A Bayesian approach for incorporating economic factors in sample size design for clinical trials of individual drugs and portfolios of drugs

Patel, Nitin R.; Ankolekar, Suresh

doi:10.1002/sim.2955

Cited by 30 publications

(24 citation statements)

References 18 publications

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“…However, more complex or alternative settings can easily be implemented. 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.…”

Section: Discussionmentioning

confidence: 99%

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

Section: Discussionmentioning

confidence: 99%

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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“…Most papers use ENPV to measure return with different extensions to include risk, whereas others used real options theory in their modeling . We use ENPV in a decision tree analysis implemented in an IP model, as carried out by Patel and Ankolekar . We decided not to use the real options approach because we agree with that it is difficult to implement it in practice.…”

Section: A Mathematical Model For Optimizing Clinical Research For a mentioning

confidence: 99%

“…Following Patel and Ankolekar [13], we use a Bayesian prior at the design stage to determine the sample size while assuming that the data resulting from the trial will be analyzed using classical Neyman-Pearson methods. Spiegelhalter et al [27] called this the 'hybrid classical Bayesian' approach.…”

Section: Overview Of the Modelmentioning

confidence: 99%

A mathematical model for maximizing the value of phase 3 drug development portfolios incorporating budget constraints and risk

Patel

Ankolekar

Antonijevic

et al. 2013

Statistics in Medicine

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We describe a value-driven approach to optimizing pharmaceutical portfolios. Our approach incorporates inputs from research and development and commercial functions by simultaneously addressing internal and external factors. This approach differentiates itself from current practices in that it recognizes the impact of study design parameters, sample size in particular, on the portfolio value. We develop an integer programming (IP) model as the basis for Bayesian decision analysis to optimize phase 3 development portfolios using expected net present value as the criterion. We show how this framework can be used to determine optimal sample sizes and trial schedules to maximize the value of a portfolio under budget constraints. We then illustrate the remarkable flexibility of the IP model to answer a variety of 'what-if' questions that reflect situations that arise in practice. We extend the IP model to a stochastic IP model to incorporate uncertainty in the availability of drugs from earlier development phases for phase 3 development in the future. We show how to use stochastic IP to re-optimize the portfolio development strategy over time as new information accumulates and budget changes occur.

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“…The design will consider two types of costs, fixed and variable costs . The fixed costs are costs of setting up and running the trials, which do not depend on the size of the trial.…”

Section: Optimal Design For a Series Of Decision‐theoretic Phase II Tmentioning

confidence: 99%

Designing a series of decision‐theoretic phase II trials in a small population

Hee

Stallard

2012

Statistics in Medicine

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This paper introduces a decision-theoretic design for a series of phase II trials. Instead of designing phase II trials individually, we proposed a development plan that consists of a series of phase II trials and one phase III trial such that the long-term expected utility on the whole is optimized. The phase II trials are conducted sequentially, and patients are recruited sequentially to each phase II trial. At each interim stage, a decision is made to continue recruiting patients to the current trial, to stop and recommend the treatment proceeds to a phase III trial, to stop and initiate a new phase II trial or to stop and cease the development plan. The methodology uses a hybrid approach in which it is assumed that the data from the final phase III trial will be analysed using a classical frequentist hypothesis test. The expected power of this test based on some specified prior distribution for the effect of the experimental treatment is then used in a utility function, which is used to obtain the optimal design for the whole series of trials.

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A Bayesian approach for incorporating economic factors in sample size design for clinical trials of individual drugs and portfolios of drugs

Cited by 30 publications

References 18 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

A mathematical model for maximizing the value of phase 3 drug development portfolios incorporating budget constraints and risk

Designing a series of decision‐theoretic phase II trials in a small population

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