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eventPred

eventPred predicts enrollment and event timing in clinical trials. It supports both:

• Design stage prediction using model assumptions and optional priors.
• Analysis stage prediction using observed blinded or unblinded data.

The package provides enrollment modeling, time-to-event modeling, time-to-dropout modeling, simulation-based prediction intervals, and an interactive Shiny app.

Installation

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("kaifenglu/eventPred")

Key Features

• Enrollment models: Poisson, time-decay, B-spline, piecewise Poisson.
• Event/dropout models: exponential, Weibull, log-logistic, log-normal, piecewise exponential, model averaging, spline, and Cox.
• By-treatment prediction and optional baseline covariates.
• Prediction intervals based on simulation (nreps).
• Support for fixed follow-up and variable follow-up designs.
• Built-in example datasets: interimData1, interimData2, finalData.

Typical Workflow

1. Summarize observed data with summarizeObserved().
2. Fit enrollment, event, and dropout models with fitEnrollment(), fitEvent(), and fitDropout().
3. Generate predictions with:
   • predictEnrollment() for enrollment only
   • predictEvent() for event timing only
   • getPrediction() for end-to-end enrollment and event prediction

Minimal Example

library(eventPred)

# Event prediction after enrollment completion
set.seed(3000)

pred <- getPrediction(
  df = interimData2,
  to_predict = "event only",
  target_d = 200,
  event_model = "weibull",
  dropout_model = "exponential",
  pilevel = 0.90,
  nreps = 100
)

pred$event_pred$event_pred_summary

Model-Fit + Prediction Example

library(eventPred)

set.seed(2000)

event_fits <- fitEvent(
  df = interimData2,
  event_model = "piecewise exponential",
  piecewiseSurvivalTime = c(0, 140, 352)
)

dropout_fits <- fitDropout(
  df = interimData2,
  dropout_model = "exponential"
)

event_pred <- predictEvent(
  df = interimData2,
  target_d = 200,
  event_fit = event_fits$fit,
  dropout_fit = dropout_fits$fit,
  pilevel = 0.90,
  nreps = 100
)

event_pred$event_pred_summary

Design Stage Example

library(eventPred)

set.seed(1000)

enroll_pred <- predictEnrollment(
  target_n = 300,
  enroll_fit = list(
    model = "piecewise poisson",
    theta = log(26 / 9 * seq(1, 9) / 30.4375),
    vtheta = diag(9) * 1e-8,
    accrualTime = seq(0, 8) * 30.4375
  ),
  pilevel = 0.90,
  nreps = 100
)

enroll_pred$enroll_pred_summary

Run the Shiny App

library(eventPred)
runShinyApp_eventPred()

Time Unit

The package uses days as the primary time unit. To convert rates per month to rates per day, divide by 30.4375.

Documentation

• Website: https://kaifenglu.github.io/eventPred/
• Function reference: https://kaifenglu.github.io/eventPred/reference/
• Issues: https://github.com/kaifenglu/eventPred/issues

Citation

If you use eventPred in analysis or reporting, please cite relevant methodology references included in the package documentation.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.