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Meta-analysis often requires pooling of correlated estimates to compute regression
slopes (trends) across different exposure or treatment levels. The authors propose
two methods that account for the correlations but require only the summary estimates
and marginal data from the studies. These methods provide more efficient estimates
of regression slope, more accurate variance estimates, and more valid heterogeneity
tests than those previously available. One method also allows estimation of nonlinear
trend components, such as quadratic effects. The authors illustrate these methods
in a meta-analysis of alcohol use and breast cancer.