Interval-censored survival analysis for data where the exact event time is unknown but falls within an interval. Common in periodic follow-up studies, screening programs, and clinical trials with scheduled visits.
Usage
intervalsurvival(
left_time,
right_time,
status_var = NULL,
covariates = NULL,
stratification_vars = NULL,
model_type = "aft_weibull",
estimation_method = "em_algorithm",
confidence_level = 0.95,
convergence_tolerance = 1e-04,
max_iterations = 500,
baseline_hazard_smooth = FALSE,
smoothing_bandwidth = 1,
survival_curves = TRUE,
hazard_function = FALSE,
model_diagnostics = TRUE,
residual_analysis = TRUE,
goodness_of_fit = TRUE,
model_comparison = FALSE,
prediction_times = "12,24,36,60",
bootstrap_samples = 200,
mcmc_iterations = 5000,
mcmc_burnin = 1000,
imputation_method = "midpoint"
)Arguments
- left_time
Left boundary of the censoring interval
- right_time
Right boundary of the censoring interval
- status_var
Censoring status indicator variable
- covariates
Covariates to include in the survival model
- stratification_vars
Variables for stratifying the baseline hazard
- model_type
Model specification for interval-censored survival
- estimation_method
Method for parameter estimation
- confidence_level
Confidence level for parameter estimates
- convergence_tolerance
Convergence criterion for iterative algorithms
- max_iterations
Maximum number of algorithm iterations
- baseline_hazard_smooth
Whether to smooth the baseline hazard function
- smoothing_bandwidth
Bandwidth for baseline hazard smoothing
- survival_curves
Whether to plot survival curves
- hazard_function
Whether to plot hazard functions
- model_diagnostics
Whether to include model diagnostics
- residual_analysis
Whether to perform residual analysis
- goodness_of_fit
Whether to test model goodness-of-fit
- model_comparison
Whether to compare multiple models
- prediction_times
Time points for survival probability predictions
- bootstrap_samples
Number of bootstrap resamples
- mcmc_iterations
MCMC sampling iterations
- mcmc_burnin
MCMC burn-in period
- imputation_method
Imputation approach for incomplete intervals
Value
A results object containing:
results$instructions | a html | ||||
results$data_summary | a html | ||||
results$model_results | a html | ||||
results$covariate_effects | a html | ||||
results$survival_estimates | a html | ||||
results$hazard_estimates | a html | ||||
results$model_diagnostics_summary | a html | ||||
results$goodness_of_fit_results | a html | ||||
results$model_comparison_table | a html | ||||
results$residual_summary | a html | ||||
results$survival_curves_plot | an image | ||||
results$hazard_plot | an image | ||||
results$interval_plot | an image | ||||
results$residual_plots | an image | ||||
results$model_comparison_plot | an image | ||||
results$convergence_plot | an image |
Examples
# Example: Tumor progression between visits
intervalsurvival(
data = oncology_data,
left_time = last_negative_visit,
right_time = first_positive_visit,
covariates = c("age", "stage", "treatment"),
model_type = "aft_weibull"
)