Natural disease course modeling of achondroplasia to evaluate the efficacy of recifercept in the absence of a placebo control arm in phase II study.

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.

Methods to assess evidence consistency in dose-response model based network meta-analysis.

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.

Modeling of Survival and Frequency of Cardiovascular-Related Hospitalization in Patients with Transthyretin Amyloid Cardiomyopathy Treated with Tafamidis.

INTRODUCTION: ATTR-ACT (Tafamidis in Transthyretin Cardiomyopathy Clinical Trial) demonstrated the efficacy and safety of tafamidis in transthyretin amyloid cardiomyopathy (ATTR-CM). Model-based analyses from ATTR-ACT can examine predictor effects on dose-response/exposure-response relationships. METHODS: Parametric hazard distributions were developed for all-cause mortality and frequency of cardiovascular-related hospitalization. Time-to-event models were fitted to survival data, and repeated time-to-event models were fitted to hospitalization data. Disease-specific characteristics were assessed as baseline predictors of event hazards. RESULTS: There were 441 patients in this analysis. At month 30, 70.5% (tafamidis) and 57.1% (placebo) of patients were alive, with 154/441 deaths reported; 495 cardiovascular-related hospitalizations occurred. The cumulative risk of death was 42.1% (95% confidence interval [CI] 24.2-58.0) lower with tafamidis than with placebo, regardless of New York Heart Association (NYHA) class; significant predictors of decreased risk were genotype (wild-type), greater 6-Minute Walk Test (6MWT) distance, higher left ventricular ejection fraction (LVEF), and lower blood urea nitrogen (BUN) and N-terminal pro-B-type natriuretic peptide concentrations. The average cumulative risk of cardiovascular-related hospitalization up to 30 months was 40.8% (95% CI 31.0-49.7) lower with tafamidis in NYHA class I/II patients. Significant predictors of reduced risk were greater 6MWT distance, higher LVEF, and lower BUN and troponin I concentrations. CONCLUSIONS: Tafamidis reduced cumulative mortality and hospitalization risk versus placebo in patients with ATTR-CM. Baseline predictors of outcome were consistent with the cardiovascular nature of the disease and suggested that earlier treatment may improve outcomes. CLINICAL TRIALS. GOV IDENTIFIER: NCT01994889 (date of registration: November 26, 2013).

Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis.

BACKGROUND: Network meta-analysis (NMA) synthesizes direct and indirect evidence on multiple treatments to estimate their relative effectiveness. However, comparisons between disconnected treatments are not possible without making strong assumptions. When studies including multiple doses of the same drug are available, model-based NMA (MBNMA) presents a novel solution to this problem by modeling a parametric dose-response relationship within an NMA framework. In this article, we illustrate several scenarios in which dose-response MBNMA can connect and strengthen evidence networks. METHODS: We created illustrative data sets by removing studies or treatments from an NMA of triptans for migraine relief. We fitted MBNMA models with different dose-response relationships. For connected networks, we compared MBNMA estimates with NMA estimates. For disconnected networks, we compared MBNMA estimates with NMA estimates from an "augmented" network connected by adding studies or treatments back into the data set. RESULTS: In connected networks, relative effect estimates from MBNMA were more precise than those from NMA models (ratio of posterior SDs NMA v. MBNMA: median = 1.13; range = 1.04-1.68). In disconnected networks, MBNMA provided estimates for all treatments where NMA could not and were consistent with NMA estimates from augmented networks for 15 of 18 data sets. In the remaining 3 of 18 data sets, a more complex dose-response relationship was required than could be fitted with the available evidence. CONCLUSIONS: Where information on multiple doses is available, MBNMA can connect disconnected networks and increase precision while making less strong assumptions than alternative approaches. MBNMA relies on correct specification of the dose-response relationship, which requires sufficient data at different doses to allow reliable estimation. We recommend that systematic reviews for NMA search for and include evidence (including phase II trials) on multiple doses of agents where available.

Evaluation of an occupation-based, mental-health program: Meeting being, belonging and becoming needs.

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.

Performance of model-based network meta-analysis (MBNMA) of time-course relationships: A simulation study.

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.

Modelling time-course relationships with multiple treatments: Model-based network meta-analysis for continuous summary outcomes.

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.

Being, belonging and becoming: Development of the 3B Scale.

BACKGROUND: The aim of the current study was to assess the construct validity and internal consistency of a measure used by an occupation-based, mental health program. The measure was formulated according to the 3B theory of being, belong and becoming and system impacts. METHODS: The scale was administered to a sample of 122 current members of Northern Initiative for Social Action. Confirmatory factor analysis (CFA) was carried out to examine the model fit of the 3B Scale. Internal reliability was assessed using Cronbach's alpha measure of internal consistency. RESULTS: The CFA confirmed the four hypothesised underlying constructs of the 3B theory. The results of the CFA reported adequate model fit. Each factor had good to excellent internal reliability with Cronbach's alpha ranging from 0.81 to 0.91. CONCLUSION: The 3B Scale is a valid and reliable tool for measuring Being, Belonging and Becoming needs in an occupation-based mental health program. The 3B Scale is an example of an outcome measure created specifically to quantify program outcomes and demonstrates that instruments suited to unique programs can be individually configured and assist occupational therapists in their efforts to measure client perspectives. These are the essential first steps to client-centred practice and the development of practice-based evidence. The 3B Scale may be useful to similar peer-led programs, as well as community-based occupational programs that foster participation. Further research is needed to determine whether the 3B Scale can detect change over time.

Many Flavors of Model-Based Meta-Analysis: Part II - Modeling Summary Level Longitudinal Responses.

Meta-analyses typically assess comparative treatment response for an end point at specific timepoints across studies. However, during drug development, it is often of interest to understand the response time-course of competitor compounds for a variety of purposes. Examples of such application include informing study design and characterizing the onset, maintenance, and offset of action. This tutorial acts as a "points for consideration" document, reviews relevant literature, and fits a longitudinal model to an example dataset.

Imputation of missing variance data using non-linear mixed effects modelling to enable an inverse variance weighted meta-analysis of summary-level longitudinal data: a case study.

Missing variances, on the basis of the summary-level data, can be a problem when an inverse variance weighted meta-analysis is undertaken. A wide range of approaches in dealing with this issue exist, such as excluding data without a variance measure, using a function of sample size as a weight and imputing the missing standard errors/deviations. A non-linear mixed effects modelling approach was taken to describe the time-course of standard deviations across 14 studies. The model was then used to make predictions of the missing standard deviations, thus, enabling a precision weighted model-based meta-analysis of a mean pain endpoint over time. Maximum likelihood and Bayesian approaches were implemented with example code to illustrate how this imputation can be carried out and to compare the output from each method. The resultant imputations were nearly identical for the two approaches. This modelling approach acknowledges the fact that standard deviations are not necessarily constant over time and can differ between treatments and across studies in a predictable way.