Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles.
Adaptive
Clinical trial
External control
Meta-analytic predictive approach
Platform
Prior distribution
SMART
Small sample
Journal
Orphanet journal of rare diseases
ISSN: 1750-1172
Titre abrégé: Orphanet J Rare Dis
Pays: England
ID NLM: 101266602
Informations de publication
Date de publication:
07 05 2022
07 05 2022
Historique:
received:
09
12
2021
accepted:
26
04
2022
entrez:
7
5
2022
pubmed:
8
5
2022
medline:
11
5
2022
Statut:
epublish
Résumé
Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes. Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications. The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.
Sections du résumé
BACKGROUND
Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.
MAIN TEXT
Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.
CONCLUSION
The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.
Identifiants
pubmed: 35526036
doi: 10.1186/s13023-022-02342-5
pii: 10.1186/s13023-022-02342-5
pmc: PMC9077995
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
186Informations de copyright
© 2022. The Author(s).
Références
Ther Innov Regul Sci. 2021 Sep;55(5):1019-1035
pubmed: 34014439
Int J Environ Res Public Health. 2021 Jan 24;18(3):
pubmed: 33498915
Alzheimers Dement. 2017 Jan;13(1):8-19
pubmed: 27583651
Orphanet J Rare Dis. 2008 May 02;3:11
pubmed: 18454853
Lancet Neurol. 2014 Jul;13(7):676-85
pubmed: 24873720
Stat Med. 2021 Jan 30;40(2):312-326
pubmed: 33111381
J Pharmacokinet Pharmacodyn. 2020 Feb;47(1):91-104
pubmed: 31960231
Brain. 2007 Jun;130(Pt 6):1552-65
pubmed: 17405767
Clin Pharmacol Ther. 2017 Dec;102(6):934-941
pubmed: 28795401
Orphanet J Rare Dis. 2014 Nov 26;9:170
pubmed: 25427578
J Chronic Dis. 1976 Mar;29(3):175-88
pubmed: 770493
Clin Epidemiol. 2020 May 08;12:457-467
pubmed: 32440224
Curr Clin Pharmacol. 2018;13(3):199-208
pubmed: 29866013
Stat Med. 2018 Sep 20;37(21):3047-3055
pubmed: 29761523
Orphanet J Rare Dis. 2021 Jan 6;16(1):3
pubmed: 33407688
Biometrics. 1989 Dec;45(4):1197-211
pubmed: 2611322
Eur J Epidemiol. 2016 Apr;31(4):337-50
pubmed: 27209009
JAMA. 2017 Oct 24;318(16):1605-1606
pubmed: 29067406
Mov Disord. 2014 Apr;29(4):470-8
pubmed: 24532007
J Biopharm Stat. 2020 Nov 1;30(6):1109-1120
pubmed: 32892710
Stat Med. 2021 Aug 30;40(19):4167-4184
pubmed: 33960507
Ther Innov Regul Sci. 2022 Jan 3;:
pubmed: 34978048
Comput Stat Data Anal. 2017 Sep;113:136-153
pubmed: 28630525
Clin Trials. 2005;2(4):295-300; discussion 301-4, 364-78
pubmed: 16281428
Clin Trials. 2018 Feb;15(1_suppl):27-32
pubmed: 29452522
Pharm Stat. 2018 Jul;17(4):301-316
pubmed: 29603614
Int J Appl Basic Med Res. 2012 Jan;2(1):3-6
pubmed: 23776799
Trials. 2020 Apr 28;21(1):362
