1
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Willigers BJ, Nagarajan S, Ghiorghui S, Darken P, Lennard S. Algorithmic benchmark modulation: A novel method to develop success rates for clinical studies. Clin Trials 2024; 21:220-232. [PMID: 38126256 DOI: 10.1177/17407745231207858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
BACKGROUND High-quality decision-making in the pharmaceutical industry requires accurate assessments of the Probability of Technical Success of clinical trials. Failure to do so will lead to lost opportunities for both patients and investors. Pharmaceutical companies employ different methodologies to determine Probability of Technical Success values. Some companies use power and assurance calculations; others prefer to use industry benchmarks with or without the overlay of subjective modulations. At AstraZeneca, both assurance calculations and industry benchmarks are used, and both methods are combined with modulations. METHODS AstraZeneca has recently implemented a simple algorithm that allows for modulation of a Probability of Technical Success value. The algorithm is based on a set of multiple-choice questions. These questions cover a comprehensive set of issues that have historically been considered by AstraZeneca when subjective modulations to Probability of Technical Success values were made but do so in a much more structured way. RESULTS A set of 57 phase 3 Probability of Technical Success assessments suggests that AstraZeneca's historical estimation of Probability of Technical Success has been reasonably accurate. A good correlation between the subjective modulation and the modulation algorithm was found. This latter observation, combined with the finding that historically AstraZeneca has been reasonably accurate in its estimation of Probability of Technical Success, gives confidence in the validity of the novel method. DISCUSSION Although it is too early to demonstrate whether the method has improved the accuracy of company's Probability of Technical Success assessments, we present our data and analysis here in the hope that it may assist the pharmaceutical industry in addressing this key challenge. This new methodology, developed for pivotal studies, enables AstraZeneca to develop more consistent Probability of Technical Success assessments with less effort and can be used to adjust benchmarks as well as assurance calculations. CONCLUSION The Probability of Technical Success modulation algorithm addresses several concerns generally associated with assurance calculations or benchmark without modulation: selection biases, situations where little relevant prior data are available and the difficulty to model many factors affecting study outcomes. As opposed to using industry benchmarks, the Probability of Technical Success modulation algorithm allows to accommodate project-specific considerations.
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2
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Quan H, Xu Z, Luo J, Paux G, Cho M, Chen X. Utilization of treatment effect on a surrogate endpoint for planning a study to evaluate treatment effect on a final endpoint. Pharm Stat 2023. [PMID: 36866697 DOI: 10.1002/pst.2298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
To design a phase III study with a final endpoint and calculate the required sample size for the desired probability of success, we need a good estimate of the treatment effect on the endpoint. It is prudent to fully utilize all available information including the historical and phase II information of the treatment as well as external data of the other treatments. It is not uncommon that a phase II study may use a surrogate endpoint as the primary endpoint and has no or limited data for the final endpoint. On the other hand, external information from the other studies for the other treatments on the surrogate and final endpoints may be available to establish a relationship between the treatment effects on the two endpoints. Through this relationship, making full use of the surrogate information may enhance the estimate of the treatment effect on the final endpoint. In this research, we propose a bivariate Bayesian analysis approach to comprehensively deal with the problem. A dynamic borrowing approach is considered to regulate the amount of historical data and surrogate information borrowing based on the level of consistency. A much simpler frequentist method is also discussed. Simulations are conducted to compare the performances of different approaches. An example is used to illustrate the applications of the methods.
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Affiliation(s)
- Hui Quan
- Biostatistics and Programming, Sanofi, Bridgewater, New Jersey, USA
| | - Zhixing Xu
- Biostatistics and Programming, Sanofi, Bridgewater, New Jersey, USA
| | - Junxiang Luo
- Biostatistics and Programming, Moderna, Cambridge, Massachusetts, USA
| | - Gautier Paux
- Biostatistics and Programming, Sanofi, Bridgewater, New Jersey, USA
| | - Meehyung Cho
- Biostatistics and Programming, Sanofi, Bridgewater, New Jersey, USA
| | - Xun Chen
- Biostatistics and Programming, Sanofi, Bridgewater, New Jersey, USA
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3
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Graham E, Harbron C, Jaki T. Updating the probability of study success for combination therapies using related combination study data. Stat Methods Med Res 2023; 32:712-731. [PMID: 36776025 PMCID: PMC10363930 DOI: 10.1177/09622802231151218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Combination therapies are becoming increasingly used in a range of therapeutic areas such as oncology and infectious diseases, providing potential benefits such as minimising drug resistance and toxicity. Sets of combination studies may be related, for example, if they have at least one treatment in common and are used in the same indication. In this setting, value can be gained by sharing information between related combination studies. We present a framework that allows the study success probabilities of a set of related combination therapies to be updated based on the outcome of a single combination study. This allows us to incorporate both direct and indirect data on a combination therapy in the decision-making process for future studies. We also provide a robustification that accounts for the fact that the prior assumptions on the correlation structure of the set of combination therapies may be incorrect. We show how this framework can be used in practice and highlight the use of the study success probabilities in the planning of clinical studies.
