Bmc Med Res Methodol
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Bmc Med Res Methodol · Aug 2016
Performance of models for estimating absolute risk difference in multicenter trials with binary outcome.
Reporting of absolute risk difference (RD) is recommended for clinical and epidemiological prospective studies. In analyses of multicenter studies, adjustment for center is necessary when randomization is stratified by center or when there is large variation in patients outcomes across centers. While regression methods are used to estimate RD adjusted for baseline predictors and clustering, no formal evaluation of their performance has been previously conducted. ⋯ We recommend the use of a binomial or Poisson GEE model with identity link to estimate RD for correlated binary outcome data. If these models fail to run, then either a logistic regression, log Poisson regression, or linear regression GEE model can be used.
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Bmc Med Res Methodol · Aug 2016
Specialist nurses' perceptions of inviting patients to participate in clinical research studies: a qualitative descriptive study of barriers and facilitators.
Increasing the number of patients participating in research studies is a current priority in the National Health Service (NHS) in the United Kingdom. The role of specialist nurses in inviting patients to participate is important, yet little is known about their experiences of doing so. The aim of this study was to explore the perceptions of barriers and facilitators held by specialist nurses with experience of inviting adult NHS patients to a wide variety of research studies. ⋯ Our study offers several new insights regarding the role of specialist nurses in recruiting patients for research. It shows that strong local research culture and teamwork overcome some wider organisational and workload barriers reported in previous studies. In addition, and in contrast to common practice, our findings suggest research teams may benefit from individualising study training and invitation procedures to specialist nurses' preferences and requirements. Findings provide a basis for reflection on practice for specialist nurses, research teams, policymakers, and all with an interest in increasing patient participation in research.
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Bmc Med Res Methodol · Aug 2016
Measuring inter-rater reliability for nominal data - which coefficients and confidence intervals are appropriate?
Reliability of measurements is a prerequisite of medical research. For nominal data, Fleiss' kappa (in the following labelled as Fleiss' K) and Krippendorff's alpha provide the highest flexibility of the available reliability measures with respect to number of raters and categories. Our aim was to investigate which measures and which confidence intervals provide the best statistical properties for the assessment of inter-rater reliability in different situations. ⋯ Fleiss' K and Krippendorff's alpha with bootstrap confidence intervals are equally suitable for the analysis of reliability of complete nominal data. The asymptotic confidence interval for Fleiss' K should not be used. In the case of missing data or data or higher than nominal order, Krippendorff's alpha is recommended. Together with this article, we provide an R-script for calculating Fleiss' K and Krippendorff's alpha and their corresponding bootstrap confidence intervals.