A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements

KALAYLIOĞLU AKYILDIZ Z. I., Demirhan H.

STATISTICAL METHODS IN MEDICAL RESEARCH, cilt.26, sa.6, ss.2885-2896, 2017 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 6
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1177/0962280215615003
  • Dergi Adı: STATISTICAL METHODS IN MEDICAL RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2885-2896
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.