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Mendeley readers
| Title |
A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements
|
|---|---|
| Published in |
Statistical Methods in Medical Research, November 2015
|
| DOI | 10.1177/0962280215615003 |
| Pubmed ID | |
| Authors |
Zeynep Kalaylioglu, Haydar Demirhan |
| Abstract |
Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors. |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 27% |
| Student > Master | 3 | 27% |
| Lecturer | 2 | 18% |
| Professor | 1 | 9% |
| Researcher | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 1 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 4 | 36% |
| Medicine and Dentistry | 3 | 27% |
| Environmental Science | 1 | 9% |
| Unknown | 3 | 27% |