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Menopausal hormone therapy (MHT) is an effective treatment for menopause‐related symptoms. Menopause management guidelines recommend a personalized approach to menopause care, including MHT use. Decision‐making around menopause care is a complex, iterative process influenced by multiple factors framed by perspectives from both women and healthcare providers (HCPs). This narrative review aims to summarize evidence around factors affecting decision‐making regarding menopause‐related care. For HCPs, the provision of individualized risk estimates is challenging in practice given the number of potential benefits and risks to consider, and the complexity of the data available, especially within time‐limited consultations. Women seeking menopause care have the difficult task of making sense of the benefit versus risk profiles to make choices in line with their decisional needs influenced by sociocultural/economic, educational, demographic, and personal characteristics. The press, social media, and influential celebrities also impact the perception of menopause and decision‐making around it. Understanding these factors can lead to improved participation in shared decision‐making, satisfaction with the decision and decision‐making process, adherence to treatment, reduced decisional regret, efficient use of resources, and ultimately long‐term satisfaction with care. [ABSTRACT FROM AUTHOR]

Background and Objective: Contextual effects (e.g. patient expectations) may play a role in treatment effectiveness. This study aimed to estimate the magnitude of contextual effects for conservative, non‐pharmacological interventions for musculoskeletal pain conditions. A systematic review and meta‐analysis of randomized controlled trials (RCTs) that compared placebo conservative non‐pharmacological interventions to no treatment for musculoskeletal pain. The outcomes assessed included pain intensity, physical functioning, health‐related quality of life, global rating of change, depression, anxiety and sleep at immediate, short‐, medium‐ and/or long‐term follow‐up. Databases and Data Treatment: MEDLINE, EMBASE, CINAHL, Web of Science Core Collection, CENTRAL and SPORTDiscus were searched from inception to September 2021. Trial registry searches, backward and forward citation tracking and searches for prior systematic reviews were completed. The Cochrane risk of bias 2 tool was implemented. Results: The study included 64 RCTs (N = 4314) out of 8898 records. For pain intensity, a mean difference of (MD: −5.32, 95% confidence interval (CI): −7.20, −3.44, N = 57 studies with 74 outcomes, GRADE: very low) was estimated for placebo interventions. A small effect in favour of the placebo interventions for physical function was estimated (SMD: −0.22, 95% CI: −0.35, −0.09; N = 37 with 48 outcomes, GRADE: very low). Similar results were found for a broad range of patient‐reported outcomes. Meta‐regression analyses did not explain heterogeneity among analyses. Conclusion: The study found that the contextual effect of non‐pharmacological conservative interventions for musculoskeletal conditions is likely to be small. However, given the known effect sizes of recommended evidence‐based treatments for musculoskeletal conditions, it may still contribute an important component. Significance: Contextual effects of non‐pharmacological conservative interventions for musculoskeletal conditions are likely to be small for a broad range of patient‐reported outcomes (pain intensity, physical function, quality of life, global rating of change and depression). Contextual effects are unlikely, in isolation, to offer much clinical care. But these factors do have relevance in an overall treatment context as they provide almost 30% of the minimally clinically important difference. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]