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. In the introduction, the observed outcome variable “tumor growth” was normally distributed. However, the drugdevelopR package is not only restricted to normally distributed outcome variables but also binary distributed outcome variables and a time-to-event outcome variables. In this article we want explain how the setting with time-to-event variables works.

Suppose we are developing a new tumor treatment, *exper*. The
patient variable that we want to investigate is how long the patient
survives without further progression of the tumor (progression-free
survival). This is a time-to-event outcome variable.

Within our drug development program, we will compare our experimental
treatment *exper* to the control treatment *contro*. The
treatment effect measure is given by \(\theta
= −\log(HR)\), which is the negative logarithm of the hazard
ratio \(HR\), which in turn is the
ratio of the hazard rates. The hazard ratio is a little difficult to
understand. A hazard ratio of 0.75 would mean that the momentary rate of
experiencing (the “hazard”) at any point in time is reduced by 75% in
the experimental group.

After installing the package according to the installation instructions, we can load it using the following code:

In order to apply the package to the setting from our example, we need to specify the following parameters:

`hr1`

is our hazard ratio. As already explained above, we assume that our experimental treatment*exper*leads to a hazard reduced by 75% compared to the control treatment*contro*. Therefore, we set`hr1 = 0.75`

. For now, we will not model the hazard ratio on any prior distribution. Thus, we will set`fixed = TRUE`

.`d2min`

and`d2max`

specify the minimal and maximal number of events for the phase II trial. The package will search for the optimal sample size within this region. For now, we want the program to search for the optimal sample size in the interval between 20 and 400 events. In addition, we will tell the program to search this region in steps of four participants at a time by setting`stepd2 = 4`

.`hrgomin`

and`hrgomax`

specify the minimal and maximal threshold value for the go/no-go decision rule in terms of the negative logarithm of the hazard ratio. The package will search for the optimal threshold value within this region. For now, we want the program to search in the interval between 0.7 and 0.9 while going in steps of`stephrgo = 0.01`

. Note that the lower bound of the decision rule set represents the smallest size of treatment effect observed in phase II allowing to go to phase III, so it can be used to model the minimally clinically relevant effect size. Moreover, note that the interval specified above corresponds to the set \(\{-\log(0.9), ..., -\log(0.7)\}\).`xi2`

and`xi3`

correspond to the event rates in phase II and phase III. After calculating the optimal number of events`d2`

and`d3`

, the event rates are used to calculate the optimal sample sizes in phase II and III. We assume event rates of 0.7 in each phase, indicating that 70 events correspond to an optimal sample size of 100, respectively.`c02`

and`c03`

are fixed costs for phase II and phase III respectively. We will set the phase II costs to 100 and the phase III costs to 150 (in \(10^5\)$), i.e. we have fixed costs of 10 000 000$ in phase II and 15 000 000$ in phase III. Note that the currency of the input values does not matter, so an input value for`c02`

of 15 could also be interpreted as fixed costs of 1 500 000€ if necessary.`c2`

and`c3`

are the costs in phase II and phase III per patient. We will set them to be 0.75 in phase II and 0.1 in phase III. Again, these values are given in \(10^5\)$, i.e. we have per patient costs of 75 000$ in phase II and 100 000$ in phase III.`b1`

,`b2`

and`b3`

are the expected small, medium and large benefit categories for successfully launching the treatment on the market for each effect size category in \(10^5\)$. We will define a small benefit of 1000, a medium benefit of 2000, and a large benefit of 3000. The effect size categories directly correspond to the treatment effect: For example, if the treatment effect is between 1 and 0.95 (in terms of the risk ratio) we have a small treatment effect yielding an expected benefit of 100 000 000$.`alpha`

is the specified one-sided significance level. We will set`alpha = 0.025`

.- 1 -
`beta`

is the minimal power that we require for our drug development program. We will set`beta = 0.1`

, meaning that we require a power of 90%.

As in the setting with normally distributed outcomes, the treatment effect may be fixed (as in this example) or may be distributed with respect to a prior distribution. Furthermore, all options to adapt the program to your specific needs are also available in this setting.

Now that we have defined all parameters needed for our example, we
are ready to feed them to the package. We will use the function
`optimal_tte()`

, which calculates the optimal sample size and
the optimal threshold value for a time-to-event outcome variable.

```
res <- optimal_tte(w = 0.3, # define parameters for prior
hr1 = 0.75, hr2 = 0.8, id1 = 210, id2 = 420, # (https://web.imbi.uni-heidelberg.de/prior/)
d2min = 20, d2max = 400, stepd2 = 5, # define optimization set for d2
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.01, # define optimization set for HRgo
alpha = 0.025, beta = 0.1, xi2 = 0.7, xi3 = 0.7, # drug development planning parameters
c2 = 0.75, c3 = 1, c02 = 100, c03 = 150, # define fixed and variable costs
K = Inf, N = Inf, S = -Inf, # set constraints
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85, # define boundary for effect size categories
b1 = 1000, b2 = 2000, b3 = 3000, # define expected benefits
gamma = 0, # assume different/same population structures
fixed = TRUE, # choose if effects are fixed or random
skipII = FALSE, # more parameters
num_cl = 1)
```

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

```
res
#> Optimization result:
#> Utility: 377.1
#> Sample size:
#> phase II: 236, phase III: 614, total: 850
#> Expected number of events:
#> phase II: 165, phase III: 430, total: 595
#> Assumed event rate:
#> phase II: 0.7, phase III: 0.7
#> Probability to go to phase III: 0.81
#> Total cost:
#> phase II: 277, phase III: 736, cost constraint: Inf
#> Fixed cost:
#> phase II: 100, phase III: 150
#> Variable cost per patient:
#> phase II: 0.75, phase III: 1
#> Effect size categories (expected gains):
#> small: 1 (1000), medium: 0.95 (2000), large: 0.85 (3000)
#> Success probability: 0.62
#> Success probability by effect size:
#> small: 0.09, medium: 0.29, large: 0.24
#> Significance level: 0.025
#> Targeted power: 0.9
#> Decision rule threshold: 0.86 [HRgo]
#> Assumed true effect: 0.75 [hr]
#> Treatment effect offset between phase II and III: 0 [gamma]
```

The program returns a total of sixteen output values and the input parameters. For now, we will only look at the most important ones:

`res$d2`

is the optimal number of events for phase II and`res$d3`

the resulting number of events for phase III. We see that the optimal scenario requires 165 events in phase II and 430 events in phase III, which correspond to 236 participants in phase II and 614 in phase III.`res$HRgo`

is the optimal threshold value for the go/no-go decision rule. We see that we need a hazard ratio of less than 0.86 in phase II in order to proceed to phase III.`res$u`

is the expected utility of the program for the optimal sample size and threshold value. In our case it amounts to 377.1, i.e. we have an expected utility of 37 710 000$.

In this article we introduced the setting, when the outcome is a time-to-event variable. For more information on how to use the package, see:

*Introduction to drugdevelopR:*See how the package works in a basic normally distributed example.*Different outcomes:*Apply it to binary endpoints.*Interpreting the rest of the output:*Obtain further details on your drug development program.*Fixed or prior:*Model the assumed treatment effect on a prior distribution.*More parameters:*Define custom effect size categories. Put constraints on the optimization by defining maximum costs, the total expected sample size of the program or the minimum expected probability of a successful program. Define an expected difference in treatment effect between phase II and III. Skip phase II.*Complex drug development programs:*Adapt to situations with biased effect estimators, multiple phase III trials, multi-arm trials, or multiple endpoints.*Parallel computing:*Be faster at calculating the optimum by using parallel computing.