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Affiliation(s)
- Emily Graham
- STOR-i Centre for Doctoral Training, 4396Lancaster University, Lancaster, UK
| | | | - Thomas Jaki
- 9147University of Regensburg, Regensburg, Germany.,MRC Biostatistics Unit, 2152University of Cambridge, Cambridge, UK
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4
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Zhang Z, Lin Y, Liu J. Probability of Study Success (PrSS) Evaluation Based on Multiple Endpoints in Late Phase Oncology Drug Development. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2120532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Affiliation(s)
- Zhen Zhang
- Otsuka Pharmaceutical Development and Commercialization Inc.
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5
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Okwuokenye M. Quantitative Decision Under Unequal Covariances and Post-Treatment Variances: A Kidney Disease Application. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2020.1864464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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6
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Stylianou A, Blanks KJH, Gibson RA, Kendall LK, English M, Williams S, Mehta R, Clarke A, Kanyuuru L, Aluvaala J, Darmstadt GL. Quantitative decision making for investment in global health intervention trials: Case study of the NEWBORN study on emollient therapy in preterm infants in Kenya. J Glob Health 2022; 12:04045. [PMID: 35972445 PMCID: PMC9185187 DOI: 10.7189/jogh.12.04045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background Partners from an NGO, academia, industry and government applied a tool originating in the private sector – Quantitative Decision Making (QDM) – to rigorously assess whether to invest in testing a global health intervention. The proposed NEWBORN study was designed to assess whether topical emollient therapy with sunflower seed oil in infants with very low birthweight <1500 g in Kenya would result in a significant reduction in neonatal mortality compared to standard of care. Methods The QDM process consisted of prior elicitation, modelling of prior distributions, and simulations to assess Probability of Success (PoS) via assurance calculations. Expert opinion was elicited on the probability that emollient therapy with sunflower seed oil will have any measurable benefit on neonatal mortality based on available evidence. The distribution of effect sizes was modelled and trial data simulated using Statistical Analysis System to obtain the overall assurance which represents the PoS for the planned study. A decision-making framework was then applied to characterise the ability of the study to meet pre-selected decision-making endpoints. Results There was a 47% chance of a positive outcome (defined as a significant relative reduction in mortality of ≥15%), a 45% chance of a negative outcome (defined as a significant relative reduction in mortality <10%), and an 8% chance of ending in the consider zone (ie, a mortality reduction of 10 to <15%) for infants <1500 g. Conclusions QDM is a novel tool from industry which has utility for prioritisation of investments in global health, complementing existing tools [eg, Child Health and Nutrition Research Initiative]. Results from application of QDM to the NEWBORN study suggests that it has a high probability of producing clear results. Findings encourage future formation of public-private partnerships for health.
