Interpreting the rest of the output

Johannes Cepicka

Suppose we are planning a drug development program testing the superiority of an experimental treatment over a control treatment. Our drug development program consists of an exploratory phase II trial which is, in case of promising results, followed by a confirmatory phase III trial.

The drugdevelopR package enables us to optimally plan such programs using a utility-maximizing approach. To get a brief introduction, we presented a very basic example on how the package works in Introduction to planning phase II and phase III trials with drugdevelopR. As we only discussed the most important results of the function in the introduction, we now want to interpret the rest of the output.

The example setting and the input parameters

We are in the same setting as in the introduction, i.e. we suppose we are developing a new tumor treatment, exper. The patient variable that we want to investigate is the difference in tumor width between the one-year visit and baseline. This is a normally distributed outcome variable.

The parameters we insert into the function optimum_normal are the same parameters we also inserted in the basic setting. Thus, for more information on the input values we refer to the Introduction. We start by loading the drugdevelopR package.


Then we insert the input values into the function optimal_normal:

 res <- optimal_normal(Delta1 = 0.625, fixed = TRUE, # treatment effect
                       n2min = 20, n2max = 400, # sample size region
                       stepn2 = 4, # sample size step size
                       kappamin = 0.02, kappamax = 0.2, # threshold region
                       stepkappa = 0.02, # threshold step size
                       c2 = 0.675, c3 = 0.72, # maximal total trial costs
                       c02 = 15, c03 = 20, # maximal per-patient costs
                       b1 = 3000, b2 = 8000, b3 = 10000, # gains for patients
                       alpha = 0.025, # significance level
                       beta = 0.1, # 1 - power
                       Delta2 = NULL, w = NULL, in1 = NULL, in2 = NULL, 
                       a = NULL,b = NULL) # setting all unneeded parameters to NULL

Interpreting the output

After setting all these input parameters and running the function, let’s take a look at the output of the program.

#> Optimization result:
#>  Utility: 2946.07
#>  Sample size:
#>    phase II: 92, phase III: 192, total: 284
#>  Probability to go to phase III: 1
#>  Total cost:
#>    phase II: 77, phase III: 158, cost constraint: Inf
#>  Fixed cost:
#>    phase II: 15, phase III: 20
#>  Variable cost per patient:
#>    phase II: 0.675, phase III: 0.72
#>  Effect size categories (expected gains):
#>   small: 0 (3000), medium: 0.5 (8000), large: 0.8 (10000)
#>  Success probability: 0.85
#>  Success probability by effect size:
#>    small: 0.72, medium: 0.12, large: 0
#>  Significance level: 0.025
#>  Targeted power: 0.9
#>  Decision rule threshold: 0.06 [Kappa] 
#>  Assumed true effect: 0.625 [Delta] 
#>  Treatment effect offset between phase II and III: 0 [gamma]

The program returns a total of thirteen values and the input values. We will now take a closer look and explain all output values with the necessary detail:

Where to go from here

This tutorial explains how we can interpret all of the output generated by our program.

For more information on how to use the package, see: