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. 2012 Jan 1;175(1):66-73.
doi: 10.1093/aje/kwr265. Epub 2011 Dec 1.

Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software

Nicola Orsini  1 , Ruifeng Li, Alicja Wolk, Polyna Khudyakov, Donna Spiegelman
Affiliations

Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software

Nicola Orsini et al. Am J Epidemiol. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
Dose-response relations between alcohol intake and relative risks of A) colorectal cancer (P for nonlinearity = 0.05) and B) lung cancer (P for nonlinearity = 0.22). Data were modeled with fixed-effects restricted cubic spline models with 4 knots and using the Greenland and Longnecker method to estimate the covariances of multivariable-adjusted relative risks. Lines with long dashes represent the pointwise 95% confidence intervals for the fitted nonlinear trend (solid line). Lines with short dashes represent the linear trend.
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