Approaches to sample size calculation for clinical trials in rare diseases

Sample size determination for time-to-event endpoints in randomized selection trials with generalized exponential distribution

Randomized selection trials are frequently used to compare experimental treatments that have the potential to be beneficial, but they often do not include a control group. While time-to-event endpoints are commonly applied in clinical investigations, methodologies for determining the required sample size for such endpoints, except exponential distribution, are lacking. In recent times, there has been a shift in clinical trials, with a growing emphasis on progression-free survival as a primary endpoint. However, the utilization of this measure has typically been restricted to specific time points for both sample size determination and analysis. This alteration in approach could wield a substantial influence on the clinical trial process, potentially diminishing the capacity to discern variances between treatment groups. In the calculation of sample sizes for randomized trials, this investigation operates under the assumption that the time-to-event endpoint conforms to either an exponential, Weibull, or generalized exponential distribution.

Clinical Trials for Special Populations: Children, Older Adults, and Rare Diseases

Research cannot maximize population health unless it improves health for all members of the public, including special populations such as children, older adults, and people living with rare diseases. Each of these categories require special considerations when planning and performing clinical trials, and common threads of ethical conduct of research in vulnerable populations appear throughout. In this review, definitions of each of the three categories of special population (children, older adults, and rare diseases) are discussed in terms of US research regulations, the unique challenges to conducting clinical trials for these special populations, critical ethical issues, and opportunities for innovative ways to design and operationalize clinical trials in special populations. Additional critical attention is focused on factors that influence the generalizability of study results to reduce health disparities, as well as the importance of community engagement and advocacy groups that can help to educate potential trial participants of the benefits of clinical trial participation.

Innovations in Clinical Development in Rare Diseases of Children and Adults: Small Populations and/or Small Patients

Many of the afflictions of children are rare diseases. This creates numerous drug development challenges related to small populations, including limited information about the disease state, enrollment challenges, and diminished incentives for pediatric development of novel therapies by pharmaceutical and biotechnology sponsors. We review selected innovations in clinical development that may partially mitigate some of these difficulties, starting with the concept of development efficiency for individual clinical trials, clinical programs (involving multiple trials for a single drug), and clinical portfolios of multiple drugs, and decision analysis as a tool to optimize efficiency. Development efficiency is defined as the ability to reach equally rigorous or more rigorous conclusions in less time, with fewer trial participants, or with fewer resources. We go on to discuss efficient methods for matching targeted therapies to biomarker-defined subgroups, methods for eliminating or reducing the need for natural history data to guide rare disease development, the use of basket trials to enhance efficiency by grouping multiple similar disease applications in a single clinical trial, and the use of alternative data sources including historical controls to augment or replace concurrent controls in clinical studies. Greater understanding and broader application of these methods could lead to improved therapies and/or more widespread and rapid access to novel therapies for rare diseases in both children and adults.

The Value of the Information That Can Be Generated: Optimizing Study Design to Enable the Study of Treatments Addressing an Unmet Need for Rare Pathogens

In traditional phase 3 trials confirming safety and efficacy of new treatments relative to a comparator, a one-sided type I error rate of 2.5% is traditionally used, and typically leads to minimum sizes of 300-600 subjects per study. However, for rare pathogens, it may be necessary to work with data from as few as 50–100 subjects. For areas with a high unmet need, there is a balance between traditional type I error and power and enabling feasible studies. In such cases, an alternative one-sided alpha level of 5% or 10% should be considered and we review herein the implications of such approaches. Resolving this question requires engagement of patients, the medical community, regulatory agencies, and trial sponsors.

A roadmap to using randomization in clinical trials

BACKGROUND

Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.

METHODS

We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.

RESULTS

Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.

CONCLUSIONS

The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

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

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.

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

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.

Serum Vitamin D Levels in Children with Immune Thrombocytopenia

OBJECTIVE

Immune thrombocytopenia (ITP) is an acquired immuno-mediated disorder characterized by thrombocytopenia with an increased risk of bleeding. In recent years 1,25[OH]2D3 has been rediscovered as an immune modulator. We decided to evaluate serum Vitamin D levels in a cohort of children with immune thrombocytopenia in order to discover if Vitamin D concentrations may predict ITP duration.

METHODS

Thirty children were enrolled in this study (sixteen with chronic ITP and fourteen with newly diagnosed ITP) to assess serum Vitamin D levels.

RESULTS

The results showed that 80% of the enrolled children presented a D hypovitaminosis status. Children with newly diagnosis ITP showed no statistically significantly higher median values of Vitamin D compared to chronic ITP.

