計量生物学
Online ISSN : 2185-6494
Print ISSN : 0918-4430
ISSN-L : 0918-4430
原著
A Poisson Mixed Effects Model for Investigating the Exposure-by-Cohort Interaction: A Gibbs Sampling Approach
Seitaro YoshidaYutaka MatsuyamaYasuo OhashiHirotsugu Ueshima
著者情報
ジャーナル フリー

2008 年 29 巻 2 号 p. 61-74

詳細
抄録

A meta-analysis is a useful method for taking the findings of many studies and combining them in the hopes of identifying consistent patterns and sources of disagreement among those findings. While we interpret the average exposure effect, it is necessary to examine the homogeneity of the observed exposure effects across cohort, that is, exposure-by-cohort interaction. If the homogeneity is confirmed, the conclusions concerning exposure effects can be generalized to a broader population. In this paper, a Poisson mixed effects model is used to investigate the cohort effects on the exposure as well as on the baseline risk. The marginal posterior distributions are estimated by a Markov Chain Monte Carlo method, i.e. the Gibbs sampling, to overcome current computational limitations. We illustrate the methods with analyses of data from the Japan Arteriosclerosis Longitudinal Study, in which the effects of smoking on stroke events are examined based on the individual data of 23,860 subjects among 10 cohorts.

著者関連情報
© 2008 The Biometric Society of Japan
次の記事
feedback
Top