pubmed: 32345372
Orphanet J Rare Dis. 2016 Oct 4;11(1):134
pubmed: 27716293
Stat Med. 2018 Nov 20;37(26):3723-3732
pubmed: 30010207
Br J Clin Pharmacol. 2011 Apr;71(4):488-96
pubmed: 21395641
N Engl J Med. 2017 Jul 6;377(1):62-70
pubmed: 28679092
Contemp Clin Trials. 2020 May;92:105989
pubmed: 32200006
Biometrics. 2012 Mar;68(1):203-11
pubmed: 21714780
Stat Med. 2015 Dec 10;34(28):3724-49
pubmed: 26346180
J Am Med Inform Assoc. 2000 May-Jun;7(3):254-66
pubmed: 10833162
Haemophilia. 2009 Jul;15(4):869-80
pubmed: 19473411
Stat Med. 2014 Oct 30;33(24):4186-201
pubmed: 24957522
Contemp Clin Trials. 2019 Nov;86:105852
pubmed: 31614215
Stat Med. 2012 Nov 10;31(25):2955-72
pubmed: 22711340
Lancet. 2008 Jun 14;371(9629):2051-5
pubmed: 18555919
Eur Urol Focus. 2019 Sep;5(5):706-709
pubmed: 31160252
Mov Disord. 2016 Oct;31(10):1574-1577
pubmed: 27324431
JAMA Oncol. 2018 Mar 01;4(3):384-388
pubmed: 29188284
Am Soc Clin Oncol Educ Book. 2015;:e141-7
pubmed: 25993165
Best Pract Res Clin Rheumatol. 2014 Apr;28(2):247-62
pubmed: 24974061
Expert Opin Drug Discov. 2021 Jul;16(7):727-735
pubmed: 33653187
Blood Adv. 2020 Nov 10;4(21):5433-5441
pubmed: 33156923
Stat Med. 2021 Feb 20;40(4):963-977
pubmed: 33216360
Eur J Hum Genet. 2020 Feb;28(2):165-173
pubmed: 31527858
Clin Trials. 2009 Jun;6(3):203-4
pubmed: 19528129
Biometrics. 2011 Sep;67(3):1047-56
pubmed: 21361892
Mov Disord Clin Pract. 2015 May 22;2(2):127-134
pubmed: 30363842
Contemp Clin Trials. 2016 Jan;46:48-51
pubmed: 26586608
Orphanet J Rare Dis. 2018 Nov 6;13(1):195
pubmed: 30400970
Gut. 2012 Dec;61(12):1693-700
pubmed: 22595313
Clin Trials. 2021 Dec;18(6):706-710
pubmed: 34657476
BMJ. 2014 Nov 24;349:g6802
pubmed: 25422272
Lancet Neurol. 2021 Mar;20(3):182-192
pubmed: 33609476
Stat Med. 2000 Dec 30;19(24):3359-76
pubmed: 11122501
Clin Trials. 2010 Feb;7(1):5-18
pubmed: 20156954
BMJ. 1995 Dec 16;311(7020):1621-5
pubmed: 8555809
Nat Rev Drug Discov. 2020 Feb;19(2):77-78
pubmed: 32020066
Biometrics. 2014 Dec;70(4):1023-32
pubmed: 25355546
JAMA. 2015 Apr 28;313(16):1619-20
pubmed: 25799162
Genet Med. 2021 Nov;23(11):2194-2201
pubmed: 34183788
Stat Med. 2019 Apr 15;38(8):1459-1474
pubmed: 30511500
J Clin Epidemiol. 2022 Apr;144:93-101
pubmed: 34910979
Nat Rev Drug Discov. 2006 Jan;5(1):27-36
pubmed: 16485344
Nat Med. 2021 Jul;27(7):1187-1196
pubmed: 34155411
Clin Pharmacol Ther. 2021 Jun;109(6):1489-1498
pubmed: 32748400
Neurotherapeutics. 2012 Jan;9(1):176-84
pubmed: 22139591
Clin Cancer Res. 2020 May 15;26(10):2290-2296
pubmed: 31969335
Stat Med. 2005 May 30;24(10):1455-81
pubmed: 15586395
Orphanet J Rare Dis. 2020 Mar 12;15(1):69
pubmed: 32164754
Pharm Stat. 2018 Nov;17(6):797-810
pubmed: 30221446