CONCLUSION

This study may suggest that Vitamin D deficiency does not represent a chronicity factor for ITP. However, further studies are needed to understand the role of Vitamin D in ITP pathogenesis.

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

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, Npc1em1PavNpc1em1Pav/em1Pav mutants showed notably reduced NPC1 protein compared to controls and displayed the pathological and biochemical hallmarks of NPC1. Interestingly, Npc1em1Pav/em1Pav mutants on a C57BL/6J genetic background showed more severe visceral pathology and a significantly shorter lifespan compared to Npc1em1Pav/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.

NPC1 Deficiency in Mice is Associated with Fetal Growth Restriction, Neonatal Lethality and Abnormal Lung Pathology

The rare lysosomal storage disorder Niemann-Pick disease type C1 (NPC1) arises from mutation of NPC1, which encodes a lysosomal transmembrane protein essential for normal transport and trafficking of cholesterol and sphingolipids. NPC1 is highly heterogeneous in both clinical phenotypes and age of onset. Previous studies have reported sub-Mendelian survival rates for mice homozygous for various Npc1 mutant alleles but have not studied the potential mechanisms underlying this phenotype. We performed the first developmental analysis of a Npc1 mouse model, Npc1^em1Pav, and discovered significant fetal growth restriction in homozygous mutants beginning at E16.5. Npc1^{em1Pav/em1Pav} mice also exhibited cyanosis, increased respiratory effort, and over 50% lethality at birth. Analysis of neonatal lung tissues revealed lipid accumulation, notable abnormalities in surfactant, and enlarged alveolar macrophages, suggesting that lung abnormalities may be associated with neonatal lethality in Npc1^{em1Pav/em1Pav} mice. The phenotypic severity of the Npc1^em1Pav model facilitated this first analysis of perinatal lethality and lung pathology in an NPC1 model organism, and this model may serve as a useful resource for developing treatments for respiratory complications seen in NPC1 patients.

A pragmatic, adaptive clinical trial design for a rare disease: The FOcal Cerebral Arteriopathy Steroid (FOCAS) trial

BACKGROUND

Pediatric stroke investigators identified as their top research priority a clinical trial of corticosteroids for focal cerebral arteriopathy (FCA). However, FCA is both rare and an acute condition making it infeasible to enroll the large sample sizes needed for standard, confirmatory clinical trials. We present a pragmatic approach to clinical trial design that may inform the approach to other rare disorders.

METHODS

We surveyed pediatric stroke experts to determine the level of evidence that would impact their clinical management of FCA. Incorporating survey results, a randomized, group sequential Bayesian adaptive design was proposed based on a quantitative radiologic outcome measure (change from baseline in change in the FCA Severity Score). Using accumulating information, the design determines whether intervention is better than control with high probability.

RESULTS

Among 21 (100%) respondents, the probability of corticosteroid efficacy that would lead the experts to treat was 30% (median). The probability of efficacy that would make them unwilling to randomize (because they would feel all children should receive corticosteroids) was 70%. Simulation studies with the proposed design showed that a total of 42 subjects controls the type I error rate at the desired level 0.20 and yields a smaller average sample size and trial duration compared to a conventional design.

CONCLUSIONS

Designs in rare diseases require special considerations; this is especially true for this childhood disease, which is both uncommon and acute. This design has incorporated expert consensus to establish the criteria for success, formal monitoring rules for safety, and early stopping rules.

Demonstrating effectiveness or demonstrating not ineffectiveness - A potential solution for rare disease drug product development?

For review and approval of new drug products, substantial evidence regarding safety and effectiveness of the drug products under investigation are necessarily provided. A traditional approach is to test a null hypothesis of ineffectiveness against an alternative hypothesis of effectiveness at the 5% level of significance. The rejection of the null hypothesis of ineffectiveness is in favor of the alternative hypothesis of effectiveness. This approach, however, is somewhat misleading because the rejection of the null hypothesis of ineffectiveness leads to the conclusion of not ineffectiveness, which consists of the proportion of inconclusiveness and the proportion of effectiveness. In this article, we explore the potential use of the concept of demonstrating not ineffectiveness and then effectiveness for regulatory approval of new drug products, especially for rare disease drug products. For rare disease drug product development, one of the major obstacles and challenges is how to use small patient population available for achieving the same standards for regulatory approval. To address this problem, a two-stage approach by first demonstrating not ineffectiveness and then ruling out (or controlling) the probability of inconclusiveness for demonstrating effectiveness is proposed. The proposed two-stage approach is useful with small patient population available for achieving the same standards for regulatory approval of rare disease drug products.

Recent advances in methodology for clinical trials in small populations: the InSPiRe project

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.