Optimal unplanned design modification in adaptive two-stage trials.
adaptive design
clinical trial
conditional error principle
optimal design
sample size calculation
Journal
Pharmaceutical statistics
ISSN: 1539-1612
Titre abrégé: Pharm Stat
Pays: England
ID NLM: 101201192
Informations de publication
Date de publication:
11 2022
11 2022
Historique:
revised:
01
02
2022
received:
18
06
2021
accepted:
24
04
2022
pubmed:
24
5
2022
medline:
18
11
2022
entrez:
23
5
2022
Statut:
ppublish
Résumé
Adaptive planning of clinical trials allows modifying the entire trial design at any time point mid-course. In this paper, we consider the case when a trial-external update of the planning assumptions during the ongoing trial makes an unforeseen design adaptation necessary. We take up the idea to construct adaptive designs with defined features by solving an optimization problem and apply it to the situation of unplanned design reassessment. By using the conditional error principle, we present an approach on how to optimally modify the trial design at an unplanned interim analysis while at the same time strictly protecting the type I error rate. This linking of optimal design planning and the conditional error principle allows sound reactions to unforeseen events that make a design reassessment necessary.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1121-1137Informations de copyright
© 2022 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.
Références
Bauer P. Multistage testing with adaptive designs. Biometrie Und Informatik in Medizin Und Biologie. 1989;20(4):130-148.
Bauer P, Köhne K. Evaluation of experiments with adaptive interim analyses. Biometrics. 1994;50(4):1029-1041. doi:10.2307/2533441
Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential trials. Biometrics. 1999;55(4):1286-1290. doi:10.1111/j.0006-341X.1999.01286.x
Cui L, Hung HMJ, Wang SJ. Modification of sample size in group sequential clinical trials. Biometrics. 1999;55(3):853-857. doi:10.1111/j.0006-341X.1999.00853.x
Proschan MA, Hunsberger SA. Designed extension of studies based on conditional power. Biometrics. 1995;51(4):1315-1324. doi:10.2307/2533262
Vandemeulebroecke M. An investigation of two-stage tests. Statistica Sinica. 2006;16(3):933-951.
Wittes J, Brittain F. The role of internal pilot studies in increasing the efficiency of clinical trials. Stat Med. 1990;9(1-2):65-72. doi:10.1002/sim.4780090113
Kieser M, Friede T. Re-calculating the sample size in internal pilot study designs with control of the type I error rate. Stat Med. 2000;19(7):901-911. doi:10.1002/(SICI)1097-0258(20000415)19:7<901::AID-SIM405>3.0.CO;2-L
Lang T, Auterith A, Bauer P. Trendtests with adaptive scoring. Biom J. 2000;42:1007-1020. doi:10.1002/1521-4036(200012)42:8<1007::AID-BIMJ1007>3.0.CO;2-J
Hommel G. Adaptive modifications of hypotheses after an interim analysis. Biom J. 2001;43:581-589. doi:10.1002/1521-4036(200109)43:5<581::AID-BIMJ581>3.0.CO;2-J
Müller HH, Schäfer H. Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and of classical group sequential approaches. Biometrics. 2001;57(3):886-891. doi:10.1111/j.0006-341X.2001.00886.x
Müller HH, Schäfer H. A general statistical principle for changing a design any time during the course of a trial. Stat Med. 2004;23(16):2497-2508. doi:10.1002/sim.1852
Fisher LD. Self-designing clinical trials. Stat Med. 1998;17(14):1551-1562. doi:10.1002/(SICI)1097-0258(19980730)17:14<1551::AID-SIM868>3.0.CO;2-E
Shen Y, Fisher L. Statistical inference for self-designing clinical trials with a one-sided hypothesis. Biometrics. 1999;55(1):190-197. doi:10.1111/j.0006-341X.1999.00190.x
Jennison C, Turnbull BW. Mid-course sample size modification in clinical trials based on the observed treatment effect. Stat Med. 2003;22(6):971-993. doi:10.1002/sim.1457
Pritchett YL, Menon S, Marchenko O, et al. Sample size re-estimation designs in confirmatory clinical trials-current state, statistical considerations, and practical guidance. Stat Biopharm Res. 2015;7(4):309-321. doi:10.1080/19466315.2015.1098564
Kieser M. Methods and Applications of Sample Size Calculation and Recalculation in Clinical Trials. Springer International Publishing; 2020. doi:10.1007/978-3-030-49528-2
European Medicines Agency, Reflection Paper on Methodological Issues in Confirmatory Clinical Trials Planned with an Adaptive Design. https://www.ema.europa.eu/en/methodological-issues-confirmatory-clinical-trials-planned-adaptive-design; 2007.
