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Time-course model-based network meta-analysis (MBNMA) has been proposed as a framework to combine treatment comparisons from a network of randomized controlled trials reporting outcomes at multiple time-points. This can explain heterogeneity/inconsistency that arises by pooling studies with different follow-up times and allow inclusion of studies from earlier in drug development. The aim of this study is to explore using simulation: (a) how MBNMA model parameters are affected by the quantity/location of observed time-points across studies/comparisons, (b) how reliably an appropriate MBNMA model can be identified, (c) the robustness of model estimates and predictions under different dataset characteristics. Our results indicate that model parameters for a given treatment comparison are estimated with low mean bias even when no direct evidence was available, provided there was sufficient indirect evidence to estimate the time-course. A staged model selection strategy that selects time-course function, then heterogeneity, then covariance structure, identified the true model most reliably and efficiently. Predictions and parameter estimates from selected models had low mean bias even in the presence of high heterogeneity/correlation between time-points. However, failure to properly account for heterogeneity/correlation could lead to high error in precision of the estimates. Time-course MBNMA provides a statistically robust framework for synthesizing direct and indirect evidence to estimate relative effects and predicted mean responses whilst accounting for time-course and incorporating correlation and heterogeneity. This supports the use of MBNMA in evidence synthesis, particularly when additional studies are available with follow-up times that would otherwise prohibit their inclusion by conventional meta-analysis.

Background: Model-based meta-analysis (MBMA) is increasingly used to inform drug-development decisions by synthesising results from multiple studies to estimate treatment, dose-response, and time-course characteristics. Network meta-analysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments, to inform reimbursement decisions. Recently, a framework for dose-response model-based network meta-analysis (MBNMA) has been proposed that combines, often nonlinear, MBMA modelling with the statistically robust properties of NMA. Here, we aim to extend this framework to time-course models. Methods: We propose a Bayesian time-course MBNMA modelling framework for continuous summary outcomes that allows for nonlinear modelling of multiparameter time-course functions, accounts for residual correlation between observations, preserves randomisation by modelling relative effects, and allows for testing of inconsistency between direct and indirect evidence on the time-course parameters. We demonstrate our modelling framework using an illustrative dataset of 23 trials investigating treatments for pain in osteoarthritis. Results: Of the time-course functions that we explored, the Emax model gave the best fit to the data and has biological plausibility. Some simplifying assumptions were needed to identify the ET50, due to few observations at early follow-up times. Treatment estimates were robust to the inclusion of correlations in the likelihood. Conclusions: Time-course MBNMA provides a statistically robust framework for synthesising evidence on multiple treatments at multiple time points. The use of placebo-controlled studies in drug-development means there is limited potential for inconsistency. The methods can inform drug-development decisions and provide the rigour needed in the reimbursement decision-making process.

While randomized, placebo‐controlled, double‐blinded clinical studies are the gold standard for evaluating the efficacy of investigational drugs, the use of placebo in children with achondroplasia should be limited because it provides no clinical benefit while exhausting study participants' treatment window. Recifercept is an investigational drug for treating children with achondroplasia aged 2–10 years. An alternative efficacy evaluation method, instead of a placebo control arm, was employed in the phase II study. Prior to participating in the phase II study, participants completed a natural history (NH) study. Based on the NH data, a multi‐variate linear mixed effects natural disease course model of three anthropometric end points (standing height, sitting height, and arm span) was developed. The model was validated using published growth charts of children with achondroplasia. Subsequently, the model was used to simulate the natural growth trajectories (without any treatment) of the three anthropometric end points for the individuals enrolled in the phase II study. To quantify the efficacy of recifercept, the simulations were compared with the observations post‐recifercept treatment in the phase II study. For all the tested doses of recifercept, at the individual level, the observations were comparable to the simulations at 6 and 12 months post‐recifercept treatment, suggesting no treatment effect. The results contributed to the decision of terminating recifercept clinical development. This work delivers a framework that could eliminate the need for placebo in clinical trials yet provide sufficient evidence for evaluating the efficacy of an investigational drug. [ABSTRACT FROM AUTHOR]

