Optimal designs for phase II/III drug development programs including methods for discounting of phase II results

BMC Medical Research Methodology - 2020
Stella Erdmann1, Marietta Kirchner1, Heiko Götte2, Meinhard Kieser1
1Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, D-69120, Heidelberg, Germany
2Merck Healthcare KGaA, Frankfurter Str. 250, D-64293, Darmstadt, Germany

Tóm tắt

Abstract Background Go/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%. Methods We integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the “right amount of adjustment”, 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 influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies. Results In general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the “naïve” program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design. Conclusion Individual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).

Từ khóa


Tài liệu tham khảo

Erdmann, S. drugdevelopR: bias. https://web.imbi.uni-heidelberg.de/bias/. Accessed 02 Jul 2020.

DiMasi JA, Hansen RW, Grabowski HC, Lasagna L. Research and development costs for new drugs by therapeutic category. Pharmaco Economics. 1995;7:152–69.

DiMasi JA, Feldman L, Seckler A, Wilson A. Trends in risks associated with new drug development: success rates for investigational drugs. Clinical Pharmacology & Therapeutics. 2010;87:272–7.

De Martini D. Empowering phase II clinical trials to reduce phase III failures. Pharm Stat. 2020;19:178–86.

Antonijevic Z. Optimization of Pharmaceutical R&D Programs and portfolios: design and investment strategy. Heidelberg: Springer; 2015.

Hughes MD, Pocock SJ. Stopping rules and estimation problems in clinical trials. Stat Med. 1988;7:1231–42.

Fan X, DeMets DL, Lan KG. Conditional bias of point estimates following a group sequential test. J Biopharm Stat. 2004;14:505–30.

Zhang JJ, Blumenthal G, He K, Tang S, Cortazar P, Sridhara R. Overestimation of the effect size in group sequential trials. Clin Cancer Res. 2012;18:18,4872–6.

Ellenberg SS, DeMets DL, Fleming TR. Bias and trials stopped early for benefit. Jama. 2010;304:156–9.

Nardini C. Monitoring in clinical trials: benefit or bias? Theoretical Medicine and Bioethics. 2013;34:259–74.

US Food and Drug Administration. 22 case studies where phase 2 and phase 3 trials had divergent results. 2017. Available at http://go.nature.com/2mayug4. Accessed 02 Jul 2020.

Gan HK, You B, Pond GR, Chen EX. Assumptions of expected benefits in randomized phase III trials evaluating systemic treatments for cancer. J Natl Cancer Inst. 2012;104:590–8.

Arrowsmith J. Trial watch: phase II failures: 2008–2010. Nat Rev Drug Discov. 2011;10:328–9.

Chuang-Stein C, Kirby S. The shrinking or disappearing observed treatment effect. Pharm Stat. 2014;13:277–80.

Kirby S, Burke J, Chuang-Stein C, Sin C. Discounting phase 2 results when planning phase 3 clinical trials. Pharm Stat. 2012;11:373–85.

O'Hagan A, Stevens JW, Montmartin J. Bayesian cost-effectiveness analysis from clinical trial data. Stat Med. 2001;20:733–753.2005.

O'Hagan A, Stevens JW, Campbell MJ. Assurance in clinical trial design. Pharm Stat. 2005;4:187–201.

Spiegelhalter DJ, Freedman LS, Blackburn PR. Monitoring clinical trials: conditional or predictive power? Control Clin Trials. 1986;7:8–17.

Spiegelhalter DJ, Freedman LS. A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion. Statistics Med. 1986;5:1–13.

Chuang-Stein C. Sample size and the probability of a successful trial. Pharm Stat J Appl Stat Pharm Ind. 2006;5:305–9.

Chuang-Stein C, Yang R. A revisit of sample size decisions in confirmatory trials. Statistics in Biopharmaceutical Research. 2010;2:239–48.

Gasparini M, Di Scala L, Bretz F, Racine-Poon A. Some uses of predictive probability of success in clinical drug development. Epidemiology, biostatistics and. Public Health. 2013;10:1.

Saint-Hilary G, Barboux V, Pannaux M, Gasparini M, Robert V, Mastrantonio G. Predictive probability of success using surrogate endpoints. Stat Med. 2019;38:1753–74. https://doi.org/10.1002/sim.8060.

Wang SJ, Hung HM, O'Neill RT. Adapting the sample size planning of a phase III trial based on phase II data. Pharm Stat. 2006;5:85–97.

De Martini D. Adapting by calibration the sample size of a phase III trial on the basis of phase II data. Pharm Stat. 2011;10:89–95.

Götte H, Schüler A, Kirchner M, Kieser M. Sample size planning for phase II trials based on success probabilities for phase III. Pharm Stat. 2015;14:515–24.

Kirchner M, Kieser M, Götte H, Schüler A. Utility-based optimization of phase II/III programs. Stat Med. 2016;35:305–16.

Schoenfeld D. The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika. 1981;68:316–9.

IQWiG. Allgemeine Methoden. Version 5.0, 10.07.2016, Technical Report.

R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2019. Available at https://www.R-project.org/. Accessed 02 Jul 2020.

Steensma DP, Kantarjian HM. Impact of cancer research bureaucracy on innovation, costs, and patient care. J Clin Oncol. 2014;32:376–8.

Ding M, Rosner GL, Müller P. Bayesian optimal design for phase II screening trials. Biometrics. 2008;3:886–94.

Erdmann, S. drugdevelopR: prior. https://web.imbi.uni-heidelberg.de/prior/. Accessed 02 Jul 2020.

Dallow N, Best N, Montague TH. Better decision making in drug development through adoption of formal prior elicitation. Pharm Stat. 2018;17:301–16.

O'Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH, Jenkinson DJ, et al. Uncertain judgements: eliciting experts' probabilities. Chichester: Wiley; 2006.

Devilee JLA, Knol AB. Software to support expert elicitation: an exploratory study of existing software packages; 2011.

DiMasi JA, Grabowski HG, Vernon J. R&D costs and returns by therapeutic category. Drug Information J. 2004;38:211–23.

Adams CP, Brantner VV. Spending on new drug development. Health Econ. 2010;19:130–41.

Morgan S, Grootendorst P, Lexchin J, Cunningham C, Greyson D. The cost of drug development: a systematic review. Health Policy. 2011;100:4–17.

Preussler S, Kieser M, Kirchner M. Optimal sample size allocation and go/no-go decision rules for phase II/III programs where several phase III trials are performed. Biom J. 2019;61(2):357–78.

Preussler S, Kirchner M, Götte H, Kieser M. Optimal designs for multi-arm phase II/III drug development programs. Statistics in Biopharmaceutical Res. 2019. https://doi.org/10.1080/19466315.2019.1702092.

Patel NR, Ankolekar S. A Bayesian approach for incorporating economic factors in sample size design for clinical trials of individual drugs and portfolios of drugs. Stat Med. 2007;26:4976–88.

Götte H, Kirchner M, Sailer MO, Kieser M. Simulation-based adjustment after exploratory biomarker subgroup selection in phase II. Stat Med. 2017;36:2378–90.

Thông tin
Thông tin xuất bản

BMC Medical Research Methodology

Thông tin tác giả