Bmc Med Res Methodol
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Bmc Med Res Methodol · Jun 2019
The impact of varying cluster size in cross-sectional stepped-wedge cluster randomised trials.
Cluster randomised trials with unequal sized clusters often have lower precision than with clusters of equal size. To allow for this, sample sizes are inflated by a modified version of the design effect for clustering. These inflation factors are valid under the assumption that randomisation is stratified by cluster size. We investigate the impact of unequal cluster size when that constraint is relaxed, with particular focus on the stepped-wedge cluster randomised trial, where this is more difficult to achieve. ⋯ The actual realised power in a stepped-wedge trial might be substantially higher or lower than that estimated. This is particularly important when there are a small number of clusters or the variability in cluster sizes is large. Constraining the randomisation on cluster size, where feasible, might mitigate this effect.
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Bmc Med Res Methodol · Jun 2019
Effective stakeholder engagement: design and implementation of a clinical trial (SWOG S1415CD) to improve cancer care.
The Fred Hutchinson Cancer Research Center has engaged an External Stakeholder Advisory Group (ESAG) in the planning and implementation of the TrACER Study (S1415CD), a five-year pragmatic clinical trial assessing the effectiveness of a guideline-based colony stimulating factor standing order intervention. The trial is being conducted by SWOG through the National Cancer Institute Community Oncology Research Program in 45 clinics. The ESAG includes ten patient partners, two payers, two pharmacists, two guideline experts, four providers and one medical ethicist. This manuscript describes the ESAG's role and impact on the trial. ⋯ Diverse stakeholder engagement has been essential in optimizing the design, implementation and planned dissemination of the TrACER Study. The lessons described in the manuscript may assist others to effectively partner with stakeholders on clinical research.
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Bmc Med Res Methodol · May 2019
ReviewAre non-constant rates and non-proportional treatment effects accounted for in the design and analysis of randomised controlled trials? A review of current practice.
Most clinical trials with time-to-event primary outcomes are designed assuming constant event rates and proportional hazards over time. Non-constant event rates and non-proportional hazards are seen increasingly frequently in trials. The objectives of this review were firstly to identify whether non-constant event rates and time-dependent treatment effects were allowed for in sample size calculations of trials, and secondly to assess the methods used for the analysis and reporting of time-to-event outcomes including how researchers accounted for non-proportional treatment effects. ⋯ Our review confirmed that when designing trials with time-to-event primary outcomes, methodologies assuming constant event rates and proportional hazards were predominantly used despite potential efficiencies in sample size needed or power achieved using alternative methods. The Cox proportional hazards model was used almost exclusively to present inferential results, yet testing and reporting of the pivotal assumption underpinning this estimation method was lacking.
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Bmc Med Res Methodol · Apr 2019
Comparative StudyAn empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis.
A number of strategies have been proposed to handle missing binary outcome data (MOD) in systematic reviews. However, none of these have been evaluated empirically in a series of published systematic reviews. ⋯ Addressing MOD using extreme scenarios and/or ignoring the uncertainty about the scenarios may negatively affect NMA estimates. Modelling MOD via the IMOR parameter can ensure bias-adjusted estimates and offer valuable insights into missingness mechanisms. The researcher should seek an expert opinion in order to decide on the structure of log IMOR that best aligns to the condition and interventions studied and to define a proper prior distribution for log IMOR. Our findings also apply to pairwise meta-analyses.
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Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data. ⋯ We use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.