An old friend who has overstayed their welcome : the ALSFRS-R total score as primary endpoint for ALS clinical trials

Objective: The ALSFRS-R is limited by multidimensionality, which originates from the summation of various subscales. This prevents a direct comparison between patients with identical total scores. We aim to evaluate how multidimensionality affects the performance of the ALSFRS-R in clinical trials. Methods: We simulated clinical trial data with different treatment effects for the ALSFRS-R total score and its subscales (i.e. bulbar, fine motor, gross motor and respiratory). We considered scenarios where treatment reduced the rate of ALSFRS-R subscale decline either uniformly (i.e. all subscales respond identically to treatment) or non-uniformly (i.e. subscales respond differently to treatment). Two main analytical strategies were compared: (1) analyzing only the total score or (2) utilizing a subscale-based test (i.e. alternative strategy). For each analytical strategy, we calculated the empirical power and required sample size. Results: Both strategies are valid when there is no treatment benefit and provide adequate control of type 1 error. If all subscales respond identically to treatment, using the total score is the most powerful approach. As the differences in treatment responses between subscales increase, the more the total score becomes affected. For example, to detect a 40% reduction in the bulbar rate of decline with 80% power, the total score requires 1380 patients, whereas this is 336 when using the alternative strategy. Conclusions: Ignoring the multidimensional structure of the ALSFRS-R total score could have negative consequences for ALS clinical trials. We propose determining treatment benefit on a subscale level, prior to stating whether a treatment is generally effective

TRICALS: creating a highway toward a cure

A change in our current approach toward drug development is required to improve the likelihood of finding effective treatment for patients with amyotrophic lateral sclerosis (ALS). The aim of the Treatment Research Initiative to Cure ALS (TRICALS) is to extend the collective effort with industry and consolidate drug development paths. TRICALS has begun a series of meetings on how to best move the field forward collaboratively, thereby addressing five major topics in ALS clinical trials: (1) preclinical research, (2) biomarker development, (3) eligibility criteria, (4) efficacy endpoints and (5) innovative trial design. There is an appetite for ongoing discussions of these major topics in clinical trials between representatives from academia, patient advocacy groups, industry partners and funding bodies. Industry is open to fundamentally change drug development for ALS and shorten the time to effective therapy for patients by implementing promising innovations in biomarker development, trial design, and patient selection. There is however, a pressing need from all stakeholders for regulatory discussions and amendments of current guidelines to successfully adopt innovation in future clinical development lines

Innovating clinical trials for amyotrophic lateral sclerosis : challenging the established order

Development of effective treatments for amyotrophic lateral sclerosis (ALS) has been hampered by disease heterogeneity, a limited understanding of underlying pathophysiology, and methodologic design challenges. We have evaluated 2 major themes in the design of pivotal, phase 3 clinical trials for ALS—(1) patient selection and (2) analytical strategy—and discussed potential solutions with the European Medicines Agency. Several design considerations were assessed using data from 5 placebo-controlled clinical trials (n = 988), 4 population-based cohorts (n = 5,100), and 2,436 placebo-allocated patients from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. The validity of each proposed design modification was confirmed by means of simulation and illustrated for a hypothetical setting. Compared to classical trial design, the proposed design modifications reduce the sample size by 30.5% and placebo exposure time by 35.4%. By making use of prognostic survival models, one creates a potential to include a larger proportion of the population and maximize generalizability. We propose a flexible design framework that naturally adapts the trial duration when inaccurate assumptions are made at the design stage, such as enrollment or survival rate. In case of futility, the follow-up time is shortened and patient exposure to ineffective treatments or placebo is minimized. For diseases such as ALS, optimizing the use of resources, widening eligibility criteria, and minimizing exposure to futile treatments and placebo is critical to the development of effective treatments. Our proposed design modifications could circumvent important pitfalls and may serve as a blueprint for future clinical trials in this population

A road map for remote digital health technology for motor neuron disease

Despite recent and potent technological advances, the real-world implementation of remote digital health technology in the care and monitoring of patients with motor neuron disease has not yet been realized. Digital health technology may increase the accessibility to and personalization of care, whereas remote biosensors could optimize the collection of vital clinical parameters, irrespective of patients’ ability to visit the clinic. To facilitate the wide-scale adoption of digital health care technology and to align current initiatives, we outline a road map that will identify clinically relevant digital parameters; mediate the development of benefit-to-burden criteria for innovative technology; and direct the validation, harmonization, and adoption of digital health care technology in real-world settings. We define two key end products of the road map: (1) a set of reliable digital parameters to capture data collected under free-living conditions that reflect patient-centric measures and facilitate clinical decision making and (2) an integrated, open-source system that provides personalized feedback to patients, health care providers, clinical researchers, and caregivers and is linked to a flexible and adaptable platform that integrates patient data in real time. Given the ever-changing care needs of patients and the relentless progression rate of motor neuron disease, the adoption of digital health care technology will significantly benefit the delivery of care and accelerate the development of effective treatments

