Bmc Med Res Methodol
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Bmc Med Res Methodol · Feb 2020
Comparative StudyComparison of exclusion, imputation and modelling of missing binary outcome data in frequentist network meta-analysis.
Missing participant outcome data (MOD) are ubiquitous in systematic reviews with network meta-analysis (NMA) as they invade from the inclusion of clinical trials with reported participant losses. There are available strategies to address aggregate MOD, and in particular binary MOD, while considering the missing at random (MAR) assumption as a starting point. Little is known about their performance though regarding the meta-analytic parameters of a random-effects model for aggregate binary outcome data as obtained from trial-reports (i.e. the number of events and number of MOD out of the total randomised per arm). ⋯ The analysts should avoid applying strategies that manipulate MOD before analysis (i.e. exclusion and imputation) as they implicate the inferences negatively. Modelling MOD, on the other hand, via a pattern-mixture model to propagate the uncertainty about MAR assumption constitutes both conceptually and statistically proper strategy to address MOD in a systematic review.
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Bmc Med Res Methodol · Feb 2020
ReviewA scoping review of core outcome sets and their 'mapping' onto real-world data using prostate cancer as a case study.
A Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition. Using prostate cancer as a case study, we identified, summarized, and critically appraised published COS development studies and assessed the degree of overlap between them and selected real-world data (RWD) sources. ⋯ This scoping review identified few COS development studies in prostate cancer, some quite dated and with a growing level of methodological quality over time. This study revealed promising overlap between COS and RWD sources, though with important limitations; linking established, national patient registries to administrative data provide the best means to additionally capture patient-reported and some clinical outcomes over time. Thus, increasing the combination of different data sources and the interoperability of systems to follow larger patient groups in RWD is required.
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Bmc Med Res Methodol · Feb 2020
Randomized Controlled TrialThe methods and baseline characteristics of a VA randomized controlled study evaluating supported employment provided in primary care patient aligned care teams.
This article describes the design and baseline sample of a single-site trial comparing Individual Placement and Support (IPS) supported employment delivered within a Veterans Health Administration (VHA) primary care Patient Aligned Care Team (PACT) to treatment-as-usual vocational rehabilitation (TAU-VR) that includes transitional work. ⋯ Unique design features include evaluating the efficacy of evidenced-based IPS within the primary care setting, having broad diagnostic eligibility, and defining the primary outcome criterion as "steady employment", i.e. holding a competitive job for ≥26 weeks of the 12-month follow-up period. The findings illustrate the characteristics of a primary care veteran sample in need of employment services.
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Bmc Med Res Methodol · Feb 2020
Patient and Public Involvement (PPI) in evidence synthesis: how the PatMed study approached embedding audience responses into the expression of a meta-ethnography.
Patient and public involvement (PPI) has become enshrined as an important pillar of health services empirical research, including PPI roles during stages of research development and analysis and co-design approaches. Whilst user participation has been central to qualitative evidence synthesis (QES) for decades, as seen in the Cochrane consumer network and guidelines, meta-ethnography has been slow to incorporate user participation and published examples of this occurring within meta-ethnography are sparse. In this paper, drawing upon our own experience of conducting a meta-ethnography, we focus on what it means in practice to 'express a synthesis' (stage 7). We suggest the methodological importance of 'expression' in Noblit and Hare's seven stage process (Noblit, GW and Hare, RD. Meta-ethnography: synthesizing qualitative studies, 1988) has been overlooked, and in particular, opportunities for PPI user participation within it. ⋯ The paper aims to complement recent attempts in the literature to refine and improve guidance on conducting a meta-ethnography, highlighting opportunities for PPI user participation in the processes of interpretation, translation and expression. We discuss the implications of user participation in meta-ethnography on ideas of 'generalisability'.
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Bmc Med Res Methodol · Feb 2020
Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis.
Personalized healthcare promises to successfully advance the treatment of heterogeneous neurological disorders such as relapsing remitting multiple sclerosis by addressing the caveats of traditional healthcare. This study presents a framework for personalized prediction of treatment response based on real-world data from the NeuroTransData network. ⋯ Applying personalized predictive models in relapsing remitting multiple sclerosis patients is still new territory that is rapidly evolving and has many challenges. The proposed framework addresses the following challenges: robustness and accuracy of the predictions, generalizability to new patients and clinical sites and comparability of the predicted effectiveness of different therapies. The methodological and clinical soundness of the results builds the basis for a future support of patients and doctors when the current treatment is not generating the desired effect and they are considering a therapy switch. (A) The framework is developed using quality-proven real-world data of patients with relapsing remitting multiple sclerosis. Patients have heterogeneous individual characteristics and diverse disease profiles, indicated for example by variations in frequency of relapses and degree of disability. Longitudinal characteristics regarding disease history (e.g. number of previous relapses in the last 12 months) are extracted at the time of an intended therapy switch, i.e. at time point "Today" (left). All clinical parameters are captured in a standardized way (right). (B) The model predicts the course of the disease based on the observed data (panel A), and is able to account for the impact of various available therapies on chosen clinical endpoints. The resulting ranking of therapies has a dependency on patient characteristics, illustrated here by a different highest ranked therapy depending on the number of relapse in the previous 12 months. (C) The model is evaluated for various generalization properties. Compared to performance on the training set (gray) it is able to predict for new patients not part of the training set (red).Top: Prediction for new patients. Middle: Prediction for new clinical sites. Bottom: Prediction for different time windows. (D) In order to assess the clinical impact of the model, disease activity is compared between patients treated with the highest ranked therapy and those treated with any of the other therapies. Patients adhering to the highest ranked therapy are associated with a better disease outcome when compared to those who did not.