Analyzing small data sets using Bayesian estimation the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors
| Autor: | van de Schoot, Rens, Broere, Joris, Perryck, Koen, Zondervan - Zwijnenburg, M. A. J., Van Loey, N.E.E., Methodology and statistics for the behavioural and social sciences, Leerstoel Hoijtink, Afd Klinische psychologie |
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| Přispěvatelé: | 25959565 - Van de Schoot, Adrianus Gerardus Joanes, Acknowledgement: the research was supported by a grant from the Netherlands organization for scientific research: NWO-VENI-451-11-008 |
| Rok vydání: | 2015 |
| Předmět: |
Operations research
lcsh:RC435-571 medicine.medical_treatment Bayesian probability burn survivors Bayesian estimation maximum likelihood prior specification power repeated measures analyses small samples mechanical ventilation PTSS Poison control Small samples Mechanical ventilation lcsh:Psychiatry Statistics Prior probability medicine Sensitivity (control systems) Burn survivor Bayes estimator Basic Research Article Repeated measures design Repeated measures analyses 3. Good health Sample size determination Power Psychology Prior specification Maximum likelihood |
| Zdroj: | European Journal of Psychotraumatology; Vol 6 (2015): incl Supplements European Journal of Psychotraumatology European Journal of Psychotraumatology, 6. Co-Action Publishing European Journal of Psychotraumatology, Vol 6, Iss 0, Pp 1-13 (2015) |
| ISSN: | 2000-8066 2000-8198 |
| Popis: | Background : 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. Keywords: Bayesian estimation; maximum likelihood; prior specification; power; repeated measures analyses; small samples; burn survivors; mechanical ventilation; PTSS Responsible Editor: Cherie Armour, University of Ulster, United Kingdom. For the abstract or full text in other languages, please see Supplementary files in the column to the right (under ‘Article Tools’). (Published: 11 March 2015) Citation: European Journal of Psychotraumatology 2015, 6 : 25216 - http://dx.doi.org/10.3402/ejpt.v6.25216 |
| Databáze: | OpenAIRE |
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