Statistics and Probability; Biochemistry, Genetics and Molecular Biology (miscellaneous)
Abstract :
[en] The small sample sizes inherent in rare and pediatric disease settings offer significant challenges for clinical trial design. In such settings, Bayesian adaptive trial methods can often pay dividends, allowing the sensible incorporation of auxiliary data and other relevant information to bolster that collected by the trial itself. Previous work has also included the use of one-arm trials augmented by the participants’ own natural history data, from which the future course of the disease in the absence of intervention can be predicted. Patient response can then be defined by the degree to which post-intervention observations are inconsistent with the predicted “natural” trajectory. While such trials offer obvious advantages in efficiency and ethical hazard (since they expose no new patients to a placebo, anathema to patients or their parents and caregivers), they can offer no protection against bias arising from the presence of any “placebo effect,” the tendency of patients to improve merely by being in the trial. In this paper, we investigate the impact of both static and transient placebo effects on one-arm responder studies of this type, as well as two-arm versions that incorporate a small concurrent placebo group but still borrow strength from the natural history data. We also propose more traditional Bayesian changepoint models that specify a parametric functional form for the patient’s post-intervention trajectory, which in turn allow quantification of the treatment benefit in terms of the model parameters, rather than semi-parametrically in terms of a response relative to some “null” model. We compare the operating characteristics of our designs in the context of an ongoing investigation of centronuclear myopathies (CNMs), a group of congenital neuromuscular diseases whose most common and severe form is X-linked, affecting approximately 1 in 50,000 newborn boys. Our results indicate our two-arm responder and changepoint methods can offer protection against placebo effects, improving power while protecting the trial’s Type I error rate. However, further research into innovative trial designs as well as ongoing dialog with regulatory authorities remain critically important in rare disease research.
Seferian, Andreea; Institute I-Motion, Hôpital Armand Trousseau, Paris, France
Servais, Laurent ; Centre Hospitalier Universitaire de Liège - CHU > > Service de pédiatrie ; Institute I-Motion, Hôpital Armand Trousseau, Paris, France ; MDUK Oxford Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, United Kingdom ; Department of Paediatrics, Level 2, John Radcliffe Hospital, Oxford, United Kingdom
Freitag, Chris; Dynacure, Illkirch, France
Thielemans, Leen; Dynacure, Illkirch, France ; 2 Bridge, Zoersel, Belgium
Gidaro, Teresa
Gargaun, Elena
Chê, Virginie
Schara, Ulrike
Gangfuß, Andrea
D’Amico, Adele
Dowling, James J.
Darras, Basil T.
Daron, Aurore ; Centre Hospitalier Universitaire de Liège - CHU > > Service de pédiatrie
Hernandez, Arturo
de Lattre, Capucine
Arnal, Jean-Michel
Mayer, Michèle
Cuisset, Jean-Marie
Vuillerot, Carole
Fontaine, Stéphanie
Bellance, Rémi
Biancalana, Valérie
Buj-Bello, Ana
Hogrel, Jean-Yves
Landy, Hal
Amburgey, Kimberly
Andres, Barbara
Bertini, Enrico
Cardas, Ruxandra
DENIS, Séverine ; Centre Hospitalier Universitaire de Liège - CHU
This work was supported by a grant from Dynacure. The authors gratefully acknowledge the contributions of the NatHis-MTM study group for curating and allowing us access to their motivating dataset on CNM. the NatHis-MTM Study Group: Teresa Gidaro, Elena Gargaun, Virginie Chê, Ulrike Schara, Andrea Gangfuß, Adele D'Amico, James J. Dowling, Basil T. Darras, Aurore Daron, Arturo Hernandez, Capucine de Lattre, Jean-Michel Arnal, Michèle Mayer, Jean-Marie Cuisset, Carole Vuillerot, Stéphanie Fontaine, Rémi Bellance, Valérie Biancalana, Ana Buj-Bello, Jean-Yves Hogrel, Hal Landy, Kimberly Amburgey, Barbara Andres, Enrico Bertini, Ruxandra Cardas, Séverine Denis, Dominique Duchêne, Virginie Latournerie, Nacera Reguiba, Etsuko Tsuchiya, and Carina Wallgren-Pettersson.
