Decentralised Clinical Trials in Multiple Sclerosis Research

Randomised controlled trials (RCTs) play an important role in multiple sclerosis (MS) research, ensuring that new interventions are safe and efficacious before their introduction into clinical practice. Trials have been evolving to improve the robustness of their designs and the efficiency of their conduct. Advances in digital and mobile technologies in recent years have facilitated this process and the first RCTs with decentralised elements became possible. Decentralised clinical trials (DCTs) are conducted remotely, enabling participation of a more heterogeneous population who can participate in research activities from different locations and at their convenience. DCTs also rely on digital and mobile technologies which allows for more flexible and frequent assessments. While hospitals quickly adapted to e-health and telehealth assessments during the COVID-19 pandemic, the conduct of conventional RCTs was profoundly disrupted. In this paper, we review the existing evidence and gaps in knowledge in the design and conduct of DCTs in MS

Determining the effectiveness of early intensive versus escalation approaches for the treatment of relapsing-remitting multiple sclerosis: The DELIVER-MS study protocol

Multiple Sclerosis (MS) is a common cause of neurological disability among young adults and has a high economic burden. Currently there are 18 disease modifying agents for relapsing MS, which were tested in clinical trials versus placebo or an active comparator in a pairwise manner. However, there is currently no consensus on the fundamental principles of treatment approach and initial therapy selection. These factors result in variable use of disease modifying therapies. Here we describe the study protocol for Determining the Effectiveness of earLy Intensive Versus Escalation approaches for the Treatment of Relapsing-remitting Multiple Sclerosis (DELIVER-MS). The main objective of the study is to determine whether an early highly effective treatment approach, defined as use of one of four monoclonal antibodies as initial therapy, is more effective than an escalation treatment approach (any other approved medication as initial therapy with subsequent escalation to higher efficacy treatments guided by radiological and clinical evaluation). The primary endpoint of the study is reduction in normalized brain volume loss from baseline visit to month 36 visit using MRI. Brain volume loss was selected as the best short-term predictor of long-term clinical disability. A total of 400 participants will be randomized 1:1 using minimization to account for age and sex by site, and 400 will be enrolled into a parallel observational cohort. The study results will help guide overall treatment philosophy and will have important implications for patient choice, clinical practice, and treatment access

Food Insecurity Trajectories in the US During the First Year of the COVID-19 Pandemic.

INTRODUCTION: The objective of this study was to characterize population-level trajectories in the probability of food insecurity in the US during the first year of the COVID-19 pandemic and to examine sociodemographic correlates associated with identified trajectories. METHODS: We analyzed data from the Understanding America Study survey, a nationally representative panel (N = 7,944) that assessed food insecurity every 2 weeks from April 1, 2020, through March 16, 2021. We used latent class growth analysis to determine patterns (or classes) of pandemic-related food insecurity during a 1-year period. RESULTS: We found 10 classes of trajectories of food insecurity, including 1 class of consistent food security (64.7%), 1 class of consistent food insecurity (3.4%), 5 classes of decreasing food insecurity (15.8%), 2 classes of increasing food insecurity (4.6%), and 1 class of stable but elevated food insecurity (11.6%). Relative to the class that remained food secure, other classes were younger, had a greater proportion of women, and tended to identify with a racial or ethnic minority group. CONCLUSION: We found heterogeneous longitudinal patterns in the development, resolution, or persistence of food insecurity during the first year of the COVID-19 pandemic. Experiences of food insecurity were highly variable across the US population, with one-third experiencing some form of food insecurity risk. Findings have implications for identifying population groups who are at increased risk of food insecurity and related health disparities beyond the first year of the pandemic

Additional file 2: Figure S1. of A retrospective cohort study of factors relating to the longitudinal change in birth weight

Change in maternal demographics over time. A Change in maternal race over time B Change in Maternal comorbid conditions of HTN and DM over time. C Change in mean Maternal Body Mass Index over time. (JPEG 55 kb

Additional file 1: Table S1. of A retrospective cohort study of factors relating to the longitudinal change in birth weight

Univariate Pearson correlation coefficients between independent variables and birth weight, birth length, and ponderal index. Table S2. Univariate Pearson correlation coefficients between independent variables to assess for multicollinearity. Table S3A. Estimated Change in Newborn Birth Weight by Relevant Maternal and Newborn Factors by Race for African Americans. Table S3B. Estimated Change in Newborn Birth Weight by Relevant Maternal and Newborn Factors by Race for non-African Americans. (DOC 64 kb

Risk-period-cohort approach for averting identification problems in longitudinal models.

In epidemiology, gerontology, human development and the social sciences, age-period-cohort (APC) models are used to study the variability in trajectories of change over time. A well-known issue exists in simultaneously identifying age, period and birth cohort effects, namely that the three characteristics comprise a perfectly collinear system. That is, since age = period-cohort, only two of these effects are estimable at a time. In this paper, we introduce an alternative framework for considering effects relating to age, period and birth cohort. In particular, instead of directly modeling age in the presence of period and cohort effects, we propose a risk modeling approach to characterize age-related risk (i.e., a hybrid of multiple biological and sociological influences to evaluate phenomena associated with growing older). The properties of this approach, termed risk-period-cohort (RPC), are described in this paper and studied by simulations. We show that, except for pathological circumstances where risk is uniquely determined by age, using such risk indices obviates the problem of collinearity. We also show that the size of the chronological age effect in the risk prediction model associates with the correlation between a risk index and chronological age and that the RPC approach can satisfactorily recover cohort and period effects in most cases. We illustrate the advantages of RPC compared to traditional APC analysis on 27496 individuals from NHANES survey data (2005-2016) to study the longitudinal variability in depression screening over time. Our RPC method has broad implications for examining processes of change over time in longitudinal studies

Clinical Characteristics of Individuals With Serious Mental Illness and Type 2 Diabetes

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