Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment

Covariate-adjusted sensitivity analyses is proposed for missing time-to-event outcomes. The method invokes multiple imputation (MI) for the missing failure times under a variety of specifications regarding the post-withdrawal tendency for having the event of interest. With a clinical trial example, we compared methods of covariance analyses for time-to-event data, i.e., the multivariable Cox proportional hazards model and non-parametric ANCOVA, and then illustrated how to incorporate these methods into the proposed sensitivity analysis for covariate adjustment. The MI methods considered are Kaplan-Meier Multiple Imputation (KMMI), covariate-adjusted and unadjusted proportional hazards multiple imputation (PHMI). The assumptions, statistical issues, and features for these methods are discussed

Testing Random Effects in the Linear Mixed Model Using Approximate Bayes Factors

Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance and introduce a parameter that controls the relative contribution of each random effect free of the scale of the data. We integrate out the random effects and the variance components using closed form solutions. The resulting integrals needed to calculate the Bayes factor are low-dimensional integrals lacking variance components and can be efficiently approximated with Laplace’s method. We propose a default prior distribution on the parameter controlling the contribution of each random effect and conduct simulations to show that our method has good properties for model selection problems. Finally, we illustrate our methods on data from a clinical trial of patients with bipolar disorder and on data from an environmental study of water disinfection by-products and male reproductive outcomes

A robust method for comparing two treatments in a confirmatory clinical trial via multivariate time-to-event methods that jointly incorporate information from longitudinal and time-to-event data

We consider regulatory clinical trials that required a pre-specified method for the comparison of two treatments for chronic diseases (e.g. Chronic Obstructive Pulmonary Disease) in which patients suffer deterioration in a longitudinal process until death occurs. We define a composite endpoint structure that encompasses both the longitudinal data for deterioration and the time-to-event data for death, and use multivariate time-to-event methods to assess treatment differences on both data structures simultaneously, without a need for parametric assumptions or modeling. Our method is straightforward to implement, and simulations show the method has robust power in situations in which incomplete data could lead to lower than expected power for either the longitudinal or survival data. We illustrate the method on data from a study of chronic lung disease

Assessing variance components in multilevel linear models using approximate Bayes factors: A case study of ethnic disparities in birthweight

Racial/ethnic disparities in birthweight are a large source of differential morbidity and mortality worldwide and have remained largely unexplained in epidemiologic models. We assess the impact of maternal ancestry and census tract residence on infant birth weights in New York City and the modifying effects of race and nativity by incorporating random effects in a multilevel linear model. Evaluating the significance of these predictors involves the test of whether the variances of the random effects are equal to zero. This is problematic because the null hypothesis lies on the boundary of the parameter space. We generalize an approach for assessing random effects in the two-level linear model to a broader class of multilevel linear models by scaling the random effects to the residual variance and introducing parameters that control the relative contribution of the random effects. After integrating over the random effects and variance components, the resulting integrals needed to calculate the Bayes factor can be efficiently approximated with Laplace’s method

A trial like ALIC4E: why design a platform, response-adaptive, open, randomised controlled trial of antivirals for influenza-like illness?

ALIC4E is the first publicly funded, multicountry, pragmatic study determining whether antivirals should be routinely prescribed for influenza-like illness in primary care. The trial aims to go beyond determining the average treatment effect in a population to determining effects in patients with combinations of participant characteristics (age, symptom duration, illness severity, and comorbidities). It is one of the first platform, response-adaptive, open trial designs implemented in primary care, and this article aims to provide an accessible description of key aspects of the study design. 1) The platform design allows the study to remain relevant to evolving circumstances, with the ability to add treatment arms. 2) Response adaptation allows the proportion of participants with key characteristics allocated to study arms to be altered during the course of the trial according to emerging outcome data, so that participants' information will be most useful, and increasing their chances of receiving the trial intervention that will be most effective for them. 3) Because the possibility of taking placebos influences participant expectations about their treatment, and determining effects of the interventions on patient help seeking and adherence behaviour in real-world care is critical to estimates of cost-effectiveness, ALIC4E is an open-label trial

Therapy development for the mucopolysaccharidoses : updated consensus recommendations for neuropsychological endpoints

Neurological dysfunction represents a significant clinical component of many of the mucopolysaccharidoses (also known as MPS disorders). The accurate and consistent assessment of neuropsychological function is essential to gain a greater understanding of the precise natural history of these conditions and to design effective clinical trials to evaluate the impact of therapies on the brain. In 2017, an International MPS Consensus Panel published recommendations for best practice in the design and conduct of clinical studies investigating the effects of therapies on cognitive function and adaptive behavior in patients with neuronopathic mucopolysaccharidoses. Based on an International MPS Consensus Conference held in February 2020, this article provides updated consensus recommendations and expands the objectives to include approaches for assessing behavioral and social-emotional state, caregiver burden and quality of life in patients with all mucopolysaccharidoses

GLA-modified RNA treatment lowers GB3 levels in iPSC-derived cardiomyocytes from Fabry-affected individuals

