Abstract
In epidemiological studies that measure the risk at different levels of exposure, the data is often only available for the analyses that summarized the response data in grouped exposure intervals. In typical methods, the midpoints are used as the assigned exposure levels for each interval. Results of the analysis with grouped data may be sensitive to the assignment of the exposure levels. In this paper, we propose a procedure for assessing J-shaped associations based on the likelihood-based assignment of values to grouped intervals of exposure, and applying the cubic spline regression models. Numerical illustrations and comparisons based on simulations showed that the proposed procedure can yield better estimates for curves than those obtained using the typical assignment method based on the midpoints of each interval.
Highlights
In epidemiological studies, it is often necessary to determine the relationship between exposure levels and the risk of disease
The reference category was assigned to non-drinkers in Grobbee et al [3], whereas 0-2 cups/day were used in Bidel et al [2], and the meanings of the reported relative risks (RRs) were different and it was inappropriate to combine them directly
We propose a procedure for assessing nonlinear associations between exposure levels and the risk of disease from a summarized grouped data, which is based on the assignment of levels to grouped exposure intervals by applying the likelihoodbased assignment procedure proposed in Takahashi and Tango [6]
Summary
It is often necessary to determine the relationship between exposure levels and the risk of disease. In traditional meta-analysis based on aggregated data, it is not possible to obtain the original data, and the published articles do not include enough data In such situations, meta-analysis of observational studies often has to rely on the summarized data where the exposure levels are grouped into intervals available from research reports. For example, Larsson and Orsini [4] performed a dose-response meta-analysis and detected a potentially nonlinear association between coffee consumption and stroke using a cubic spline model, and cubic spline regression models may have many advantages over polynomials [12]. We propose a procedure for assessing nonlinear associations between exposure levels and the risk of disease from a summarized grouped data, which is based on the assignment of levels to grouped exposure intervals by applying the likelihoodbased assignment procedure proposed in Takahashi and Tango [6]. We provide some numerical illustrations and comparisons based on simulations with typical assignments to determine the effects of exposure level assignments
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