Relative Survival Analysis — relativesurvival • ClinicoPath

Relative survival analysis compares observed survival in a patient cohort to expected survival in a matched general population. Essential for cancer registry studies and population-based research.

Usage

relativesurvival(
  data,
  time,
  status,
  age,
  sex,
  year,
  covariates,
  ratetable = "us",
  method = "poharperme",
  time_scale = "years",
  net_survival = TRUE,
  excess_mortality = TRUE,
  crude_probability = TRUE,
  age_standardized = FALSE,
  period_analysis = FALSE,
  cohort_year = "",
  regression_model = "none",
  spline_df = 4,
  plot_observed = TRUE,
  plot_expected = TRUE,
  plot_relative = TRUE,
  plot_excess = TRUE,
  confidence_level = 0.95,
  timepoints = "1,3,5,10"
)

Arguments

data: The data as a data frame.
time: Follow-up time variable
status: Event status variable
age: Age variable for rate table matching
sex: Sex variable for rate table matching
year: Calendar year for rate table matching
covariates: Covariates for regression models
ratetable: Population rate table for expected survival
method: Relative survival estimation method
time_scale: Time unit specification
net_survival: Compute net survival estimates
excess_mortality: Compute excess mortality
crude_probability: Compute cause-specific crude probabilities
age_standardized: Perform age standardization
period_analysis: Use period analysis approach
cohort_year: Cohort year specification
regression_model: Regression model specification
spline_df: Spline complexity
plot_observed: Show observed survival
plot_expected: Show expected survival
plot_relative: Show relative survival
plot_excess: Show excess hazard
confidence_level: CI level
timepoints: Specific time points

Value

A results object containing:

`results$todo`					a html
`results$summary`					a html
`results$survivalTable`					a table
`results$netSurvivalTable`					a table
`results$excessMortalityTable`					a table
`results$crudeProbTable`					a table
`results$regressionTable`					a table
`results$observedPlot`					an image
`results$expectedPlot`					an image
`results$relativePlot`					an image
`results$excessPlot`					an image
`results$ageStandardizedTable`					a table
`results$periodAnalysisTable`					a table
`results$modelFit`					a table
`results$interpretation`					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$survivalTable$asDF

as.data.frame(results$survivalTable)

Examples

# \donttest{
# Example: Cancer relative survival
relativesurvival(
    data = cancer_registry,
    time = followup_years,
    status = vital_status,
    age = age_at_diagnosis,
    sex = gender,
    year = diagnosis_year,
    ratetable = "us_population"
)
# }