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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:

Installation

You can install DTEAssurance on CRAN using:

install.packages("DTEAssurance")

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:

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(0.0125, 0.025),
                  alpha_IF = c(0.75, 1),
                  futility_type = "none")
result <- calc_dte_assurance_adaptive(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  5 variables:
#>  $ Trial         : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ Decision      : chr  "Stop for efficacy" "Stop for efficacy" "Successful at final" "Stop for efficacy" ...
#>  $ StopTime      : num  21.6 20.4 26.8 17.9 18.7 ...
#>  $ SampleSize    : int  600 600 600 600 600 600 600 600 600 600 ...
#>  $ Final_Decision: chr  "Successful" "Successful" "Successful" "Successful" ...
design_summary <- result %>%
        summarise(
          Assurance = mean(Decision %in% c("Stop for efficacy", "Successful at final")),
          `Pr(Early Fut.)` = mean(Decision %in% c("Stop for futility")),
          `Pr(Early Eff.)` = mean(Decision %in% c("Stop for efficacy")),
          `Average Duration` = round(mean(StopTime, na.rm = TRUE), 2),
          `Average Sample Size` = round(mean(SampleSize, na.rm = TRUE), 2)
        )


design_summary
#>   Assurance Pr(Early Fut.) Pr(Early Eff.) Average Duration Average Sample Size
#> 1       0.7              0          0.528            22.38                 600