Utility‐based optimization of phase II/III programs

Kirchner, Marietta; Kieser, Meinhard; Götte, Heiko; Schueler, Armin

doi:10.1002/sim.6624

Cited by 19 publications

(34 citation statements)

References 24 publications

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“…For example, a budget constraint (e.g., restrict the phase II and III drug development program planning to those designs d which satisfy

{normalE}_{θ, {\overset{θ}{true}}_{2}} false[c_{m} false(d false) false] \leq C

, for a suitably chosen budget constraint C ), modeling the life cycle of the drug as described by Patel and Ankolekar (), or a constraint of sample size in phase II, which are among others discussed in Kirchner et al. (), may be realized without difficulty. Further, also multiple, constraints like specification of a maximal n 3 or minimal

E_{θ} false[s P false]

are also possible, as well as alternative phase III sample size calculation approaches, other prior distributions for the treatment effect, different go/no‐go decision rules or dissimilar effect size categories for each trial and/or the whole program.…”

Section: Discussionmentioning

confidence: 99%

“…The normalized log‐rank test statistics T 3 given

{\hat{θ}}_{2}

and θ is asymptotically normally distributed with expectation

θ / \sqrt{4 / N_{3}}

and variance 1. As defined before (De Martini, ; Kirchner et al., ), the probability of a successful program ( sP ), which is the joint probability of going to phase III and achieving a significant result, is given by

0true normalP ((), s P false| θ) = \int_{z_{1 - α}}^{\infty} \int_{κ}^{\infty} f ((), t_{3} | {\hat{θ}}_{2}, θ) \cdot f ((), {\hat{θ}}_{2} | θ) d {\hat{θ}}_{2} d t_{3},

where t 3 is a realization of T 3 , assuming a fixed value for the true treatment effect θ. To take the uncertainty about the treatment effect into account, θ may be modeled by a prior distribution.…”

Section: Notation and Settingmentioning

confidence: 99%

“…In this paper, the parameters are chosen to fit those of Kirchner et al. () to enable comparison of results. The event rates for phase II and III are set to

π_{i} = 0.7

for i = 2, 3 in all cases and strategies.…”

Section: Optimization Of Phase Ii/iii Programs Where Several Phase IImentioning

confidence: 99%

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Optimal sample size allocation and go/no‐go decision rules for phase II/III programs where several phase III trials are performed

2018

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The conduct of phase II and III programs is costly, time-consuming and, due to high failure rates in late development stages, risky. There is a strong connection between phase II and III trials as the go/no-go decision and the sample size chosen for phase III are based on the results observed in phase II. An integrated planning of phase II and III is therefore reasonable. The success of phase II/III programs crucially depends on the allocation of the resources to phase II and III in terms of sample size and the rule applied to decide whether to stop or to proceed with phase III. Recently, a utility-based approach was proposed, where optimal planning of phase II/III programs is achieved by taking fixed and variable costs of the drug development program and potential gains after a successful launch into account. However, this method is restricted to programs with a single phase III trial, while regulatory authorities usually require statistical significance in two or more phase III trials. We present a generalization of this procedure to programs where two or more phase III trials are performed. Optimal phase II sample sizes and go/no-go decision rules are provided for time-to-event outcomes and cases, where at least one, two, or three phase III trials need to be successful. Different drug development program strategies (e.g. one large vs. two phase III trials) are compared within these different cases. Application to practical examples typically met in oncology trials illustrates the proposed method.

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“…For example, a budget constraint (e.g., restrict the phase II and III drug development program planning to those designs d which satisfy

{normalE}_{θ, {\overset{θ}{true}}_{2}} false[c_{m} false(d false) false] \leq C

E_{θ} false[s P false]

Section: Discussionmentioning

confidence: 99%

“…The normalized log‐rank test statistics T 3 given

{\hat{θ}}_{2}

and θ is asymptotically normally distributed with expectation

θ / \sqrt{4 / N_{3}}

0true normalP ((), s P false| θ) = \int_{z_{1 - α}}^{\infty} \int_{κ}^{\infty} f ((), t_{3} | {\hat{θ}}_{2}, θ) \cdot f ((), {\hat{θ}}_{2} | θ) d {\hat{θ}}_{2} d t_{3},

Section: Notation and Settingmentioning

confidence: 99%

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Optimal sample size allocation and go/no‐go decision rules for phase II/III programs where several phase III trials are performed

2018

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“…Again, the values to be applied for α * and 1 − β * and the rule for determining n 3 from n 3, i , i = 1, … k , are chosen depending on the application situation and are concretized below. For optimizing phase II/III programs, we consider the following quantities Probability of a go decision after phase II: p go = P ( GO ∣ θ ). Average power ( AP ) for phase III for a defined success criterion S 3 given a go decision after phase II:

italicAP ((), θ) = P (∣(), S_{3}, ∣ bold-italicθ, italicGO) = \frac{P (() S_{3}, italicGO ∣ θ)}{p_{go}}

. Probability of a successful program ( POSP ), ie, achieving a go decision after phase II trial and a successful phase III trial: POSP ( θ ) = P ( S 3 ∣ θ , GO ) · p go . Expected probability of a successful program ( EPOSP ): As the assumption on θ is crucial for the value obtained for POSP and as, in the planning stage of a phase II/III program, there is usually considerable uncertainty with respect to the true treatment effect, it is appropriate to model the current knowledge on θ with a prior.…”

Section: Notation and Settingmentioning

confidence: 99%

“…Here and in the following, we denote by f (·) the density of the distribution function of the respective argument. Utility function: We aim at optimizing the phase II/III program by maximizing the overall value, which is captured by a utility function. In analogy to the study of Kirchner et al, the utility u is given by the difference between the costs for conducting the program c and the prospective gain after program success g . We distinguish fixed costs c 02 and c 03 for the phase II and phase III trials, respectively, which are required to set up the infrastructure and which incur independent of the sample size, and per‐patient costs c 2 and c 3 .…”

Section: Notation and Settingmentioning

confidence: 99%

Optimal planning of phase II/III programs for clinical trials with multiple endpoints

Kieser

Kirchner

Dölger

et al. 2018

Pharmaceutical Statistics

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Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the "all-or-none" and "at-least-one" win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology.

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Empowering phase II clinical trials to reduce phase III failures

Martini

2019

Pharmaceutical Statistics

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The large number of failures in phase III clinical trials, which occur at a rate of approximately 45%, is studied herein relative to possible countermeasures. First, the phenomenon of failures is numerically described. Second, the main reasons for failures are reported, together with some generic improvements suggested in the related literature. This study shows how statistics explain, but do not justify, the high failure rate observed. The rate of failures due to a lack of efficacy that are not expected, is considered to be at least 10%. Expanding phase II is the simplest and most intuitive way to reduce phase III failures since it can reduce phase III false negative findings and launches of phase III trials when the treatment is positive but suboptimal. Moreover, phase II enlargement is discussed using an economic profile. As resources for research are often limited, enlarging phase II should be evaluated on a case-by-case basis. Alternative strategies, such as biomarker-based enrichments and adaptive designs, may aid in reducing failures. However, these strategies also have very low application rates with little likelihood of rapid growth.

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Utility‐based optimization of phase II/III programs

Cited by 19 publications

References 24 publications

Optimal sample size allocation and go/no‐go decision rules for phase II/III programs where several phase III trials are performed

Optimal sample size allocation and go/no‐go decision rules for phase II/III programs where several phase III trials are performed

Optimal planning of phase II/III programs for clinical trials with multiple endpoints

Empowering phase II clinical trials to reduce phase III failures

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