Continuous-Time Markov Models — continuousmarkov • ClinicoPath

Continuous-time Markov models for analyzing longitudinal data with discrete states. Handles irregularly observed data and estimates transition intensities between states.

Usage

continuousmarkov(
  data,
  subject,
  time,
  state,
  covariates,
  time_covariates,
  model_structure = "full",
  baseline_hazard = "piecewise",
  n_intervals = 5,
  absorbing_states = "",
  initial_values = FALSE,
  optimization_method = "BFGS",
  calculate_sojourn = TRUE,
  transition_probabilities = TRUE,
  prevalence_estimates = TRUE,
  prediction_times = "1, 5, 10, 20",
  confidence_intervals = TRUE,
  bootstrap_se = FALSE,
  n_bootstrap = 100,
  plot_intensities = TRUE,
  plot_probabilities = TRUE,
  plot_prevalence = TRUE,
  model_selection = FALSE,
  goodness_of_fit = TRUE
)

Arguments

data: The data as a data frame.
subject: Variable identifying individual subjects
time: Time variable for observations
state: Discrete state variable
covariates: Variables affecting transition intensities
time_covariates: Covariates that change over time
model_structure: Defines which state transitions are possible
baseline_hazard: Baseline hazard specification
n_intervals: Time intervals for piecewise baseline
absorbing_states: States from which no transitions are possible
initial_values: Use custom initial parameter values
optimization_method: Method for maximum likelihood estimation
calculate_sojourn: Compute mean sojourn times
transition_probabilities: Compute P(t) matrices over time
prevalence_estimates: Estimate prevalence in each state
prediction_times: Time points for probability predictions
confidence_intervals: Include uncertainty estimates
bootstrap_se: Bootstrap-based confidence intervals
n_bootstrap: Bootstrap sample size
plot_intensities: Generate intensity plots
plot_probabilities: Generate probability plots
plot_prevalence: Generate prevalence plots
model_selection: Perform model comparison using AIC/BIC
goodness_of_fit: Test model adequacy

Value

A results object containing:

`results$todo`					a html
`results$summary`					a html
`results$dataStructure`					a table
`results$transitionMatrix`					a table
`results$intensityMatrix`					a table
`results$covariateEffects`					a table
`results$sojournTimes`					a table
`results$transitionProbabilities`					a table
`results$prevalenceTable`					a table
`results$modelFit`					a table
`results$intensityPlot`					an image
`results$probabilityPlot`					an image
`results$prevalencePlot`					an image
`results$modelComparison`					a table
`results$goodnessOfFit`					a html
`results$clinicalInterpretation`					a html

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

results$dataStructure$asDF

as.data.frame(results$dataStructure)

Examples

# \donttest{
# Example usage
continuousmarkov(
    data = longitudinal_data,
    time = observation_time,
    state = state_variable,
    subject = patient_id,
    covariates = c(age, treatment)
)
# }