Dataset Information

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

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.

SUBMITTER: Fouarge E

PROVIDER: S-EPMC7789189 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Fouarge Eve E Monseur Arnaud A Boulanger Bruno B Annoussamy Mélanie M Seferian Andreea M AM De Lucia Silvana S Lilien Charlotte C Thielemans Leen L Paradis Khazal K Cowling Belinda S BS Freitag Chris C Carlin Bradley P BP Servais Laurent L

Orphanet journal of rare diseases 20210106 1

<h4>Background</h4>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 histor ...[more]

PMID: 33407688

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Project description:Centronuclear myopathy (CNM) is an inherited neuromuscular disorder characterised by clinical features of a congenital myopathy and centrally placed nuclei on muscle biopsy.The incidence of X-linked myotubular myopathy is estimated at 2/100000 male births but epidemiological data for other forms are not currently available.The clinical picture is highly variable. The X-linked form usually gives rise to a severe phenotype in males presenting at birth with marked weakness and hypotonia, external ophthalmoplegia and respiratory failure. Signs of antenatal onset comprise reduced foetal movements, polyhydramnios and thinning of the ribs on chest radiographs; birth asphyxia may be the present. Affected infants are often macrosomic, with length above the 90th centile and large head circumference. Testes are frequently undescended. Both autosomal-recessive (AR) and autosomal-dominant (AD) forms differ from the X-linked form regarding age at onset, severity, clinical characteristics and prognosis. In general, AD forms have a later onset and milder course than the X-linked form, and the AR form is intermediate in both respects.Mutations in the myotubularin (MTM1) gene on chromosome Xq28 have been identified in the majority of patients with the X-linked recessive form, whilst AD and AR forms have been associated with mutations in the dynamin 2 (DNM2) gene on chromosome 19p13.2 and the amphiphysin 2 (BIN1) gene on chromosome 2q14, respectively. Single cases with features of CNM have been associated with mutations in the skeletal muscle ryanodine receptor (RYR1) and the hJUMPY (MTMR14) genes.Diagnosis is based on typical histopathological findings on muscle biopsy in combination with suggestive clinical features; muscle magnetic resonance imaging may complement clinical assessment and inform genetic testing in cases with equivocal features. Genetic counselling should be offered to all patients and families in whom a diagnosis of CNM has been made.The main differential diagnoses include congenital myotonic dystrophy and other conditions with severe neonatal hypotonia.Management of CNM is mainly supportive, based on a multidisciplinary approach. Whereas the X-linked form due to MTM1 mutations is often fatal in infancy, dominant forms due to DNM2 mutations and some cases of the recessive BIN1-related form appear to be associated with an overall more favourable prognosis.

Project description:BACKGROUND:The basket trial evaluates the treatment effect of a targeted therapy in patients with the same genetic or molecular aberration, regardless of their cancer types. Bayesian hierarchical modeling has been proposed to adaptively borrow information across cancer types to improve the statistical power of basket trials. Although conceptually attractive, research has shown that Bayesian hierarchical models cannot appropriately determine the degree of information borrowing and may lead to substantially inflated type I error rates. METHODS:We propose a novel calibrated Bayesian hierarchical model approach to evaluate the treatment effect in basket trials. In our approach, the shrinkage parameter that controls information borrowing is not regarded as an unknown parameter. Instead, it is defined as a function of a similarity measure of the treatment effect across tumor subgroups. The key is that the function is calibrated using simulation such that information is strongly borrowed across subgroups if their treatment effects are similar and barely borrowed if the treatment effects are heterogeneous. RESULTS:The simulation study shows that our method has substantially better controlled type I error rates than the Bayesian hierarchical model. In some scenarios, for example, when the true response rate is between the null and alternative, the type I error rate of the proposed method can be inflated from 10% up to 20%, but is still better than that of the Bayesian hierarchical model. LIMITATION:The proposed design assumes a binary endpoint. Extension of the proposed design to ordinal and time-to-event endpoints is worthy of further investigation. CONCLUSION:The calibrated Bayesian hierarchical model provides a practical approach to design basket trials with more flexibility and better controlled type I error rates than the Bayesian hierarchical model. The software for implementing the proposed design is available at http://odin.mdacc.tmc.edu/~yyuan/index_code.html.

Project description:BACKGROUND:In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. METHODS:A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. RESULTS:From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero (?0.5) , this can lead to unacceptably high false-positive rates (?40%) in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. CONCLUSION:Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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