Multistate Survival Models — multistatesurvival • ClinicoPath

Multistate models for analyzing disease progression through multiple states. Implements transition probabilities, state occupation probabilities, and competing transitions for complex disease pathways in clinical research.

Usage

multistatesurvival(
  data,
  id,
  time_start,
  time_stop,
  state_from,
  state_to,
  covariates,
  model_type = "markov",
  transition_matrix = "auto",
  state_names = "",
  initial_state = "1",
  absorbing_states = "",
  transition_probabilities = TRUE,
  state_probabilities = TRUE,
  sojourn_times = TRUE,
  hazard_ratios = TRUE,
  prediction_times = "6,12,24,60",
  plot_transitions = TRUE,
  plot_probabilities = TRUE,
  plot_cumhazard = TRUE,
  plot_individual = FALSE,
  competing_risks = TRUE,
  time_varying = FALSE,
  stratified,
  confidence_level = 0.95,
  bootstrap_ci = FALSE,
  n_bootstrap = 1000
)

Arguments

data: The data as a data frame.
id: Patient ID variable for tracking individuals
time_start: Start time of observation period
time_stop: End time of observation period
state_from: State at beginning of interval
state_to: State at end of interval
covariates: Variables influencing state transitions
model_type: Multistate model type specification
transition_matrix: Transition matrix specification
state_names: Custom names for model states
initial_state: Starting state specification
absorbing_states: States from which no transitions occur
transition_probabilities: Compute transition probability matrix
state_probabilities: Compute state occupation probabilities
sojourn_times: Compute mean sojourn times
hazard_ratios: Compute transition-specific hazard ratios
prediction_times: Times at which to evaluate probabilities
plot_transitions: Generate transition diagram plot
plot_probabilities: Generate probability curves
plot_cumhazard: Generate cumulative hazard plots
plot_individual: Create individual-level predictions
competing_risks: Use competing risks framework
time_varying: Include time-varying effects
stratified: Stratification variable
confidence_level: Confidence interval level
bootstrap_ci: Bootstrap-based inference
n_bootstrap: Bootstrap iterations

Value

A results object containing:

`results$todo`					a html
`results$summary`					a html
`results$transitionMatrix`					a table
`results$stateTable`					a table
`results$hazardTable`					a table
`results$sojournTable`					a table
`results$transitionPlot`					an image
`results$probabilityPlot`					an image
`results$cumhazardPlot`					an image
`results$individualPredictions`					a table
`results$modelFit`					a table
`results$stratifiedResults`					a html
`results$interpretation`					a html

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

results$transitionMatrix$asDF

as.data.frame(results$transitionMatrix)

Examples

# \donttest{
# Example: Cancer progression model
# States: Diagnosis -> Remission -> Relapse -> Death
#                   -> Death (direct)
multistatesurvival(
    data = cancer_data,
    id = patient_id,
    time = follow_up_time,
    state = disease_state,
    covariates = c(age, stage, treatment)
)
# }