Subcutaneous ocrelizumab in multiple sclerosis: Results of the Phase 1b OCARINA I study.

Objective: Subcutaneous ocrelizumab is being developed to provide treatment flexibility and additional choice to patients with multiple sclerosis. OCARINA I (NCT03972306) is an open-label, multicenter, Phase 1b, dose-finding study to investigate the pharmacokinetics, safety, tolerability, and immunogenicity of subcutaneous ocrelizumab and to select a dose for the Phase 3 OCARINA II study (NCT05232825).

Methods: Patients with relapsing or primary progressive multiple sclerosis (aged 18-65 years; Expanded Disability Status Scale score 0.

Online multiple hypothesis testing.

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all -values are available to be tested at a single time point.

Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals.

Background: There is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases.

Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing.

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals "to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines". The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research.

Response-adaptive randomization in clinical trials: from myths to practical considerations.

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates.

On the Biochemistry and Biophysics of Living Cell Formation.

A description is given of a sequence of events which would have led to the appearance of the organic compounds and living cells present on Earth, one of which is human cells. The evolutionary events involved are proposed as having taken place in phosphate ion-dominated aqueous pools formed in regions associated with volcanoes. The mechanism involved the unique molecular structure variations and chemical properties of polyphosphoric acid and compounds of this acid producing urea as the first organic compound formed on Earth and derivatives of urea giving rise to DNA and RNA.

Familywise error rate control for block response-adaptive randomization.

Response-adaptive randomization allows the probabilities of allocating patients to treatments in a clinical trial to change based on the previously observed response data, in order to achieve different experimental goals. One concern over the use of such designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. To address this, Robertson and Wason (Biometrics, 2019) proposed methodology that guarantees familywise error rate control for a large class of response-adaptive designs by re-weighting the usual -test statistic.

Point estimation for adaptive trial designs II: Practical considerations and guidance.

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice.

Online error rate control for platform trials.

Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time.

Point estimation for adaptive trial designs I: A methodological review.

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention.

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data.

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random.

The benefits of covariate adjustment for adaptive multi-arm designs.

Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the ability to select treatments, it is unclear how covariate adjustment affects various aspects of the study. Consider the design framework that relies on pre-specified treatment selection rule(s) and a combination test approach for hypothesis testing.

Conditional power and friends: The why and how of (un)planned, unblinded sample size recalculations in confirmatory trials.

Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Since the sample size is usually determined by an argument based on the power of the trial, an interim analysis raises the question of how the final sample size should be determined conditional on the accrued information. To this end, we first review and compare common approaches to estimating conditional power, which is often used in heuristic sample size recalculation rules.

A Review of Bayesian Perspectives on Sample Size Derivation for Confirmatory Trials.

Sample size derivation is a crucial element of planning any confirmatory trial. The required sample size is typically derived based on constraints on the maximal acceptable Type I error rate and minimal desired power. Power depends on the unknown true effect and tends to be calculated either for the smallest relevant effect or a likely point alternative.

Rapid and sustained B-cell depletion with subcutaneous ofatumumab in relapsing multiple sclerosis: APLIOS, a randomized phase-2 study.

Background: Ofatumumab, the first fully human anti-CD20 monoclonal antibody, is approved in several countries for relapsing multiple sclerosis (RMS).

Objective: To demonstrate the bioequivalence of ofatumumab administered by an autoinjector versus a pre-filled syringe (PFS) and to explore the effect of ofatumumab on B-cell depletion.

Methods: APLIOS (NCT03560739) is a 12-week, open-label, parallel-group, phase-2 study in patients with RMS receiving subcutaneous ofatumumab 20 mg every 4 weeks (q4w) (from Week 4, after initial doses on Days 1, 7, and 14).

The glenoid-intramedullary humeral angle: a measurement of compensatory scapular abduction in advanced rotator cuff arthropathy and its potential effects on implant choice.

Background: This study establishes measurements to evaluate pathologic compensation in rotator cuff tear arthropathy and resultant considerations for reverse shoulder arthroplasty.

Methods: Radiographs of patients with intact rotator cuffs were measured establishing interobserver and intraobserver reliability. Reverse shoulder arthroplasty cases performed by a single surgeon were then retrospectively reviewed.

A hypothesis of bone joint defects.

The work presented proposes origins of the extensive range of observations concerning changes in bone joints associated with rheumatoid arthritis, osteoarthritis and arthritis urica. These changes are shown to originate with alterations of cell reactions involving four basic cell biochemicals. The proposals allow explanations of, for example, the link between rheumatoid arthritis and anaemia.

Controlling type I error rates in multi-arm clinical trials: A case for the false discovery rate.

Multi-arm trials are an efficient way of simultaneously testing several experimental treatments against a shared control group. As well as reducing the sample size required compared to running each trial separately, they have important administrative and logistical advantages. There has been debate over whether multi-arm trials should correct for the fact that multiple null hypotheses are tested within the same experiment.

Effect of ocrelizumab on vaccine responses in patients with multiple sclerosis: The VELOCE study.

Objective: The phase IIIb A Study to Evaluate the Effects of Ocrelizumab on Immune Responses in Participants With Relapsing Forms of Multiple Sclerosis (VELOCE) study (NCT02545868) assessed responses to selected vaccines in ocrelizumab (OCR)-treated patients with relapsing multiple sclerosis.

Methods: Patients were randomized 2:1 into the OCR group (n = 68; OCR 600 mg) or control group (n = 34; interferon beta or no disease-modifying therapy). All received tetanus toxoid (TT)-containing vaccine, Pneumovax (23-valent pneumococcal polysaccharide vaccine [23-PPV]), and keyhole limpet hemocyanin (KLH).

Graphical approaches for the control of generalized error rates.

When simultaneously testing multiple hypotheses, the usual approach in the context of confirmatory clinical trials is to control the familywise error rate (FWER), which bounds the probability of making at least one false rejection. In many trial settings, these hypotheses will additionally have a hierarchical structure that reflects the relative importance and links between different clinical objectives. The graphical approach of Bretz et al (2009) is a flexible and easily communicable way of controlling the FWER while respecting complex trial objectives and multiple structured hypotheses.

Human body cell membranes and antigen control.

The variables involved in the initiation and operation of the human immune system are considered. It is shown that the number of variations associated with disease and other detrimental conditions exceeds the number of lymphocyte cells available for control of these conditions. It is proposed that the immune system functions by changes in the ionic strength of metabolic fluids which in turn control the formation and stability of cell membranes.