Research papers
2010 · Research Synthesis Methods · 6,873 citations
There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd.
2009 · Epidemiology · 5,024 citations
The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
2019 · Worldviews on Evidence-Based Nursing · 51 citations
BACKGROUND: Nursing education and training are essential in the attainment of evidence-based practice (EBP) competence in nursing students. Although there is a growing literature on EBP among nursing students, most of these studies are confined to a single cultural group. Thus, cross-cultural studies may provide shared global perspectives and theoretical understandings for the advancement of knowledge in this critical area. AIMS: This study compared self-perceived EBP competence among nursing students in four selected countries (India, Saudi Arabia, Nigeria, and Oman) as well as perceived barriers to EBP adoption. METHODS: A descriptive, cross-sectional, and comparative survey of 1,383 nursing students from India, Saudi Arabia, Nigeria, and Oman participated in the study. The Evidence-Based Practice Questionnaire (EBP-COQ) and the BARRIERS scale were used to collect data during the months of January 2016 to August 2017. RESULTS: Cross-country comparisons revealed significant differences in EBP competence (F = 24.437, p < .001), knowledge (F = 3.621, p = .013), skills (F = 9.527, p < .001), and attitudes (F = 74.412, p < .001) among nursing students. Three variables including nursing students' gender (β = .301, p < .001), type of institution, (β = -0.339, p = .001), and type of nursing student (β = .321, p < .001) were associated with EBP competence. Barriers to EBP adoption included having no authority to change patient care policies (M = 1.65, SD = 1.05), slow publication of evidence (M = 1.59, SD = 1.01), and paucity of time in the clinical area to implement the evidence (M = 1.59, SD = 1.05). LINKING EVIDENCE TO ACTION: Both academe and hospital administration can play a pivotal role in the successful acquisition of EBP competence in nursing students.