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Affiliation(s)
- Annie Stylianou
- GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage, Hertfordshire, UK
| | | | - Rachel A Gibson
- GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage, Hertfordshire, UK
| | - Lindsay K Kendall
- GlaxoSmithKline R&D, Gunnels Wood Road, Stevenage, Hertfordshire, UK
| | - Mike English
- Oxford Centre for Global Health Research, Nuffield Department of Clinical Medicine, Oxford, UK
- KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
| | | | | | | | - Lynn Kanyuuru
- Save the Children International, Kenya Country Office, Nairobi, Kenya
| | - Jalemba Aluvaala
- KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
- Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya
| | - Gary L Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
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7
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Singh J, Abrams KR, Bujkiewicz S. Incorporating single-arm studies in meta-analysis of randomised controlled trials: a simulation study. BMC Med Res Methodol 2021; 21:114. [PMID: 34082702 PMCID: PMC8176581 DOI: 10.1186/s12874-021-01301-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 04/21/2021] [Indexed: 01/09/2023] Open
Abstract
Background Use of real world data (RWD) from non-randomised studies (e.g. single-arm studies) is increasingly being explored to overcome issues associated with data from randomised controlled trials (RCTs). We aimed to compare methods for pairwise meta-analysis of RCTs and single-arm studies using aggregate data, via a simulation study and application to an illustrative example. Methods We considered contrast-based methods proposed by Begg & Pilote (1991) and arm-based methods by Zhang et al (2019). We performed a simulation study with scenarios varying (i) the proportion of RCTs and single-arm studies in the synthesis (ii) the magnitude of bias, and (iii) between-study heterogeneity. We also applied methods to data from a published health technology assessment (HTA), including three RCTs and 11 single-arm studies. Results Our simulation study showed that the hierarchical power and commensurate prior methods by Zhang et al provided a consistent reduction in uncertainty, whilst maintaining over-coverage and small error in scenarios where there was limited RCT data, bias and differences in between-study heterogeneity between the two sets of data. The contrast-based methods provided a reduction in uncertainty, but performed worse in terms of coverage and error, unless there was no marked difference in heterogeneity between the two sets of data. Conclusions The hierarchical power and commensurate prior methods provide the most robust approach to synthesising aggregate data from RCTs and single-arm studies, balancing the need to account for bias and differences in between-study heterogeneity, whilst reducing uncertainty in estimates. This work was restricted to considering a pairwise meta-analysis using aggregate data. Supplementary Information The online version contains supplementary material available at (10.1186/s12874-021-01301-1).
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Affiliation(s)
- Janharpreet Singh
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
| | - Keith R Abrams
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.,Centre for Health Economics, University of York, York, UK
| | - Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
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8
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Temple JR, Robertson JR. Conditional assurance: the answer to the questions that should be asked within drug development. Pharm Stat 2021; 20:1102-1111. [PMID: 33960600 PMCID: PMC9291040 DOI: 10.1002/pst.2128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 11/08/2022]
Abstract
In this paper, we extend the use of assurance for a single study to explore how meeting a study's pre-defined success criteria could update our beliefs about the true treatment effect and impact the assurance of subsequent studies. This concept of conditional assurance, the assurance of a subsequent study conditional on success in an initial study, can be used assess the de-risking potential of the study requiring immediate investment, to ensure it provides value within the overall development plan. If the planned study does not discharge sufficient later phase risk, alternative designs and/or success criteria should be explored. By transparently laying out the different design options and the risks associated, this allows for decision makers to make quantitative investment choices based on their risk tolerance levels and potential return on investment. This paper lays out the derivation of conditional assurance, discusses how changing the design of a planned study will impact the conditional assurance of a future study, as well as presenting a simple illustrative example of how this methodology could be used to transparently compare development plans to aid decision making within an organisation.
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9
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Götte H, Xiong J, Kirchner M, Demirtas H, Kieser M. Optimal decision‐making in oncology development programs based on probability of success for phase
III
utilizing phase
II
/
III
data on response and overall survival. Pharm Stat 2020; 19:861-881. [DOI: 10.1002/pst.2042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/18/2020] [Accepted: 05/27/2020] [Indexed: 11/10/2022]
Affiliation(s)
| | | | - Marietta Kirchner
- Institute of Medical Biometry and Informatics University of Heidelberg Heidelberg Germany
| | - Hakan Demirtas
- Division of Epidemiology and Biostatistics University of Illinois Chicago Illinois USA
| | - Meinhard Kieser
- Institute of Medical Biometry and Informatics University of Heidelberg Heidelberg Germany
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10
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Saint-Hilary G, Barboux V, Pannaux M, Gasparini M, Robert V, Mastrantonio G. Predictive probability of success using surrogate endpoints. Stat Med 2018; 38:1753-1774. [PMID: 30548627 DOI: 10.1002/sim.8060] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 10/29/2018] [Accepted: 11/19/2018] [Indexed: 12/30/2022]
Abstract
The predictive probability of success of a future clinical trial is a key quantitative tool for decision-making in drug development. It is derived from prior knowledge and available evidence, and the latter typically comes from the accumulated data on the clinical endpoint of interest in previous clinical trials. However, a surrogate endpoint could be used as primary endpoint in early development and, usually, no or limited data are collected on the clinical endpoint of interest. We propose a general, reliable, and broadly applicable methodology to predict the success of a future trial from surrogate endpoints, in a way that makes the best use of all the available evidence. The predictions are based on an informative prior, called surrogate prior, derived from the results of past trials on one or several surrogate endpoints. If available, in a Bayesian framework, this prior could be combined with data from past trials on the clinical endpoint of interest. Two methods are proposed to address a potential discordance between the surrogate prior and the data on the clinical endpoint. We investigate the patterns of behavior of the predictions in a comprehensive simulation study, and we present an application to the development of a drug in Multiple Sclerosis. The proposed methodology is expected to support decision-making in many different situations, since the use of predictive markers is important to accelerate drug developments and to select promising drug candidates, better and earlier.