Bauer P. Adaptive designs: looking for a needle in the haystack-a new challenge in medical research. Stat Med. 2008;27(10):1565-1580. doi:10.1002/sim.3090
Dimairo M, Pallmann P, Wason J, et al. The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. Trials. 2020;21:528. doi:10.1186/s13063-020-04334-x
Bauer P, Bretz F, Dragalin V, König F, Wassmer G. Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls. Stat Med. 2016;35(3):325-347. doi:10.1002/sim.6472
Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. Optimal planning of adaptive two-stage designs. Stat Med. 2021;40(13):3196-3213. doi:10.1002/sim.8953
Herrmann C, Pilz M, Kieser M, Rauch G. A new conditional performance score for the evaluation of adaptive group sequential designs with sample size recalculation. Stat Med. 2020;39(15):2067-2100. doi:10.1002/sim.8534
Proschan MA. Discussion of the paper: Burman CF, Sonesson C. are flexible designs sound? Biometrics. 2006;62(3):674-676.
Bauer P, König F. The reassessment of trial perspectives from interim data-a critical view. Stat Med. 2006;25(1):23-36. doi:10.1002/sim.2180
Englert S, Kieser M. Methods for proper handling of overrunning and underrunning in phase II designs for oncology trials. Stat Med. 2015;34(13):2128-2137. doi:10.1002/sim.6479
Jennison C, Turnbull BW. Adaptive sample size modification in clinical trials: start small then ask for more? Stat Med. 2015;34(29):3793-3810. doi:10.1002/sim.6575
Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. A variational approach to optimal two-stage designs. Stat Med. 2019;38(21):4159-4171. doi:10.1002/sim.8291
Simon R. Optimal two-stage designs for phase II clinical trials. Control Clin Trials. 1989;10(1):1-10. doi:10.1016/0197-2456(89)90015-9
Barber S, Jennison C. Optimal asymmetric one-sided group sequential tests. Biometrika. 2002;89:49-60. doi:10.1093/biomet/89.1.49
Kunzmann K, Pilz M, Herrmann C, Rauch G, Kieser M. The adoptr package: adaptive optimal designs for clinical trials in R. J Stat Softw. 2021;98(9):1-21. doi:10.18637/jss.v098.i09
ICH, Statistical Principles for Clinical Trials - E9. https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-9-statistical-principles-clinical-trials-step-5_en.pdf; 1998.
Spiegelhalter D, Freedman L, Blackburn P. Monitoring clinical trials: conditional or predictive power? Control Clin Trials. 1986;7(1):8-17. doi:10.1016/0197-2456(86)90003-6
Dmitrienko A, Wang MD. Bayesian predictive approach to interim monitoring in clinical trials. Stat Med. 2006;25(13):2178-2195. doi:10.1002/sim.2204
Rufibach K, Burger HU, Abt M. Bayesian predictive power: choice of prior and some recommendations for its use as probability of success in drug development. Pharm Stat. 2016;15(5):438-446. doi:10.1002/pst.1764
Dallow N, Fina P. The perils with the misuse of predictive power. Pharm Stat. 2011;10(4):311-317. doi:10.1002/pst.467
O'Hagan A, Stevens JW, Campbell MJ. Assurance in clinical trial design. Pharm Stat. 2005;4(3):187-201. doi:10.1002/pst.175
Kunzmann K, Kieser M. Optimal adaptive single-arm phase II trials under quantified uncertainty. J Biopharm Stat. 2020;30(1):89-103. doi:10.1080/10543406.2019.1609016
Wassmer G, Brannath W. Group sequential and confirmatory adaptive designs in clinical trials. Springer Series in Pharmaceutical Statistics. Springer International Publishing; 2016. doi:10.1007/978-3-319-32562-0
Posch M, Proschan MA. Unplanned adaptations before breaking the blind. Stat Med. 2012;31(30):4146-4153. doi:10.1002/sim.5361
Fedgchin M, Trivedi M, Daly E, et al. Efficacy and safety of fixed-dose esketamine nasal spray combined with a new oral antidepressant in treatment-resistant depression: results of a randomized, double-blind, active-controlled study (TRANSFORM-1). Int J Neuropsychopharmacol. 2019;22:616-630. doi:10.1093/ijnp/pyz039
Williams JB, Kobak KA. Development and reliability of a structured interview guide for the Montgomery Asberg depression rating scale (SIGMA). Br J Psychiatry. 2008;192:52-58. doi:10.1192/bjp.bp.106.032532
R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/; 2019.
Montgomery SA. Clinically relevant effect sizes in depression. Eur Neuropsychopharmacol. 1994;4:283-284. doi:10.1016/0924-977X(94)90093-0
Dallow N, Best N, Montague TH. Better decision making in drug development through adoption of formal prior elicitation. Pharm Stat. 2018;17(4):301-316. doi:10.1002/pst.1854
Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and Health-Care Evaluation; Wiley & Sons Ltd. 2004. according to https://www.wiley-vch.de/en/areas-interest/mathematics-statistics/bayesian-approaches-to-clinical-trials-and-health-care-evaluation-978-0-471-49975-6.