Time-Dependent Decision Curve Analysis

Usage

timedependentdca(
  data,
  time = NULL,
  event = NULL,
  predictors = list(),
  time_points = "365, 730, 1095",
  threshold_range_min = 0.01,
  threshold_range_max = 0.99,
  threshold_steps = 100,
  reference_strategy = "both",
  estimate_survival = "kaplan_meier",
  smoothing = FALSE,
  use_bootstrap = FALSE,
  bootstrap_iterations = 500,
  ci_level = 0.95,
  plot_net_benefit = FALSE,
  plot_by_timepoint = FALSE,
  plot_interventions_avoided = FALSE,
  random_seed = 42
)

Arguments

data

the data as a data frame

time

a string naming the time-to-event variable

event

a string naming the event indicator (1=event, 0=censored)

predictors

one or more risk score or prediction variables

time_points

comma-separated time points at which to calculate net benefit (e.g., "365, 730" for 1 and 2 years)

threshold_range_min

minimum threshold probability for decision curve

threshold_range_max

maximum threshold probability for decision curve

threshold_steps

number of threshold probabilities to evaluate

reference_strategy

reference strategy for comparison

estimate_survival

method for estimating event probabilities from predictor

smoothing

apply LOESS smoothing to decision curves for visualization

use_bootstrap

calculate 95\ using bootstrap resampling (computationally intensive)

bootstrap_iterations

number of bootstrap iterations for confidence interval calculation

ci_level

confidence level for bootstrap intervals (e.g., 0.95 for 95\

plot_net_benefitplot net benefit curves across threshold probabilities

plot_by_timepointcreate separate plots for each time point (vs. overlay)

plot_interventions_avoidedplot number of interventions avoided per 100 patients

random_seedrandom seed for reproducible bootstrap sampling

A results object containing:

results$instructionsText					a html
results$notices					a html
results$netBenefitTable					a table
results$summaryTable					a table
results$interventionsTable					a table
results$comparisonTable					a table
results$netBenefitPlot					an image
results$interventionsPlot					an image
results$interpretationText					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:results$netBenefitTable$asDFas.data.frame(results$netBenefitTable) Perform time-dependent Decision Curve Analysis (DCA) for survival data using the dcurves package. This method extends standard DCA to evaluate the clinical utility of prognostic models with time-to-event outcomes. It calculates net benefit at specific time points, accounting for censoring and time-varying risk predictions. It's essential for evaluating survival models, recurrence prediction models, and other longitudinal outcomes. Common applications: Serial biopsy surveillance, recurrence vs death prediction, time-varying treatment decisions. # Example with survival outcome library(survival) data <- lungtimedependentdca( data = data, time = 'time', event = 'status', predictor = 'ph.ecog', time_points = c(180, 365, 730), reference_strategy = 'treat_all' )