A Bayesian hierarchical model for multi-level repeated ordinal data: analysis of oral practice examinations in a large anaesthesiology training programme
✍ Scribed by Ming Tan; Yinsheng Qu; Ed Mascha; Armin Schubert
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 113 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0277-6715
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✦ Synopsis
Oral practice examinations (OPEs) are used in many anaesthesiology programmes to familiarize anaesthesiology residents with the format of the oral examination administered by the American Board of Anesthesiology. The OPE outcome ("nal grade) consists of &De"nite Not Pass', &Probable Not Pass', &Probable Pass' and &De"nite Pass'. In our study to assess the validity of the OPE, residents took an average of two (ranging from one to six) OPEs, each of which was evaluated by two board certi"ed anaesthesiologists randomly selected from a pool of 12. A key question of interest was to identify factors, for example, the length of training, didactic experience and other characteristics, that most in#uence OPE outcome. In addition, we were interested in assessing the reliability of the "nal grade, that is, the covariance parameters are of interest as well. However, estimating variance components in multi-level data with an unequal number of repeated ordinal outcomes presents several statistical challenges, such as how to estimate high dimensional random e!ects parameters, especially for ordinal outcomes. We propose a Bayesian hierarchical proportional odds model for data with such complexity. The #exibility of such a model allows us to make inference on the association of OPE outcomes with other factors and to estimate the variance components as well.