probability, sourceStep, targetStep.

• sequences: The fully decoded pathway sequences with a human-readable pathway string column.

• summary: High-level statistics (total pathways, patients, depth, unique events, vertex/edge counts).

• eventNameLookup: Named character vector mapping combo IDs to event names.

Usage

buildEventSequenceGraph(
  cpResults,
  generationSet,
  maxSteps = NULL,
  minCount = 1
)

Arguments

Value

A list of class "event_sequence_graph" with components graph (igraph object), sequences, summary, and eventNameLookup.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  cpResults <- list(
    pathwaysAnalysisPathsData = data.frame(
      pathwayAnalysisGenerationId = c(1L, 1L),
      targetCohortId = c(10L, 10L),
      step1 = c(2L, 4L),
      step2 = c(4L, NA_integer_),
      countValue = c(10L, 5L)
    ),
    pathwayAnalysisCodesLong = data.frame(
      pathwayAnalysisGenerationId = c(1L, 1L),
      code = c(2L, 4L),
      targetCohortId = c(10L, 10L),
      eventCohortId = c(1L, 2L),
      isCombo = c(0L, 0L),
      numberOfEvents = c(1L, 1L)
    ),
    isCombo = data.frame(
      targetCohortId = c(10L, 10L),
      comboId = c(2L, 4L),
      numberOfEvents = c(1L, 1L),
      isCombo = c(0L, 0L)
    )
  )

  generationSet <- data.frame(
    cohortId = c(1L, 2L, 10L),
    cohortName = c("Celecoxib", "Diclofenac", "NSAIDs")
  )

  esg <- buildEventSequenceGraph(cpResults, generationSet)
  esg$summary
  igraph::gorder(esg$graph)
}

Description

Runs the cohort pathways on all instantiated combinations of target and event cohorts. Assumes the cohorts have already been instantiated.

Supports two analysis types:

• "post-index" (default): events occurring after the target cohort index date (within the target cohort period).

• "pre-index": events occurring before the target cohort index date, in a configurable lookback window. For example, what events occurred in the 365 days before cohort entry.

Target and event cohorts can reside in different schemas/tables:

• cohortDatabaseSchema / cohortTableName for target cohorts.

• outcomeDatabaseSchema / outcomeTableName for event (outcome) cohorts. If not specified, defaults to the same as the target.

Usage

executeCohortPathways(
  connectionDetails = NULL,
  connection = NULL,
  cohortDatabaseSchema,
  cohortTableName = "cohort",
  outcomeDatabaseSchema = cohortDatabaseSchema,
  outcomeTableName = cohortTableName,
  tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
  targetCohortIds,
  eventCohortIds,
  analysisType = "post-index",
  lookbackStartDay = -365,
  lookbackEndDay = -1,
  minCellCount = 5,
  allowRepeats = FALSE,
  maxDepth = 5,
  collapseWindow = 30
)

Arguments

Value

                 A list of data frames containing pathway analysis results.

Examples

if (requireNamespace("Eunomia", quietly = TRUE) &&
    exists("createCohorts", where = asNamespace("Eunomia"), inherits = FALSE)) {
  connectionDetails <- Eunomia::getEunomiaConnectionDetails()
  createCohorts <- get("createCohorts", envir = asNamespace("Eunomia"))
  createCohorts(connectionDetails)

  postIndexResults <- executeCohortPathways(
    connectionDetails = connectionDetails,
    cohortDatabaseSchema = "main",
    cohortTableName = "cohort",
    targetCohortIds = 4,
    eventCohortIds = c(1, 2, 3),
    maxDepth = 3
  )

  preIndexResults <- executeCohortPathways(
    connectionDetails = connectionDetails,
    cohortDatabaseSchema = "main",
    cohortTableName = "cohort",
    targetCohortIds = 4,
    eventCohortIds = c(1, 2, 3),
    analysisType = "pre-index",
    lookbackStartDay = -365,
    lookbackEndDay = -1,
    maxDepth = 3
  )

  names(postIndexResults)
  names(preIndexResults)
}

Description

Produces a layered plot of the event sequence graph using plot.igraph. Nodes are laid out by step (left to right), with edge widths proportional to patient counts and node sizes proportional to event counts within each step.

Usage

## S3 method for class 'event_sequence_graph'
plot(
  x,
  colorPalette = NULL,
  edgeWidthRange = c(0.5, 8),
  vertexSizeRange = c(8, 25),
  vertexLabelCex = 0.7,
  main = "Event Sequence Graph",
  ...
)

Arguments

Value

Invisibly returns the igraph object.

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
  cpResults <- list(
    pathwaysAnalysisPathsData = data.frame(
      pathwayAnalysisGenerationId = c(1L, 1L),
      targetCohortId = c(10L, 10L),
      step1 = c(2L, 4L),
      step2 = c(4L, NA_integer_),
      countValue = c(10L, 5L)
    ),
    pathwayAnalysisCodesLong = data.frame(
      pathwayAnalysisGenerationId = c(1L, 1L),
      code = c(2L, 4L),
      targetCohortId = c(10L, 10L),
      eventCohortId = c(1L, 2L),
      isCombo = c(0L, 0L),
      numberOfEvents = c(1L, 1L)
    ),
    isCombo = data.frame(
      targetCohortId = c(10L, 10L),
      comboId = c(2L, 4L),
      numberOfEvents = c(1L, 1L),
      isCombo = c(0L, 0L)
    )
  )

  generationSet <- data.frame(
    cohortId = c(1L, 2L, 10L),
    cohortName = c("Celecoxib", "Diclofenac", "NSAIDs")
  )

  esg <- buildEventSequenceGraph(cpResults, generationSet)
  plot(esg)
}