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Affiliation(s)
- Gaelle Saint-Hilary
- Dipartimento di Scienze Matematiche (DISMA) Giuseppe Luigi Lagrange, Politecnico di Torino, Turin, Italy
| | - Valentine Barboux
- Department of Biostatistics, Institut de Recherches Internationales Servier (IRIS), Suresnes, France
| | - Matthieu Pannaux
- Department of Biostatistics, Institut de Recherches Internationales Servier (IRIS), Suresnes, France
| | - Mauro Gasparini
- Dipartimento di Scienze Matematiche (DISMA) Giuseppe Luigi Lagrange, Politecnico di Torino, Turin, Italy
| | - Veronique Robert
- Department of Biostatistics, Institut de Recherches Internationales Servier (IRIS), Suresnes, France
| | - Gianluca Mastrantonio
- Dipartimento di Scienze Matematiche (DISMA) Giuseppe Luigi Lagrange, Politecnico di Torino, Turin, Italy
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11
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Huang B, Talukder E, Han L, Kuan PF. Quantitative decision-making in randomized Phase II studies with a time-to-event endpoint. J Biopharm Stat 2018; 29:189-202. [PMID: 29969380 DOI: 10.1080/10543406.2018.1489400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
One of the most critical decision points in clinical development is Go/No-Go decision-making after a proof-of-concept study. Traditional decision-making relies on a formal hypothesis testing with control of type I and type II error rates, which is limited by assessing the strength of efficacy evidence in a small isolated trial. In this article, we propose a quantitative Bayesian/frequentist decision framework for Go/No-Go criteria and sample size evaluation in Phase II randomized studies with a time-to-event endpoint. By taking the uncertainty of treatment effect into consideration, we propose an integrated quantitative approach for a program when both the Phase II and Phase III trials share a common endpoint while allowing a discount of the observed Phase II data. Our results confirm the argument that an increase in the sample size of a Phase II trial will result in greater increase in the probability of success of a Phase III trial than increasing the Phase III trial sample size by equal amount. We illustrate the steps in quantitative decision-making with a real example of a randomized Phase II study in metastatic pancreatic cancer.
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Affiliation(s)
- Bo Huang
- a Pfizer Inc ., Groton , CT , USA
| | | | - Lixin Han
- b Sarepta Therapeutics , Cambridge , MA , USA
| | - Pei-Fen Kuan
- c Department of Applied Math and Statistics , Stony Brook University , Stony Brook , NY , USA
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12
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Crisp A, Miller S, Thompson D, Best N. Practical experiences of adopting assurance as a quantitative framework to support decision making in drug development. Pharm Stat 2018; 17:317-328. [PMID: 29635777 DOI: 10.1002/pst.1856] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 01/25/2018] [Accepted: 02/08/2018] [Indexed: 11/08/2022]
Abstract
All clinical trials are designed for success of their primary objectives. Hence, evaluating the probability of success (PoS) should be a key focus at the design stage both to support funding approval from sponsor governance boards and to inform trial design itself. Use of assurance-that is, expected success probability averaged over a prior probability distribution for the treatment effect-to quantify PoS of a planned study has grown across the industry in recent years, and has now become routine within the authors' company. In this paper, we illustrate some of the benefits of systematically adopting assurance as a quantitative framework to support decision making in drug development through several case-studies where evaluation of assurance has proved impactful in terms of trial design and in supporting governance-board reviews of project proposals. In addition, we describe specific features of how the assurance framework has been implemented within our company, highlighting the critical role that prior elicitation plays in this process, and illustrating how the overall assurance calculation may be decomposed into a sequence of conditional PoS estimates which can provide greater insight into how and when different development options are able to discharge risk.
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Affiliation(s)
- Adam Crisp
- GlaxoSmithKline, Uxbridge, Middlesex, UK
| | | | | | - Nicky Best
- GlaxoSmithKline, Uxbridge, Middlesex, UK
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13
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Dunyak J, Mitchell P, Hamrén B, Helmlinger G, Matcham J, Stanski D, Al-Huniti N. Integrating dose estimation into a decision-making framework for model-based drug development. Pharm Stat 2018; 17:155-168. [PMID: 29322659 DOI: 10.1002/pst.1841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 09/11/2017] [Accepted: 10/10/2017] [Indexed: 12/12/2022]
Abstract
Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).
