View the Incidence Rates

Creates a table with the incidence rates and optionally demographics

Usage

viewIncidenceRate(
  incidenceData,
  ageData = NULL,
  genderData = NULL,
  stratification = "overall",
  maxAgeSampleSize = 5000
)

Arguments

incidenceData

The data extracted using 'getIncidenceRates'

ageData

The data extracted using 'getBinaryTargetBaseline' with analysisIds = 3

genderData

The data extracted using 'getBinaryTargetBaseline' with analysisIds = 1

stratification

Pick either overall/age/sex/year to specify whether to view the overall rates or stratified by age/sex/year

maxAgeSampleSize

When creating the age distributions this is the max age vector length to create

Value

Returns a gt table that displays the incidence rates

Details

Input the incidence rate data (and optionally demographic data)

See also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions()

Examples

conDet <- getExampleConnectionDetails()

connectionHandler <- ResultModelManager::ConnectionHandler$new(conDet)
#> Connecting using SQLite driver
schema <- 'main'

incidenceData <- getIncidenceRates(
  connectionHandler = connectionHandler , 
  schema = schema
  )
  
  # incidence data does not have rate values to imputing them
  incidenceData$incidenceRateP100py <- 1 +
    sample(c(-1,1),replace = TRUE)*runif(nrow(incidenceData))
  incidenceData$incidenceProportionP100p <- 0.5 +
    sample(c(-1,1),replace = TRUE)*runif(nrow(incidenceData))
  
 ageData <- getBinaryTargetBaseline(
  connectionHandler = connectionHandler, 
  schema = schema,  
  analysisIds = 3
 )
 
 genderData <- getBinaryTargetBaseline(
  connectionHandler = connectionHandler, 
  schema = schema,  
  analysisIds = 1
 )

viewIncidenceRate(
  incidenceData = incidenceData,
  ageData = ageData,
  genderData = genderData
  )
#> Closing database connection
#> Closing database connection


  

      
Database
      

      
Rate per 100 person years
      

      
Proportion per 100 persons
      
N
      
Mean Follow-up Days
      
Outcomes
      
Age
      
Male %
    
 - GI bleed ( wo 9999) during ( start + 0 ) - ( end + 0 )
    
Celecoxib  ( 1 )
Synthea
0.022

1.191

1800
1
0

49.7
Celecoxib - age 18 to 64 Age 18 to 64  ( 1001 )
Synthea
0.202

1.429

1800
1
0

49.7
Diclofenac  ( 2 )
Synthea
0.181

0.707

830
1
0

47.6
Diclofenac - age 18 to 64 Age 18 to 64  ( 2001 )
Synthea
0.815

1.012

830
1
0

47.6
 - GI bleed ( wo 9999) during ( start + 0 ) - ( start + 365 )
    
Celecoxib  ( 1 )
Synthea
0.634

0.807

1800
300
0

49.7
Celecoxib - age 18 to 64 Age 18 to 64  ( 1001 )
Synthea
0.667

1.364

1800
300
0

49.7
Diclofenac  ( 2 )
Synthea
1.846

1.481

830
314
0

47.6
Diclofenac - age 18 to 64 Age 18 to 64  ( 2001 )
Synthea
1.125

0.885

830
314
0

47.6