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dc.contributor.authorVan de Schoot, Rens
dc.contributor.authorBroere, Joris J.
dc.contributor.authorPerryck, Koen H.
dc.contributor.authorZondervan-Zwijnenburg, Mariëlle
dc.contributor.authorVan Loey, Nancy E.
dc.date.accessioned2017-02-17T08:39:11Z
dc.date.available2017-02-17T08:39:11Z
dc.date.issued2015
dc.identifier.citationVan De Schoot, A.G.J. et al. 2015. Analyzing small data sets using Bayesian estimation the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European Journal Of Psychotraumatology, 6(25216):1–13. [http://tandfonline.com/action/journalInformation?show=aimsScope&journalCode=zept]
dc.identifier.issn2000–8066
dc.identifier.issn2000–8066 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/20402
dc.description.abstractBackground: The analysis of small data sets in longitudinal studies can lead to power issues and often suffers from biased parameter values. These issues can be solved by using Bayesian estimation in conjunction with informative prior  distributions. By means of a simulation study and an empirical example concerning posttraumatic stress symptoms (PTSS) following mechanical ventilation in burn survivors, we demonstrate the advantages and potential pitfalls of using Bayesian estimation." "Methods: First, we show how to specify prior distributions and by means of a sensitivity analysis we demonstrate how to check the exact influence of the prior (mis–) specification. Thereafter, we show by means of a simulation the situations in which the Bayesian approach outperforms the default, maximum likelihood and approach. Finally, we re–analyze empirical data on burn survivors which provided preliminary evidence of an aversive influence of a period of mechanical ventilation on the course of PTSS following burns." "Results: Not suprisingly, maximum likelihood estimation showed insufficient coverage as well as power with very small samples. Only when Bayesian analysis, in conjunction with informative priors, was used power increased to acceptable levels. As expected, we showed that the smaller the sample size the more the results rely on the prior specification." "Conclusion: We show that two issues often encountered during analysis of small samples, power and biased parameters, can be solved by including prior information into Bayesian analysis. We argue that the use of informative priors should always be reported together with a sensitivity analysis.
dc.description.urihttp://www.ejpt.net/index.php/ejpt/article/view/25216
dc.description.urihttp://dx.doi.org/10.3402/ejpt.v6.25216
dc.description.urihttp://europepmc.org/articles/PMC4357639
dc.publisherCo–action Publishing
dc.subjectBayesian estimation
dc.subjectMaximum likelihood
dc.subjectPrior specification
dc.subjectPower
dc.subjectRepeated measures analyses
dc.subjectSmall samples
dc.subjectBurn survivor
dc.subjectMechanical ventilation
dc.subjectPTSS
dc.titleAnalyzing small data sets using Bayesian estimation the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors
dc.typeArticle
dc.contributor.researchID25959565 – Van De Schoot, Adrianus Gerardus Joanes


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