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14
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Götte H, Kirchner M, Sailer MO, Kieser M. Simulation-based adjustment after exploratory biomarker subgroup selection in phase II. Stat Med 2017; 36:2378-2390. [PMID: 28436046 DOI: 10.1002/sim.7294] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 03/08/2017] [Indexed: 01/08/2023]
Abstract
As part of the evaluation of phase II trials, it is common practice to perform exploratory subgroup analyses with the aim of identifying patient populations with a beneficial treatment effect. When investigating targeted therapies, these subgroups are typically defined by biomarkers. Promising results may lead to the decision to select the respective subgroup as the target population for a subsequent phase III trial. However, a selection based on a large observed treatment effect may potentially induce an upwards-bias leading to over-optimistic expectations on the success probability of the phase III trial. We describe how Approximate Bayesian Computation techniques can be used to derive a simulation-based bias adjustment method in this situation. Recommendations for the implementation of the approach are given. Simulation studies show that the proposed method reduces bias substantially compared with the maximum likelihood estimator. The procedure is illustrated with data from an oncology trial. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
| | - Marietta Kirchner
- Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
| | | | - Meinhard Kieser
- Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
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15
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Frewer P, Mitchell P, Watkins C, Matcham J. Decision-making in early clinical drug development. Pharm Stat 2016; 15:255-63. [DOI: 10.1002/pst.1746] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 01/14/2016] [Accepted: 02/11/2016] [Indexed: 12/26/2022]
Affiliation(s)
- Paul Frewer
- Early Clinical Development Biometrics; AstraZeneca; Royston UK
| | - Pat Mitchell
- Early Clinical Development Biometrics; AstraZeneca; Royston UK
| | | | - James Matcham
- Early Clinical Development Biometrics; AstraZeneca; Royston UK
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16
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17
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Bellanti F, van Wijk RC, Danhof M, Della Pasqua O. Integration of PKPD relationships into benefit-risk analysis. Br J Clin Pharmacol 2015; 80:979-91. [PMID: 25940398 DOI: 10.1111/bcp.12674] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2014] [Revised: 04/10/2015] [Accepted: 04/17/2015] [Indexed: 12/19/2022] Open
Abstract
AIM Despite the continuous endeavour to achieve high standards in medical care through effectiveness measures, a quantitative framework for the assessment of the benefit-risk balance of new medicines is lacking prior to regulatory approval. The aim of this short review is to summarise the approaches currently available for benefit-risk assessment. In addition, we propose the use of pharmacokinetic-pharmacodynamic (PKPD) modelling as the pharmacological basis for evidence synthesis and evaluation of novel therapeutic agents. METHODS A comprehensive literature search has been performed using MESH terms in PubMed, in which articles describing benefit-risk assessment and modelling and simulation were identified. In parallel, a critical review of multi-criteria decision analysis (MCDA) is presented as a tool for characterising a drug's safety and efficacy profile. RESULTS A definition of benefits and risks has been proposed by the European Medicines Agency (EMA), in which qualitative and quantitative elements are included. However, in spite of the value of MCDA as a quantitative method, decisions about benefit-risk balance continue to rely on subjective expert opinion. By contrast, a model-informed approach offers the opportunity for a more comprehensive evaluation of benefit-risk balance before extensive evidence is generated in clinical practice. CONCLUSIONS Benefit-risk balance should be an integral part of the risk management plan and as such considered before marketing authorisation. Modelling and simulation can be incorporated into MCDA to support the evidence synthesis as well evidence generation taking into account the underlying correlations between favourable and unfavourable effects. In addition, it represents a valuable tool for the optimization of protocol design in effectiveness trials.
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Affiliation(s)
- Francesco Bellanti
- Division of Pharmacology, Leiden Academic Centre for Drug Research, the Netherlands
| | - Rob C van Wijk
- Division of Pharmacology, Leiden Academic Centre for Drug Research, the Netherlands
| | - Meindert Danhof
- Division of Pharmacology, Leiden Academic Centre for Drug Research, the Netherlands
| | - Oscar Della Pasqua
- Division of Pharmacology, Leiden Academic Centre for Drug Research, the Netherlands.,Clinical Pharmacology & Therapeutics, University College London, London.,Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, Stockley Park, UK
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