Eve Fouarge1, Arnaud Monseur2, Bruno Boulanger2, Mélanie Annoussamy3,4, Andreea M Seferian3, Silvana De Lucia3, Charlotte Lilien3,5, Leen Thielemans6,7, Khazal Paradis8, Belinda S Cowling6, Chris Freitag6, Bradley P Carlin9, Laurent Servais10,11,12,13. 1. Division of Child Neurology, Centre de Référence Des Maladies Neuromusculaires, Department of Paediatrics, University Hospital of Liège and University of Liège, Liège, Belgium. 2. Pharmalex Belgium, Mont-Saint-Guibert, Belgium. 3. Institute I-Motion, Hôpital Armand Trousseau, Paris, France. 4. Sysnav, Vernon, France. 5. MDUK Oxford Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, UK. 6. Dynacure, 67400, Illkirch, France. 7. 2 Bridge, Rodendijk 60/X, 2980, Zoersel, Belgium. 8. Paradis Consultancy SAS, 06570, Saint-Paul-de-Vence, France. 9. PharmaLex US, Burlington, MA, USA. 10. Division of Child Neurology, Centre de Référence Des Maladies Neuromusculaires, Department of Paediatrics, University Hospital of Liège and University of Liège, Liège, Belgium. Laurent.servais@paediatrics.ox.ac.uk. 11. Institute I-Motion, Hôpital Armand Trousseau, Paris, France. Laurent.servais@paediatrics.ox.ac.uk. 12. MDUK Oxford Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, UK. Laurent.servais@paediatrics.ox.ac.uk. 13. Department of Paediatrics, Level 2, John Radcliffe Hospital, Headley Way, Headington, OX3 9DU, Oxford, UK. Laurent.servais@paediatrics.ox.ac.uk.
Abstract
BACKGROUND: Centronuclear myopathies are severe rare congenital diseases. The clinical variability and genetic heterogeneity of these myopathies result in major challenges in clinical trial design. Alternative strategies to large placebo-controlled trials that have been used in other rare diseases (e.g., the use of surrogate markers or of historical controls) have limitations that Bayesian statistics may address. Here we present a Bayesian model that uses each patient's own natural history study data to predict progression in the absence of treatment. This prospective multicentre natural history evaluated 4-year follow-up data from 59 patients carrying mutations in the MTM1 or DNM2 genes. METHODS: Our approach focused on evaluation of forced expiratory volume in 1 s (FEV1) in 6- to 18-year-old children. A patient was defined as a responder if an improvement was observed after treatment and the predictive probability of such improvement in absence of intervention was less than 0.01. An FEV1 response was considered clinically relevant if it corresponded to an increase of more than 8%. RESULTS: The key endpoint of a clinical trial using this model is the rate of response. The power of the study is based on the posterior probability that the rate of response observed is greater than the rate of response that would be observed in the absence of treatment predicted based on the individual patient's previous natural history. In order to appropriately control for Type 1 error, the threshold probability by which the difference in response rates exceeds zero was adapted to 91%, ensuring a 5% overall Type 1 error rate for the trial. CONCLUSIONS: Bayesian statistical analysis of natural history data allowed us to reliably simulate the evolution of symptoms for individual patients over time and to probabilistically compare these simulated trajectories to actual observed post-treatment outcomes. The proposed model adequately predicted the natural evolution of patients over the duration of the study and will facilitate a sufficiently powerful trial design that can cope with the disease's rarity. Further research and ongoing dialog with regulatory authorities are needed to allow for more applications of Bayesian statistics in orphan disease research.
BACKGROUND:Centronuclear myopathies are severe rare congenital diseases. The clinical variability and genetic heterogeneity of these myopathies result in major challenges in clinical trial design. Alternative strategies to large placebo-controlled trials that have been used in other rare diseases (e.g., the use of surrogate markers or of historical controls) have limitations that Bayesian statistics may address. Here we present a Bayesian model that uses each patient's own natural history study data to predict progression in the absence of treatment. This prospective multicentre natural history evaluated 4-year follow-up data from 59 patients carrying mutations in the MTM1 or DNM2 genes. METHODS: Our approach focused on evaluation of forced expiratory volume in 1 s (FEV1) in 6- to 18-year-old children. A patient was defined as a responder if an improvement was observed after treatment and the predictive probability of such improvement in absence of intervention was less than 0.01. An FEV1 response was considered clinically relevant if it corresponded to an increase of more than 8%. RESULTS: The key endpoint of a clinical trial using this model is the rate of response. The power of the study is based on the posterior probability that the rate of response observed is greater than the rate of response that would be observed in the absence of treatment predicted based on the individual patient's previous natural history. In order to appropriately control for Type 1 error, the threshold probability by which the difference in response rates exceeds zero was adapted to 91%, ensuring a 5% overall Type 1 error rate for the trial. CONCLUSIONS: Bayesian statistical analysis of natural history data allowed us to reliably simulate the evolution of symptoms for individual patients over time and to probabilistically compare these simulated trajectories to actual observed post-treatment outcomes. The proposed model adequately predicted the natural evolution of patients over the duration of the study and will facilitate a sufficiently powerful trial design that can cope with the disease's rarity. Further research and ongoing dialog with regulatory authorities are needed to allow for more applications of Bayesian statistics in orphan disease research.