Annoussamy M, Seferian AM, Daron A et al (2021) (2021) Natural history of Type 2 and 3 spinal muscular atrophy: 2-year NatHis-SMA study. Ann Clin Transl Neurol 8(2):359–373. 10.1002/acn3.51281 DOI: 10.1002/acn3.51281
Lilien C, Gasnier E, Gidaro T et al (2019) Home-based monitor for gait and activity analysis. J Vis Exp. 10.3791/59668 DOI: 10.3791/59668
Frank DE, Schnell FJ, Akana C et al (2020) Increased dystrophin production with golodirsen in patients with Duchenne muscular dystrophy. Neurology 94(21):e2270–e2282. 10.1212/WNL.0000000000009233 DOI: 10.1212/WNL.0000000000009233
Dangouloff T, Servais L (2019) Clinical evidence supporting early treatment of patients with spinal muscular atrophy: current perspectives. Ther Clin Risk Manag 15:1153–1161. 10.2147/TCRM.S172291 DOI: 10.2147/TCRM.S172291
Berry SM, Carlin BP, Lee JJ, Muller P (2011) Bayesian adaptive methods for clinical trials. Chapman and Hall/CRC Press, Boca Raton, FL
Carlin BP, Louis TA (2009) Bayesian methods for data analysis, 3rd edn. Chapman and Hall/CRC Press, Boca Raton, FL
Ibrahim JG, Chen M-H (2000) Power prior distributions for regression models. Stat Sci 15(1):46–60
Hobbs BP, Carlin BP, Mandrekar S, Sargent DJ (2011) Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials. Biometrics 67:1047–1056 DOI: 10.1111/j.1541-0420.2011.01564.x
Schmidli H, Gsteiger S, Roychoudhury S, O’Hagan A, Spiegelhalter D, Neuenschwander B (2014) Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics 70(4):1023–1032 DOI: 10.1111/biom.12242
Neuenschwander B, Schmidli H (2020) Use of historical data. In: Lesaffre E, Baio G, Boulanger B (eds) Bayesian methods in pharmaceutical research. Taylor and Francis/CRC Press, Boca Raton, FL, pp 111–137 DOI: 10.1201/9781315180212-6
Cooner F, Williamson F, Carlin BP (2020) Bayesian frameworks for rare disease clinical development programs. In: Lesaffre E, Baio G, Boulanger B (eds) Bayesian methods in pharmaceutical research. Taylor and Francis/CRC Press, Boca Raton, FL, pp 243–257 DOI: 10.1201/9781315180212-12
Basu C, Carlin BP (2020) Bayesian hierarchical models for data extrapolation and analysis in pediatric disease clinical trials. In: Lesaffre E, Baio G, Boulanger B (eds) Bayesian methods in pharmaceutical research. Taylor and Francis/CRC Press, Boca Raton, FL, pp 259–270
Gamalo-Siebers M, Savic J, Basu C, Zhao X, Islas CD, Gopalakrishnan M, Guo A, Song G, Baygani S, Thompson L, Xia HA, Price KL, Tiwari RC, Carlin BP, for the DIA Bayesian Statistics Working Group (2017) Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation. Pharm Stat 16:232–249 DOI: 10.1002/pst.1807
Zhao H, Hobbs BP, Ma H, Jiang Q, Carlin BP (2016) Combining non-randomized and randomized data in clinical trials using commensurate priors. Health Serv Outcomes Res Method 16:154–171 DOI: 10.1007/s10742-016-0155-7
Wang C, Lu N, Chen WC, Li H, Tiwari R, Xu Y, Yue LQ (2020) Propensity score-integrated composite likelihood approach for incorporating real-world evidence in single-arm clinical studies. J Biopharm Stat 30(3):495–507 DOI: 10.1080/10543406.2019.1684309
Li H, Chen W-C, Lu N, Song C, Wang C, Tiwari R, Xu Y, Yue LQ (2021) Mitigating study power loss caused by clinical Trial Disruptions Due to the COVID-19 pandemic: leveraging external data via propensity score-integrated approaches. Statistics in Biopharmaceutical Research. 10.1080/19466315.2020.1860813 DOI: 10.1080/19466315.2020.1860813
Dunne J, Rodriguez W, Murphy M, Beasley B, Burckhart G, Filie J, Lewis J, Sachs H, Sheridan P, Starke P (2011) Extrapolation of adult data and other data in pediatric drug-development programs. Pediatrics 128(5):e1242-1249 DOI: 10.1542/peds.2010-3487
Fouarge E, Monseur A, Boulanger B, Annoussamy M, Seferian AM, De Lucia S, Lilien C, Thielemans L, Paradis K, Cowling BS, Freitag C, Carlin BP, Servais L, the NatHis-MTM Study Group (2021) Hierarchical Bayesian modelling of disease progression to inform clinical trial design in centronuclear myopathy. Orphanet J Rare Dis 16(1):1–11 DOI: 10.1186/s13023-020-01663-7
Carlin BP, Gelfand AE, Smith AFM (1992) Hierarchical Bayesian analysis of changepoint problems. Appl Stat 41:389–405 DOI: 10.2307/2347570
Goemans N, Signorovitch J, Sajeev G, Yao Z, Gordish-Dressman H, McDonald CM et al (2020) Suitability of external controls for drug evaluation in Duchenne muscular dystrophy. Neurology 95(10):1381–1391 DOI: 10.1212/WNL.0000000000010170
Gelman A, Meng X-L, Stern HS (1996) Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Stat Sin 6:733–807
Annoussamy M, Lilien C, Gidaro T, Gargaun E, Chê V, Schara U et al (2019) X-linked myotubular myopathy: a prospective international natural history study. Neurology 92(16):e1852–e1867. 10.1212/WNL.0000000000007319 DOI: 10.1212/WNL.0000000000007319
Mercuri E, Darras BT, Chiriboga CA et al (2018) Nusinersen versus sham control in later-onset spinal muscular atrophy. N Engl J Med 378(7):625–635 DOI: 10.1056/NEJMoa1710504
O’Hagan A, Stevens JW, Campbell MJ (2005) Assurance in clinical trial design. Pharm Stat J Appl Stat Pharm Ind 4(3):187–201
Kruschke J (2014) Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan, 2nd edn. Academic Press, New York