Approaches to sample size calculation for clinical trials in rare diseases

Miller, Frank; Zohar, Sarah; Stallard, Nigel; Madan, Jason; Posch, Martin; Hee, Siew Wan; Pearce, Michael; Vågerö, Mårten; Day, Simon

doi:10.1002/pst.1848

Cited by 20 publications

(12 citation statements)

References 34 publications

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“…On the other side, the maximum sample size was 126 in those three studies, while the parameters were several genotypes, drug concentration, a series of serum index, and so one. A preliminary step before planning a clinical trial is the sample size calculation because the accuracy of consequence closely depends on the sufficient sample size ( Miller et al, 2018 ). An important and often difficult step in sample size determination is to specify the required parameters for these methods ( D’Arrigo et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Factors Influencing the Steady-State Plasma Concentration of Imatinib Mesylate in Patients With Gastrointestinal Stromal Tumors and Chronic Myeloid Leukemia

et al. 2020

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Imatinib mesylate (IM) is the standard treatment for advanced, metastatic gastrointestinal stromal tumors (GISTs) and chronic myeloid leukemia (CML) with a fixed daily standard dosage via the oral route. Interindividual and intraindividual variability in plasma concentrations have been closely linked to the efficacy of IM therapy. Therefore, this review identifies and describes the key factors influencing the plasma concentration of IM in patients with GISTs and CML. We used the following keywords to search the PubMed, EMBASE, Ovid, Wangfang, and CNKI databases to identify published reports: IM, plasma concentration, GISTs, CML, drug combination/interaction, pathology, and genotype/genetic polymorphism, either alone or in combination. This literature review revealed that only 10 countries have reported the mean concentrations of IM in GISTs or CML patients and the clinical outcomes in different ethnic groups and populations. There were totally 24 different gene polymorphisms, which were examined for any potential influence on the steady-state plasma concentration of IM. As a result, some genotype locus made discrepant conclusion. Herein, the more sample capacity, multicenter, long-term study was worthy to carry out. Eleven reports were enumerated on clinical drug interactions with IM, while there is not sufficient information on the pharmacokinetic parameters altered by drug combinations with IM that could help in investigating the actual drug interactions. The drug interaction with IM should be paid more attention in the future research.

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Section: Resultsmentioning

confidence: 99%

Factors Influencing the Steady-State Plasma Concentration of Imatinib Mesylate in Patients With Gastrointestinal Stromal Tumors and Chronic Myeloid Leukemia

et al. 2020

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“…Animal models are important tools to use for evaluation of rare, fatal disorders such as NPC1, because the small size of the affected patient population is often insufficient to power conventional clinical trials (Miller et al, 2018). NPC1 also presents the additional complexities of extensive genetic and clinical heterogeneity, including phenotypic differences that are present even among patients with the same NPC1 mutation (Vanier, 2010;Vanier et al, 1991Vanier et al, , 1988Walterfang et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Genetic background modifies phenotypic severity and longevity in a mouse model of Niemann-Pick disease type C1

Rodriguez-Gil

Watkins‐Chow

Baxter

et al. 2020

Disease Models &Amp; Mechanisms

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Niemann-Pick disease type C1 (NPC1) is a rare, fatal neurodegenerative disorder characterized by lysosomal accumulation of unesterified cholesterol and glycosphingolipids. These subcellular pathologies lead to phenotypes of hepatosplenomegaly, neurological degeneration and premature death. NPC1 is extremely heterogeneous in the timing of clinical presentation and is associated with a wide spectrum of causative NPC1 mutations. To study the genetic architecture of NPC1, we have generated a new NPC1 mouse model, Npc1 em1Pav. Npc1 em1Pav/em1Pav mutants showed notably reduced NPC1 protein compared to controls and displayed the pathological and biochemical hallmarks of NPC1. Interestingly, Npc1 em1Pav/em1Pav mutants on a C57BL/6J genetic background showed more severe visceral pathology and a significantly shorter lifespan compared to Npc1 em1Pav/em1Pav mutants on a BALB/cJ background, suggesting that strain-specific modifiers contribute to disease severity and survival. QTL analysis for lifespan of 202 backcross N2 mutants on a mixed C57BL/6J and BALB/cJ background detected significant linkage to markers on chromosomes 1 and 7. The discovery of these modifier regions demonstrates that mouse models are powerful tools for analyzing the genetics underlying rare human diseases, which can be used to improve understanding of the variability in NPC1 phenotypes and advance options for patient diagnosis and therapy. This article has an associated First Person interview with the first author of the paper.

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“…However, as recalled in the introduction, this practice is not generally supported by regulatory agencies. As an alternative, some authors have proposed the use of a Bayesian decision-theoretic framework [19]. A Bayesian decision-theoretic approach can be applied when we would like a treatment recommendation not based on type I error but on maximizing an expected gain for the total population.…”

Section: Sample Size Determination/re-estimationmentioning

confidence: 99%

Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges

Ursino

Stallard

2021

IJERPH

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The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed.

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Approaches to sample size calculation for clinical trials in rare diseases

Cited by 20 publications

References 34 publications

Factors Influencing the Steady-State Plasma Concentration of Imatinib Mesylate in Patients With Gastrointestinal Stromal Tumors and Chronic Myeloid Leukemia

Factors Influencing the Steady-State Plasma Concentration of Imatinib Mesylate in Patients With Gastrointestinal Stromal Tumors and Chronic Myeloid Leukemia

Genetic background modifies phenotypic severity and longevity in a mouse model of Niemann-Pick disease type C1

Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges

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