Am J Epidemiol 2004; 159:1077-1086.
Copyright © 2004 by the Johns
Hopkins Bloomberg School of Public Health
ORIGINAL CONTRIBUTIONS |
Flexible Meta-Regression Functions for Modeling Aggregate Dose-Response Data, with an Application to Alcohol and Mortality
From the Department of Statistics, University of Milan–Bicocca, Milan, Italy.
In this paper, the authors describe fractional polynomials and cubic splines with which to represent smooth dose-response relations in summarizing meta-analytical aggregate data. Use of these two curve-fitting families can help prevent the problems arising from inappropriate linearity assumptions. These methods are illustrated in the problem of estimating the shape of the dose-response curve between alcohol consumption and all-cause mortality risk. The authors considered aggregate data from 29 cohort studies investigating this issue (1966–2000). J-shaped curves with a nadir at approximately 5–7 g/day of alcohol consumption and a last protective dose of 47–60 g/day were consistently obtained from fractional polynomials and cubic splines. The authors conclude that both of the curve-fitting families are useful tools with which to explore dose-response epidemiologic questions by means of meta-analytical approaches, especially when important nonlinearity is anticipated.
alcohol drinking; dose-response; meta-analysis; mortality; polynomial regression; regression analysis; spline smoothing
Abbreviations: Abbreviations: AIC, Akaike’s Information Criterion; CI, confidence interval; RR, relative risk.
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