alexa Cubic Spline Regression of J-shaped Dose-Response Curves with Likelihood-based Assignments of Grouped Exposure Levels
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Cubic Spline Regression of J-shaped Dose-Response Curves with Likelihood-based Assignments of Grouped Exposure Levels

Kunihiko Takahashi1*, Hiroyuki Nakao2 and Satoshi Hattori3

1Department of Biostatistics, Nagoya University Graduate School of Medicine, Japan

2Center for Public Health Informatics, National Institute of Public Health, Japan

3Biostatistics Center, Kurume University, Japan

*Corresponding Author:
Kunihiko Takahashi
Department of Biostatistics
Nagoya University Graduate School of Medicine
65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
Tel: +81-52-744-2489
Fax: +81-52-744-2488
E-mail: [email protected]

Received Date: November 18, 2013; Accepted Date: November 28, 2013; Published Date: November 30, 2013

Citation: Takahashi K, Nakao H, Hattori S (2013) Cubic Spline Regression of J-shaped Dose-Response Curves with Likelihood-based Assignments of Grouped Exposure Levels. J Biomet Biostat 4:181. doi: 10.4172/2155-6180.1000181

Copyright: © 2013 Takahashi K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

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.

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