Implements transformation models providing a unified framework for various survival analysis approaches through transformation functions. Includes linear transformation models, Box-Cox transformations, and non-parametric transformations, enabling flexible modeling of survival data with automatic transformation selection and validation.
Usage
transformationmodels(
data,
elapsedtime,
outcome,
covariates,
outcomeLevel = "1",
transformation = "boxcox",
distribution = "normal",
method = "ml",
support = "automatic",
lambda_search = TRUE,
lambda_range_min = -2,
lambda_range_max = 2,
stratify_variable,
weights_variable,
confidence_level = 0.95,
max_iterations = 100,
convergence_tolerance = 1e-06,
transformation_validation = TRUE,
model_selection = FALSE,
bootstrap_ci = FALSE,
bootstrap_samples = 500,
show_model_summary = TRUE,
show_coefficients = TRUE,
show_transformation = TRUE,
show_diagnostics = TRUE,
show_comparison = TRUE,
show_transformation_plots = TRUE,
show_residual_plots = TRUE,
show_survival_plots = TRUE,
show_qq_plots = TRUE,
showSummaries = FALSE,
showExplanations = FALSE
)Arguments
- data
the data as a data frame
- elapsedtime
Survival time or follow-up duration variable
- outcome
Event indicator variable (0/1, FALSE/TRUE, or factor)
- covariates
Predictor variables for the transformation model
- outcomeLevel
Level of outcome variable indicating event occurrence
- transformation
Type of transformation function to apply
- distribution
Error distribution assumption
- method
Parameter estimation method
- support
Specification of transformation support
- lambda_search
Automatic search for optimal Box-Cox lambda parameter
- lambda_range_min
Minimum value for lambda parameter search
- lambda_range_max
Maximum value for lambda parameter search
- stratify_variable
Variable for stratified analysis
- weights_variable
Observation weights variable
- confidence_level
Confidence level for intervals
- max_iterations
Maximum number of iterations
- convergence_tolerance
Convergence tolerance for estimation
- transformation_validation
Validate transformation assumptions
- model_selection
Automatic selection of best transformation
- bootstrap_ci
Compute bootstrap confidence intervals
- bootstrap_samples
Number of bootstrap samples
- show_model_summary
Display comprehensive model summary
- show_coefficients
Display coefficient estimates table
- show_transformation
Display transformation function analysis
- show_diagnostics
Display model diagnostics
- show_comparison
Compare different transformation models
- show_transformation_plots
Display transformation function plots
- show_residual_plots
Display residual diagnostic plots
- show_survival_plots
Display survival curves
- show_qq_plots
Display quantile-quantile plots
- showSummaries
Generate natural language summaries
- showExplanations
Show detailed methodology explanations
Value
A results object containing:
results$modelSummary | a table | ||||
results$coefficients | a table | ||||
results$transformationAnalysis | a table | ||||
results$diagnostics | a table | ||||
results$modelComparison | a table | ||||
results$transformationInfo | a table | ||||
results$lambdaSearch | a table | ||||
results$validationResults | a table | ||||
results$transformationPlots | an image | ||||
results$residualPlots | an image | ||||
results$survivalPlots | an image | ||||
results$qqPlots | an image | ||||
results$summaryTable | a html | ||||
results$methodExplanation | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelSummary$asDF
as.data.frame(results$modelSummary)
Examples
# Example: Transformation model with Box-Cox transformation
library(survival)
library(tram)
transformationmodels(
data = survival_data,
elapsedtime = "time",
outcome = "status",
outcomeLevel = "1",
covariates = c("age", "treatment", "biomarker"),
transformation = "boxcox",
distribution = "normal",
method = "ml"
)