Recent studies in non-human model systems have shown therapeutic potential of nucleoside-modified messenger RNA (modRNA) treatments for lysosomal storage diseases. Here, we assessed the efficacy of a modRNA treatment to restore the expression of the galactosidase alpha (GLA), which codes for α-Galactosidase A (α-GAL) enzyme, in a human cardiac model generated from induced pluripotent stem cells (iPSCs) derived from two individuals with Fabry disease. Consistent with the clinical phenotype, cardiomyocytes from iPSCs derived from Fabry-affected individuals showed accumulation of the glycosphingolipid Globotriaosylceramide (GB3), which is an α-galactosidase substrate. Furthermore, the Fabry cardiomyocytes displayed significant upregulation of lysosomal-associated proteins. Upon GLA modRNA treatment, a subset of lysosomal proteins were partially restored to wild-type levels, implying the rescue of the molecular phenotype associated with the Fabry genotype. Importantly, a significant reduction of GB3 levels was observed in GLA modRNA-treated cardiomyocytes, demonstrating that α-GAL enzymatic activity was restored. Together, our results validate the utility of iPSC-derived cardiomyocytes from affected individuals as a model to study disease processes in Fabry disease and the therapeutic potential of GLA modRNA treatment to reduce GB3 accumulation in the heart.</p

Platform adaptive trial of novel antivirals for early treatment of COVID-19 In the community (PANORAMIC): protocol for a randomised, controlled, open-label, adaptive platform trial of community novel antiviral treatment of COVID-19 in people at increased risk of more severe disease

Introduction: There is an urgent need to determine the safety, effectiveness and cost-effectiveness of novel antiviral treatments for COVID-19 in vaccinated patients in the community at increased risk of morbidity and mortality from COVID-19. // Methods and analysis: PANORAMIC is a UK-wide, open-label, prospective, adaptive, multiarm platform, randomised clinical trial that evaluates antiviral treatments for COVID-19 in the community. A master protocol governs the addition of new antiviral treatments as they become available, and the introduction and cessation of existing interventions via interim analyses. The first two interventions to be evaluated are molnupiravir (Lagevrio) and nirmatrelvir/ritonavir (Paxlovid). Eligibility criteria: community-dwelling within 5 days of onset of symptomatic COVID-19 (confirmed by PCR or lateral flow test), and either (1) aged 50 years and over, or (2) aged 18–49 years with qualifying comorbidities. Registration occurs via the trial website and by telephone. Recruitment occurs remotely through the central trial team, or in person through clinical sites. Participants are randomised to receive either usual care or a trial drug plus usual care. Outcomes are collected via a participant-completed daily electronic symptom diary for 28 days post randomisation. Participants and/or their Trial Partner are contacted by the research team after days 7, 14 and 28 if the diary is not completed, or if the participant is unable to access the diary. The primary efficacy endpoint is all-cause, non-elective hospitalisation and/or death within 28 days of randomisation. Multiple prespecified interim analyses allow interventions to be stopped for futility or superiority based on prespecified decision criteria. A prospective economic evaluation is embedded within the trial. // Ethics and dissemination: Ethical approval granted by South Central–Berkshire REC number: 21/SC/0393; IRAS project ID: 1004274. Results will be presented to policymakers and at conferences, and published in peer-reviewed journals. // Trial registration number: ISRCTN30448031; EudraCT number: 2021-005748-31

Causes and consequences of child growth faltering in low-resource settings

Growth faltering in children (low length for age or low weight for length) during the first 1,000 days of life (from conception to 2 years of age) influences short-term and long-term health and survival 1,2. Interventions such as nutritional supplementation during pregnancy and the postnatal period could help prevent growth faltering, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. Identification of age windows and population subgroups on which to focus will benefit future preventive efforts. Here we use a population intervention effects analysis of 33 longitudinal cohorts (83,671 children, 662,763 measurements) and 30 separate exposures to show that improving maternal anthropometry and child condition at birth accounted for population increases in length-for-age z-scores of up to 0.40 and weight-for-length z-scores of up to 0.15 by 24 months of age. Boys had consistently higher risk of all forms of growth faltering than girls. Early postnatal growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits exhibited higher mortality rates from birth to 2 years of age than children without growth deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes and severe consequences for children who experienced early growth faltering support a focus on pre-conception and pregnancy as a key opportunity for new preventive interventions

Child wasting and concurrent stunting in low- and middle-income countries

Sustainable Development Goal 2.2—to end malnutrition by 2030—includes the elimination of child wasting, defined as a weight-for-length z-score that is more than two standard deviations below the median of the World Health Organization standards for child growth 1. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery and persistence—key features that inform preventive interventions and estimates of disease burden. Here we analyse 21 longitudinal cohorts and show that wasting is a highly dynamic process of onset and recovery, with incidence peaking between birth and 3 months. Many more children experience an episode of wasting at some point during their first 24 months than prevalent cases at a single point in time suggest. For example, at the age of 24 months, 5.6% of children were wasted, but by the same age (24 months), 29.2% of children had experienced at least one wasting episode and 10.0% had experienced two or more episodes. Children who were wasted before the age of 6 months had a faster recovery and shorter episodes than did children who were wasted at older ages; however, early wasting increased the risk of later growth faltering, including concurrent wasting and stunting (low length-for-age z-score), and thus increased the risk of mortality. In diverse populations with high seasonal rainfall, the population average weight-for-length z-score varied substantially (more than 0.5 z in some cohorts), with the lowest mean z-scores occurring during the rainiest months; this indicates that seasonally targeted interventions could be considered. Our results show the importance of establishing interventions to prevent wasting from birth to the age of 6 months, probably through improved maternal nutrition, to complement current programmes that focus on children aged 6–59 months