Meta-Analysis for Linear and Nonlinear Dose-Response Relations: Examples, an Evaluation of Approximations, and Software
Meta-Analysis for Linear and Nonlinear Dose-Response Relations: Examples, an Evaluation of Approximations, and Software
复制标题
DOI：
10.1093/aje/kwr265
复制
发表时间：
2012-01-01
影响因子：
5
通讯作者：
Spiegelman, Donna
中科院分区：
文献类型：
--
作者：
Orsini, Nicola;Li, Ruifeng;Spiegelman, Donna
在已发表的有序分类暴露-反应数据的荟萃分析中，提出了对数线性暴露-反应关系相对风险的两种点和区间估计方法。作者将使用这两种方法对已发表数据进行荟萃分析的结果与原始数据可用时的结果进行了比较，并调查了有效使用每种荟萃分析方法所需的近似值失效的情况。然后，他们扩展了处理非线性暴露-响应关系的方法。在本文中，通过正在进行的饮食与癌症前瞻性研究汇集项目中关于饮酒与结直肠癌和肺癌风险之间关系的研究来说明方法。在这些例子中，汇总发表数据的荟萃分析结果与单个原始数据的汇总分析结果之间的差异很小。然而，错误地假设同一研究中暴露类别的相对风险估计值之间没有相关性，会导致趋势的置信区间偏倚，当其他模型协变量存在强混淆时，非线性和研究间异质性检验的P值也会偏倚。作者举例说明了使用2个公开可用的用户友好程序（Stata和SAS）来实施剂量-反应数据的荟萃分析。
Two methods for point and interval estimation of relative risk for log-linear exposure-response relations in meta-analyses of published ordinal categorical exposure-response data have been proposed. The authors compared the results of a meta-analysis of published data using each of the 2 methods with the results that would be obtained if the primary data were available and investigated the circumstances under which the approximations required for valid use of each meta-analytic method break down. They then extended the methods to handle nonlinear exposure-response relations. In the present article, methods are illustrated using studies of the relation between alcohol consumption and colorectal and lung cancer risks from the ongoing Pooling Project of Prospective Studies of Diet and Cancer. In these examples, the differences between the results of a meta-analysis of summarized published data and the pooled analysis of the individual original data were small. However, incorrectly assuming no correlation between relative risk estimates for exposure categories from the same study gave biased confidence intervals for the trend and biased P values for the tests for nonlinearity and between-study heterogeneity when there was strong confounding by other model covariates. The authors illustrate the use of 2 publicly available user-friendly programs (Stata and SAS) to implement meta-analysis for dose-response data.