Introduction: Being, belonging and becoming are important theoretical constructs for occupational scientists and therapists, and for members of Northern Initiative for Social Action (NISA), located in northern Ontario, Canada. Collaborative research with service users guided the development of NISA and its evaluation tool: the 3B~S Scale. The aim of this paper is to share the results of the 2018 program evaluation. Methods: 113 participants completed a questionnaire consisting of the 3B~S Scale, demographic and program satisfaction questions, and open‐ended questions. Quantitative analysis used descriptive statistics followed by ordinal logistic regression to determine the intersectional effects of gender, race and age on becoming and system impact outcomes. Open‐ended responses were analysed thematically and triangulated with quantitative findings. Results: Participants agreed‐to‐strongly agreed that the program met their 3B needs (x = 4.20, SD = 0.24). Participants indicated strong satisfaction with the program (x = 4.38, SD = 0.66), and agreement that participating in the program reduced their reliance on other system‐based services (x = 3.96, SD = 0.24). The regression revealed no significant differences in gender, race or age in predicting six of 10 outcomes examined; race was not significant for any outcome. Younger females were more likely to agree that the work they do is part of a larger community charitable purpose, the program is helping them to achieve their goals, and is increasing their involvement in community. Younger participants were more likely to agree that participation facilitated a return to school or employment than older participants. Conclusions: Occupation‐based, mental health programs that address participants' being, belonging and becoming needs can contribute to improvements in perceived mental health and well‐being, as well as to improved community and system usage outcomes. The NISA model provides a framework for clinically operationalising the 3B's and may provide a unique contribution to ongoing theoretical discussions of these constructs within occupational therapy and science. [ABSTRACT FROM AUTHOR]

In this short work, the author will reflect on how we might understand the technologysubject relationship in a way that equally captures the position of the individual with a disability and that of the interplanetary astronaut. The works of Tamar Sharon in mediated posthumanism and Dan Goodley in critical disability studies will be consulted. This cursory exploration will conclude that both the astronaut and the individual with a disability are congruent posthuman subjects insofar as their relationship to technology challenges the idea of a transhumanist overcoming of human limits. Exploring this relationship can tell us something about how posthuman subjects may be understood more generally. [ABSTRACT FROM AUTHOR]

The intent of this article is to define the concept of context during information retrieval and to better understand its impact on the definition of relevancy and its evaluation by the user, namely the assessment of relevancy. Following a discussion of the concept of context, the author explores the principal definitions of relevancy as well as several theoretical models that underscore the increasing influence of the socio-technical approach on these models, specifically the integration of human and technical components, including their interactions. The author outlines the criteria used to evaluate the relevancy of the research results and describes how these criteria can be influenced by the different dimensions of the context. Finally, he underscores the dynamic and multidimensional character of the concepts of relevancy and context. [ABSTRACT FROM AUTHOR]

Network meta-analysis (NMA) simultaneously estimates multiple relative treatment effects based on evidence that forms a network of treatment comparisons. Heterogeneity in treatment definitions, such as dose, can lead to a violation of the consistency assumption that underpins NMA. Model-based NMA (MBNMA) methods have been proposed that allow functional dose-response relationships to be estimated within an NMA, which avoids lumping different doses together and thereby reduces the likelihood of inconsistency. Dose-response MBNMA relies on appropriate specification of the dose-response relationship as well as consistency of relative effects. In this article we describe methods to check for inconsistency in dose-response MBNMA models. Global and local (node-splitting) tests for inconsistency are described that account for studies with ≥3 arms that are typical in dose-finding trials. We show that consistency needs to be assessed with respect to the choice of dose-response function. We illustrate the methods using a network comparing biologics for moderate-to-severe psoriasis. By comparing results from an Emax and an exponential dose-response function we show that failure to correctly characterise the dose-response can introduce apparent inconsistency. The number of comparisons for which node-splitting is possible is also shown to be dependent on the complexity of the selected dose-response function. We highlight that the nature of dose-finding studies, which typically compare multiple doses of the same agent, provide limited scope to assess inconsistency, but these study designs help guard against inconsistency in the first place. We demonstrate the importance of assessing consistency to obtain robust relative effects to inform drug-development and policy decisions., (© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)