| 高文龙,林和,刘小宁,任晓卫,李娟生,申希平,朱素玲.贝叶斯log-binomial回归方法评估患病率比的研究[J].中华流行病学杂志,2017,38(3):400-405 |
| 贝叶斯log-binomial回归方法评估患病率比的研究 |
| Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model |
| 收稿日期:2016-09-08 出版日期:2017-03-18 |
| DOI：10.3760/cma.j.issn.0254-6450.2017.03.025 |
| 中文关键词: 贝叶斯定理 回归分析 模型,统计学 患病率比 log-binomial回归 |
| 英文关键词: Bayes theorem Regression analysis Models, statistical Prevalence ratio Log-binomial regression |
| 基金项目:教育部人文社科基金（15XJC910001）；兰州大学中央高校基本科研业务费（lzujbky-2016-025） |
|
| 摘要点击次数: 3918 |
| 全文下载次数: 1574 |
| 中文摘要: |
| 探讨贝叶斯log-binomial回归估计患病率比的方法及应用。以看护人识别腹泻危险症状与婴幼儿腹泻求医关系为实例，利用Openbugs软件拟合贝叶斯log-binomial回归模型估计看护人识别腹泻危险症状与婴幼儿腹泻求医关系的患病率比（prevalence ratio，PR）。看护人能识别腹泻危险症状可提高大约13%的求医概率。贝叶斯log-binomial回归3个模型均收敛，估计的PR值（95% CI）分别为1.130（1.005～1.265）、1.128（1.001～1.264）、1.132（1.004～1.267）；常规log-binomial回归模型1和模型2收敛，估计的PR值（95% CI）分别为1.130（1.055～1.206）和1.126（1.051～1.203），但模型3不收敛，用复制方法估计PR值（95% CI）为1.125（1.051～1.200）。贝叶斯log-binomial回归3个模型PR的点估计和区间估计虽与常规log-binomial回归稍有差异，但整体一致性较好。贝叶斯log-binomial回归能有效地估计PR，模型不收敛问题少，与常规log-binomial回归相比在应用上更有优势。 |
| 英文摘要: |
| To evaluate the estimation of prevalence ratio (PR) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant's risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1:not adjusting for the covariates; model 2:adjusting for duration of caregivers' education, model 3:adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95%CI:1.005-1.265), 1.128(95%CI:1.001-1.264) and 1.132(95%CI:1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95%CI:1.055-1.206) and 1.126(95%CI:1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR, which was 1.125 (95%CI:1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR. Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model. |
|
查看全文
Html全文
查看/发表评论 下载PDF阅读器 |
|
| 关闭 |