Entities:
Keywords:
Bayesian analysis; Centronuclear myopathy; Complex innovative clinical trial design; Disease progression model; Natural history data
Authors: Jerry R Mendell; Louise R Rodino-Klapac; Zarife Sahenk; Kandice Roush; Loren Bird; Linda P Lowes; Lindsay Alfano; Ann Maria Gomez; Sarah Lewis; Janaiah Kota; Vinod Malik; Kim Shontz; Christopher M Walker; Kevin M Flanigan; Marco Corridore; John R Kean; Hugh D Allen; Chris Shilling; Kathleen R Melia; Peter Sazani; Jay B Saoud; Edward M Kaye Journal: Ann Neurol Date: 2013-09-10 Impact factor: 10.422
Authors: Jerry R Mendell; Samiah Al-Zaidy; Richard Shell; W Dave Arnold; Louise R Rodino-Klapac; Thomas W Prior; Linda Lowes; Lindsay Alfano; Katherine Berry; Kathleen Church; John T Kissel; Sukumar Nagendran; James L'Italien; Douglas M Sproule; Courtney Wells; Jessica A Cardenas; Marjet D Heitzer; Allan Kaspar; Sarah Corcoran; Lyndsey Braun; Shibi Likhite; Carlos Miranda; Kathrin Meyer; K D Foust; Arthur H M Burghes; Brian K Kaspar Journal: N Engl J Med Date: 2017-11-02 Impact factor: 91.245
Authors: J Laporte; L J Hu; C Kretz; J L Mandel; P Kioschis; J F Coy; S M Klauck; A Poustka; N Dahl Journal: Nat Genet Date: 1996-06 Impact factor: 38.330
Authors: Alan H Beggs; Barry J Byrne; Sabine De Chastonay; Tmirah Haselkorn; Imelda Hughes; Emma S James; Nancy L Kuntz; Jennifer Simon; Lindsay C Swanson; Michele L Yang; Zi-Fan Yu; Sabrina W Yum; Suyash Prasad Journal: Muscle Nerve Date: 2017-12-22 Impact factor: 3.217
Authors: A V Ramanan; L V Hampson; H Lythgoe; A P Jones; B Hardwick; H Hind; B Jacobs; D Vasileiou; I Wadsworth; N Ambrose; J Davidson; P J Ferguson; T Herlin; A Kavirayani; O G Killeen; S Compeyrot-Lacassagne; R M Laxer; M Roderick; J F Swart; C M Hedrich; M W Beresford Journal: PLoS One Date: 2019-06-05 Impact factor: 3.240
Authors: Darryl C De Vivo; Enrico Bertini; Kathryn J Swoboda; Wuh-Liang Hwu; Thomas O Crawford; Richard S Finkel; Janbernd Kirschner; Nancy L Kuntz; Julie A Parsons; Monique M Ryan; Russell J Butterfield; Haluk Topaloglu; Tawfeg Ben-Omran; Valeria A Sansone; Yuh-Jyh Jong; Francy Shu; John F Staropoli; Douglas Kerr; Alfred W Sandrock; Christopher Stebbins; Marco Petrillo; Gabriel Braley; Kristina Johnson; Richard Foster; Sarah Gheuens; Ishir Bhan; Sandra P Reyna; Stephanie Fradette; Wildon Farwell Journal: Neuromuscul Disord Date: 2019-09-12 Impact factor: 4.296
Authors: Robert J Graham; Francesco Muntoni; Imelda Hughes; Sabrina W Yum; Nancy L Kuntz; Michele L Yang; Barry J Byrne; Suyash Prasad; Rachel Alvarez; Casie A Genetti; Tmirah Haselkorn; Emma S James; Laurie B LaRusso; Mojtaba Noursalehi; Salvador Rico; Alan H Beggs Journal: Arch Dis Child Date: 2019-09-04 Impact factor: 3.791
Authors: Kelley M Kidwell; Satrajit Roychoudhury; Barbara Wendelberger; John Scott; Tara Moroz; Shaoming Yin; Madhurima Majumder; John Zhong; Raymond A Huml; Veronica Miller Journal: Orphanet J Rare Dis Date: 2022-05-07 Impact factor: 4.303