Do You Want to Stay Single? Considerations on Single-Arm Trials in Drug Development and the Postregulatory Space.

Single-arm trials (SATs), while not preferred, remain in use throughout the drug development cycle. They may be accepted by regulators in particular contexts (e.g., in oncology or rare diseases) when the potential effects of new treatments are very large and placebo treatment is unethical. However, in the postregulatory space, SATs are common, and perhaps even more poorly suited to address the questions of interest. In this manuscript, we review regulatory and HTA positions on SATs; challenges posed by SATs to address research questions beyond regulators, evolving statistical methods to provide context for SATs, case studies where SATs could and could not address questions of interest, and communication strategies to influence decision making and optimize study design to address evidence needs.

SAkuraBONSAI: Protocol design of a novel, prospective study to explore clinical, imaging, and biomarker outcomes in patients with AQP4-IgG-seropositive neuromyelitis optica spectrum disorder receiving open-label satralizumab.

Background: Neuromyelitis optica spectrum disorder (NMOSD) is a rare, autoimmune disease of the central nervous system that produces acute, unpredictable relapses causing cumulative neurological disability. Satralizumab, a humanized, monoclonal recycling antibody that targets the interleukin-6 receptor, reduced NMOSD relapse risk vs. placebo in two Phase 3 trials: SAkuraSky (satralizumab ± immunosuppressive therapy; NCT02028884) and SAkuraStar (satralizumab monotherapy; NCT02073279). Satralizumab is approved to treat aquaporin-4 IgG-seropositive (AQP4-IgG+) NMOSD. SAkuraBONSAI (NCT05269667) will explore fluid and imaging biomarkers to better understand the mechanism of action of satralizumab and the neuronal and immunological changes following treatment in AQP4-IgG+ NMOSD. Objectives: SAkuraBONSAI will evaluate clinical disease activity measures, patient-reported outcomes (PROs), pharmacokinetics, and safety of satralizumab in AQP4-IgG+ NMOSD. Correlations between imaging markers (magnetic resonance imaging [MRI] and optical coherence tomography [OCT]) and blood and cerebrospinal fluid (CSF) biomarkers will be investigated. Study design: SAkuraBONSAI is a prospective, open-label, multicenter, international, Phase 4 study that will enroll approximately 100 adults (18-74 years) with AQP4-IgG+ NMOSD. This study includes two patient cohorts: newly diagnosed, treatment-naïve patients (Cohort 1; n = 60); and inadequate responders to recent (<6 months) rituximab infusion (Cohort 2; n = 40). Satralizumab monotherapy (120 mg) will be administered subcutaneously at Weeks 0, 2, 4, and Q4W thereafter for a total of 92 weeks. Endpoints: Disease activity related to relapses (proportion relapse-free, annualized relapse rate, time to relapse, and relapse severity), disability progression (Expanded Disability Status Scale), cognition (Symbol Digit Modalities Test), and ophthalmological changes (visual acuity; National Eye Institute Visual Function Questionnaire-25) will all be assessed. Peri-papillary retinal nerve fiber layer and ganglion cell complex thickness will be monitored using advanced OCT (retinal nerve fiber layer and ganglion cell plus inner plexiform layer thickness). Lesion activity and atrophy will be monitored by MRI. Pharmacokinetics, PROs, and blood and CSF mechanistic biomarkers will be assessed regularly. Safety outcomes include the incidence and severity of adverse events. Conclusions: SAkuraBONSAI will incorporate comprehensive imaging, fluid biomarker, and clinical assessments in patients with AQP4-IgG+ NMOSD. SAkuraBONSAI will provide new insights into the mechanism of action of satralizumab in NMOSD, while offering the opportunity to identify clinically relevant neurological, immunological, and imaging markers.

Survival analysis for AdVerse events with VarYing follow-up times (SAVVY)-estimation of adverse event risks.

BACKGROUND: The SAVVY project aims to improve the analyses of adverse events (AEs), whether prespecified or emerging, in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). Although statistical methodologies have advanced, in AE analyses, often the incidence proportion, the incidence density, or a non-parametric Kaplan-Meier estimator are used, which ignore either censoring or CEs. In an empirical study including randomized clinical trials from several sponsor organizations, these potential sources of bias are investigated. The main purpose is to compare the estimators that are typically used to quantify AE risk within trial arms to the non-parametric Aalen-Johansen estimator as the gold-standard for estimating cumulative AE probabilities. A follow-up paper will consider consequences when comparing safety between treatment groups. METHODS: Estimators are compared with descriptive statistics, graphical displays, and a more formal assessment using a random effects meta-analysis. The influence of different factors on the size of deviations from the gold-standard is investigated in a meta-regression. Comparisons are conducted at the maximum follow-up time and at earlier evaluation times. CEs definition does not only include death before AE but also end of follow-up for AEs due to events related to the disease course or safety of the treatment. RESULTS: Ten sponsor organizations provided 17 clinical trials including 186 types of investigated AEs. The one minus Kaplan-Meier estimator was on average about 1.2-fold larger than the Aalen-Johansen estimator and the probability transform of the incidence density ignoring CEs was even 2-fold larger. The average bias using the incidence proportion was less than 5%. Assuming constant hazards using incidence densities was hardly an issue provided that CEs were accounted for. The meta-regression showed that the bias depended mainly on the amount of censoring and on the amount of CEs. CONCLUSIONS: The choice of the estimator of the cumulative AE probability and the definition of CEs are crucial. We recommend using the Aalen-Johansen estimator with an appropriate definition of CEs whenever the risk for AEs is to be quantified and to change the guidelines accordingly.

Parameter estimation and quantitative parametric linkage analysis with GENEHUNTER-QMOD.

OBJECTIVE: We present a parametric method for linkage analysis of quantitative phenotypes. The method provides a test for linkage as well as an estimate of different phenotype parameters. We have implemented our new method in the program GENEHUNTER-QMOD and evaluated its properties by performing simulations. METHODS: The phenotype is modeled as a normally distributed variable, with a separate distribution for each genotype. Parameter estimates are obtained by maximizing the LOD score over the normal distribution parameters with a gradient-based optimization called PGRAD method. RESULTS: The PGRAD method has lower power to detect linkage than the variance components analysis (VCA) in case of a normal distribution and small pedigrees. However, it outperforms the VCA and Haseman-Elston regression for extended pedigrees, nonrandomly ascertained data and non-normally distributed phenotypes. Here, the higher power even goes along with conservativeness, while the VCA has an inflated type I error. Parameter estimation tends to underestimate residual variances but performs better for expectation values of the phenotype distributions. CONCLUSION: With GENEHUNTER-QMOD, a powerful new tool is provided to explicitly model quantitative phenotypes in the context of linkage analysis. It is freely available at http://www.helmholtz-muenchen.de/genepi/downloads.