scholarly journals Defining SOD1 ALS natural history to guide therapeutic clinical trial design

2016 ◽  
Vol 88 (2) ◽  
pp. 99-105 ◽  
Author(s):  
Taha Bali ◽  
Wade Self ◽  
Jingxia Liu ◽  
Teepu Siddique ◽  
Leo H Wang ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Natural History ◽  
Trial Design ◽  
Clinical Trial Design

scholarly journals Hierarchical Bayesian modelling of disease progression to inform clinical trial design in centronuclear myopathy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Eve Fouarge ◽  
◽  
Arnaud Monseur ◽  
Bruno Boulanger ◽  
Mélanie Annoussamy ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Natural History ◽  
Bayesian Statistics ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Threshold Probability ◽  
Congenital Diseases ◽  
Type 1 Error ◽  
Rate Of Response

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.

A prospective one-year natural history study of mucopolysaccharidosis types IIIA and IIIB: Implications for clinical trial design

2016 ◽  
Vol 119 (3) ◽  
pp. 239-248 ◽  
Author(s):  
K.V. Truxal ◽  
H. Fu ◽  
D.M. McCarty ◽  
K.A. McNally ◽  
K.L. Kunkler ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Natural History ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Natural History Study ◽  
One Year

Integrating radiomics into clinical trial design

2019 ◽  
Vol 63 (4) ◽  
Author(s):  
Jessica J. Waninger ◽  
Michael D. Green ◽  
Catherine Cheze Le Rest ◽  
Benjamin Rosen ◽  
Issam El Naqa
Keyword(s):  
Clinical Trial ◽  
Trial Design ◽  
Clinical Trial Design

scholarly journals X-change symposium: status and future of modern radiation oncology—from technology to biology

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Stefanie Corradini ◽  
Maximilian Niyazi ◽  
Dirk Verellen ◽  
Vincenzo Valentini ◽  
Seán Walsh ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Radiation Oncology ◽  
Trial Design ◽  
Rapid Development ◽  
Clinical Trial Design ◽  
Current Status ◽  
Treatment Modalities ◽  
Special Treatment ◽  
Image Guided Radiotherapy

AbstractFuture radiation oncology encompasses a broad spectrum of topics ranging from modern clinical trial design to treatment and imaging technology and biology. In more detail, the application of hybrid MRI devices in modern image-guided radiotherapy; the emerging field of radiomics; the role of molecular imaging using positron emission tomography and its integration into clinical routine; radiation biology with its future perspectives, the role of molecular signatures in prognostic modelling; as well as special treatment modalities such as brachytherapy or proton beam therapy are areas of rapid development. More clinically, radiation oncology will certainly find an important role in the management of oligometastasis. The treatment spectrum will also be widened by the rational integration of modern systemic targeted or immune therapies into multimodal treatment strategies. All these developments will require a concise rethinking of clinical trial design. This article reviews the current status and the potential developments in the field of radiation oncology as discussed by a panel of European and international experts sharing their vision during the “X-Change” symposium, held in July 2019 in Munich (Germany).

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