Jones R H, Ford P M, Hamman R F
Department of Preventive Medicine and Biometrics, School of Medicine, University of Colorado Health Sciences Center, Denver 80262.
Biometrics. 1988 Dec;44(4):1131-44.
A new test using incidence data is developed for testing whether two or more groups have the same seasonal pattern. The method fits sine waves to the data with a fundamental period of one cycle per year, and has the possibility of using higher harmonics, when necessary, to adequately model the data. The seasonal pattern can, therefore, have an arbitrary shape. The method allows for different length time intervals and different size populations at risk in the time intervals. Maximum likelihood estimation, based on the Poisson distribution, is used to determine the parameters of the model. Likelihood ratio tests and Akaike's information criterion (AIC) are used to determine the number of harmonics, and to test hypotheses. This method has been used to test for seasonal patterns in the incidence of insulin-dependent diabetes mellitus (IDDM) in Colorado among persons aged 0-17 years. Comparisons of seasonal patterns are made between males and females, and three age groups, each controlling for the other effect as in analysis of variance. Other potential applications of this approach are also discussed. A basic program is available for an IBM-PC to carry out these analyses.
一种利用发病率数据的新测试方法被开发出来，用于检验两个或更多组是否具有相同的季节性模式。该方法将正弦波拟合到数据上，基本周期为每年一个周期，并且在必要时有可能使用更高次谐波来充分模拟数据。因此，季节性模式可以具有任意形状。该方法允许不同的时间间隔长度以及时间间隔内不同规模的风险人群。基于泊松分布的最大似然估计用于确定模型的参数。似然比检验和赤池信息准则（AIC）用于确定谐波数量并检验假设。此方法已用于检验科罗拉多州0至17岁人群中胰岛素依赖型糖尿病（IDDM）发病率的季节性模式。在男性和女性以及三个年龄组之间进行季节性模式比较，每个组都像在方差分析中那样控制其他因素的影响。还讨论了该方法的其他潜在应用。有一个适用于IBM-PC的基本程序可用于进行这些分析。