Statistical process control models in agro-chains

Consumer expectations on quality of food products are very high and tend to increase as well as diversify to include flexible and fast delivery, health, safety and minimal environmental impact. Assurance to conform or even exceed these expectations can no longer come from mass inspection, but has to be founded on intelligent process control, process and product design and continuous improvement. Statistical Process Control (SPC) provides the sound basis for this. Although SPC used to be strongly associated with the statistical tools applied, it is now regarded in general as an indispensable approach to managing processes (Deming (1986), Snee (1990), Joiner (1994), Hare et al. (1995), Hoerl (1995), Does et al. (1997), Roes and Dorr (1997) and Roes (1997)). In the broader context this approach is based upon the principles that: all work is a series of interconnected processes; all processes vary; sources of variation can roughly be distinguished as arising from common causes (inherent to the process as designed) and special causes; understanding the origin of each of these sources of variation is the key to reduction of variation; reduction of variation is the key to quality improvement, productivity and profitability. Statistical methods such as control charts, experimental design, data analysis are applied to uncover causes of variation and thus control and improve the processes. SPC can be implemented on the shop floor by cross-disciplinary teams, called Process Action Teams (PATs). In production processes such teams consist of operators, foremen, process-engineers, maintenance-engineers and other technical personnel involved with the process, and a statistician. A PAT implements SPC for a specific process following a stepwise approach, based on the Plan-Do-Check-Act cycle. This forms a close link between statistical thinking and the scientific method. The main steps are: Definition of the process to be dealt with Diagnosis of the process Actions and measurements Design of feedback control loop Implementation and further improvement (back to I) The result of the phases I through V is usually twofold. The main purpose is to install a control loop with control charts and accompanying out of control action plan (Figure 1). In this control loop, deviations from the normal performance of the process are detected by means of control charts. Subsequently, the shop floor operators follow the out of control action plan to identify and remove the cause as quickly as possible. Concurrently with establishing this control loop, opportunities for improvement arise during process diagnosis and appropriate action is taken or is planned to be taken once control is established. The process diagnosis is a crucial step and includes describing processes using flow-charts and performing a risk analysis based on the Failure Mode and Effect Analysis (FMEA) technique (see Stamatis, 1995). Possible causes and effects critical quality characteristic

Prior distributions for variance parameters in a sparse-event meta-analysis of a few small trials

Contains fulltext : 232638.pdf (Publisher’s version ) (Open Access)In rare diseases, typically only a small number of patients are available for a randomized clinical trial. Nevertheless, it is not uncommon that more than one study is performed to evaluate a (new) treatment. Scarcity of available evidence makes it particularly valuable to pool the data in a meta-analysis. When the primary outcome is binary, the small sample sizes increase the chance of observing zero events. The frequentist random-effects model is known to induce bias and to result in improper interval estimation of the overall treatment effect in a meta-analysis with zero events. Bayesian hierarchical modeling could be a promising alternative. Bayesian models are known for being sensitive to the choice of prior distributions for between-study variance (heterogeneity) in sparse settings. In a rare disease setting, only limited data will be available to base the prior on, therefore, robustness of estimation is desirable. We performed an extensive and diverse simulation study, aiming to provide practitioners with advice on the choice of a sufficiently robust prior distribution shape for the heterogeneity parameter. Our results show that priors that place some concentrated mass on small τ values but do not restrict the density for example, the Uniform(-10, 10) heterogeneity prior on the log(τ(2) ) scale, show robust 95% coverage combined with less overestimation of the overall treatment effect, across varying degrees of heterogeneity. We illustrate the results with meta-analyzes of a few small trials

Combined assessment of early and late-phase outcomes in orphan drug development

In drug development programs, proof-of-concept Phase II clinical trials typically have a biomarker as a primary outcome, or an outcome that can be observed with relatively short follow-up. Subsequently, the Phase III clinical trials aim to demonstrate the treatment effect based on a clinical outcome that often needs a longer follow-up to be assessed. Early-phase outcomes or biomarkers are typically associated with late-phase outcomes and they are often included in Phase III trials. The decision to proceed to Phase III development is based on analysis of the early-Phase II outcome data. In rare diseases, it is likely that only one Phase II trial and one Phase III trial are available. In such cases and before drug marketing authorization requests, positive results of the early-phase outcome of Phase II trials are then likely seen as supporting (or even replicating) positive Phase III results on the late-phase outcome, without a formal retrospective combined assessment and without accounting for between-study differences. We used double-regression modeling applied to the Phase II and Phase III results to numerically mimic this informal retrospective assessment. We provide an analytical solution for the bias and mean square error of the overall effect that leads to a corrected double-regression. We further propose a flexible Bayesian double-regression approach that minimizes the bias by accounting for between-study differences via discounting the Phase II early-phase outcome when they are not in line with the Phase III biomarker outcome results. We illustrate all methods with an orphan drug example for Fabry disease

Bayesian sample size re-estimation using power priors

Contains fulltext : 215389.pdf (publisher's version ) (Open Access)The sample size of a randomized controlled trial is typically chosen in order for frequentist operational characteristics to be retained. For normally distributed outcomes, an assumption for the variance needs to be made which is usually based on limited prior information. Especially in the case of small populations, the prior information might consist of only one small pilot study. A Bayesian approach formalizes the aggregation of prior information on the variance with newly collected data. The uncertainty surrounding prior estimates can be appropriately modelled by means of prior distributions. Furthermore, within the Bayesian paradigm, quantities such as the probability of a conclusive trial are directly calculated. However, if the postulated prior is not in accordance with the true variance, such calculations are not trustworthy. In this work we adapt previously suggested methodology to facilitate sample size re-estimation. In addition, we suggest the employment of power priors in order for operational characteristics to be controlled