Conditional Generalized Estimating Equations for analyzing gap times between recurrent events. This analysis models the time between consecutive event occurrences conditional on previous event history, accounting for within-subject correlation and providing robust population-average estimates of covariate effects on inter-event times.
Usage
conditionalgee(
data,
subjectID,
gap_time,
event,
event_number,
covariates,
time_varying_covariates,
baseline_covariates,
distribution_family = "weibull",
correlation_structure = "exchangeable",
link_function = "log",
conditioning_set = "previous_gap",
confidence_level = 0.95,
max_iterations = 200,
tolerance = 1e-06,
robust_se = TRUE,
include_diagnostics = TRUE,
include_predictions = TRUE,
plotGapTimes = TRUE,
plotCorrelation = TRUE,
plotResiduals = FALSE,
plotPredictions = FALSE,
showEducation = TRUE,
showInterpretation = TRUE,
exportResults = FALSE
)Arguments
- data
.
- subjectID
.
- gap_time
.
- event
.
- event_number
.
- covariates
.
- time_varying_covariates
.
- baseline_covariates
.
- distribution_family
.
- correlation_structure
.
- link_function
.
- conditioning_set
.
- confidence_level
.
- max_iterations
.
- tolerance
.
- robust_se
.
- include_diagnostics
.
- include_predictions
.
- plotGapTimes
.
- plotCorrelation
.
- plotResiduals
.
- plotPredictions
.
- showEducation
.
- showInterpretation
.
- exportResults
.
Value
A results object containing:
results$todo | a html | ||||
results$modelfit | a table | ||||
results$coefficients | a table | ||||
results$correlationStructure | a table | ||||
results$modelDiagnostics | a table | ||||
results$gapTimePredictions | a table | ||||
results$educationalContent | a html | ||||
results$interpretationContent | a html | ||||
results$gapTimesPlot | an image | ||||
results$correlationPlot | an image | ||||
results$residualsPlot | an image | ||||
results$predictionsPlot | an image | ||||
results$exportTable | a table |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelfit$asDF
as.data.frame(results$modelfit)
Examples
data('histopathology', package='ClinicoPath')