Assurance Methods for Clinical Trials with a Delayed Treatment Effect

Provides functions for planning clinical trials subject to a delayed treatment effect using assurance-based methods. Includes two 'shiny' applications for interactive exploration, simulation, and visualisation of trial designs and outcomes. The methodology is described in: Salsbury JA, Oakley JE, Julious SA, Hampson LV (2024) "Assurance methods for designing a clinical trial with a delayed treatment effect" , Salsbury JA, Oakley JE, Julious SA, Hampson LV (2024) "Adaptive clinical trial design with delayed treatment effects using elicited prior distributions" .

README

DTEAssurance

DTEAssurance is an R package for implementing assurance methodology in the design of clinical trials with an anticipated delayed treatment effect (DTE).
It uses elicited prior distributions—via the SHELF framework—for the delay duration and post-delay hazard ratio, and simulates operating characteristics to inform trial design.

The methodology is based on the following papers:

• Salsbury JA, Oakley JE, Julious SA, Hampson LV.
  Assurance methods for designing a clinical trial with a delayed treatment effect.
  Statistics in Medicine, 2024; 43(19): 3595–3612. doi:10.1002/sim.10136

• Salsbury JA, Oakley JE, Julious SA, Hampson LV.
  Adaptive clinical trial design with delayed treatment effects using elicited prior distributions.
  arXiv preprint, 2025; arXiv:2509.07602 [under revision at Pharmaceutical Statistics]

Installation

You can install DTEAssurance from GitHub using:

# Install from GitHub
devtools::install_github("jamesalsbury/DTEAssurance")
#> Using GitHub PAT from the git credential store.
#> Skipping install of 'DTEAssurance' from a github remote, the SHA1 (da7dd954) has not changed since last install.
#>   Use `force = TRUE` to force installation
library(DTEAssurance)

Once accepted to CRAN, you’ll be able to install it with:

install.packages("DTEAssurance")
#> Warning: package 'DTEAssurance' is in use and will not be installed

Assurance Methods for Delayed Treatment Effects

Shiny App

Launch the interactive app to explore assurance under delayed treatment effects:

DTEAssurance::assurance_shiny_app()

Offline Example

You can also use the package offline via the main function:

DTEAssurance::calc_dte_assurance()

This function requires the following arguments:

• n_c: Number of patients in the control group
• n_t: Number of patients in the treatment group
• control_model: A named list specifying the control arm survival distribution
• effect_model: A named list specifying beliefs about the treatment effect
• censoring_model: A named list specifying the censoring mechanism
• recruitment_model: A named list specifying the recruitment process
• analysis_model: A named list specifying the statistical test and decision rule
• n_sims: Number of simulations to run

An example of this is shown:

control_model <- list(dist = "Exponential", parameter_mode = "Fixed", fixed_type = "Parameters", lambda = 0.1)
effect_model <- list(delay_SHELF = SHELF::fitdist(c(3, 4, 5), probs = c(0.25, 0.5, 0.75), lower = 0, upper = 10),
delay_dist = "gamma",
HR_SHELF = SHELF::fitdist(c(0.55, 0.6, 0.7), probs = c(0.25, 0.5, 0.75), lower = 0, upper = 1.5),
HR_dist = "gamma",
P_S = 1, P_DTE = 0)
censoring_model <- list(method = "Time", time = 12)
recruitment_model <- list(method = "power", period = 12, power = 1)
analysis_model <- list(method = "LRT", alpha = 0.025, alternative_hypothesis = "one.sided")
result <- calc_dte_assurance(n_c = 300, n_t = 300,
                             control_model = control_model,
                             effect_model = effect_model,
                             censoring_model = censoring_model,
                             recruitment_model = recruitment_model,
                             analysis_model = analysis_model,
                             n_sims = 100)

str(result)
#> List of 4
#>  $ assurance  : num 0.83
#>  $ CI         : num [1, 1:2] 0.742 0.898
#>  $ duration   : num 12
#>  $ sample_size: num 600

We can vary the sample sizes and plot the resulting output:

result <- calc_dte_assurance(n_c = seq(50, 500, by = 50),
                             n_t = seq(50, 500, by = 50),
                             control_model = control_model,
                             effect_model = effect_model,
                             censoring_model = censoring_model,
                             recruitment_model = recruitment_model,
                             analysis_model = analysis_model,
                             n_sims = 500)

Assurance for DTE - With Group Sequential Designs

Shiny App

Launch the interactive shiny app to explore assurance under delayed treatment effects using group sequential designs:

DTEAssurance::assurance_GSD_shiny_app()

Offline Example

You can also use the package offline via the main function:

DTEAssurance::calc_dte_assurance_interim()

This function requires the following arguments:

• n_c: Number of patients in the control group
• n_t: Number of patients in the treatment group
• control_model: A named list specifying the control arm survival distribution
• effect_model: A named list specifying beliefs about the treatment effect
• recruitment_model: A named list specifying the recruitment process
• GSD_model: A named list specifying the group sequential design
• n_sims: Number of simulations to run

An example of this is shown:

control_model <- list(dist = "Exponential", parameter_mode = "Fixed", fixed_type = "Parameters", lambda = 0.08)
effect_model <- list(delay_SHELF = SHELF::fitdist(c(3, 4, 5), probs = c(0.25, 0.5, 0.75), lower = 0, upper = 10),
delay_dist = "gamma",
HR_SHELF = SHELF::fitdist(c(0.55, 0.6, 0.7), probs = c(0.25, 0.5, 0.75), lower = 0, upper = 1.5),
HR_dist = "gamma",
P_S = 0.9, P_DTE = 0.7)
recruitment_model <- list(method = "power", period = 12, power = 1)
GSD_model <- list(events = 450, alpha_spending = c("0.01, 0.025"),
                  beta_spending = c("0.05, 0.1"), IF_vec = c("0.5, 1"))
result <- calc_dte_assurance_interim(n_c = 300, n_t = 300,
                             control_model = control_model,
                             effect_model = effect_model,
                             recruitment_model = recruitment_model,
                             GSD_model = GSD_model,
                             n_sims = 500)

str(result)
#> 'data.frame':    500 obs. of  6 variables:
#>  $ Trial         : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ IF            : chr  "0.5, 1" "0.5, 1" "0.5, 1" "0.5, 1" ...
#>  $ Decision      : chr  "Successful at final" "Stop for efficacy" "Stop for futility" "Stop for futility" ...
#>  $ StopTime      : num  27.1 13.6 12.5 12.9 13.7 ...
#>  $ SampleSize    : int  600 600 600 600 600 600 600 600 600 600 ...
#>  $ Final_Decision: chr  "Successful" "Successful" "Successful" "Successful" ...

If we wish to compare the operating characteristics we can do so by changing the GSD_model and plotting the proportion of outcomes:

GSD_model <- list(events = 450, 
                  alpha_spending = c("0.01, 0.025", "0.01, 0.025", "0.01, 0.025"),
                  beta_spending = c("0.05, 0.1", "0.05, 0.1", "0.05, 0.1"), 
                  IF_vec = c("0.25, 1", "0.5, 1", "0.75, 1"))

result <- calc_dte_assurance_interim(n_c = 300, n_t = 300,
                             control_model = control_model,
                             effect_model = effect_model,
                             recruitment_model = recruitment_model,
                             GSD_model = GSD_model,
                             n_sims = 500)