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Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies

机译:广义加性模型中的Bootstrap假设检验,用于比较纵向研究中的治疗曲线

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The study of the effect of a treatment may involve the evaluation of a variable at a number of moments. When assuming a smooth curve for the mean response along time, estimation can be afforded by spline regression, in the context of generalized additive models. The novelty of our work lies in the construction of hypothesis tests to compare two curves of treatments in any interval of time for several types of response variables. The within-subject correlation is not modeled but is considered to obtain valid inferences by the use of bootstrap. We propose both semiparametric and nonparametric bootstrap approaches, based on resampling vectors of residuals or responses, respectively. Simulation studies revealed a good performance of the tests, considering, for the outcome, different distribution functions in the exponential family and varying the correlation between observations along time. We show that the sizes of bootstrap tests are close to the nominal value, with tests based on a standardized statistic having slightly better size properties. The power increases as the distance between curves increases and decreases when correlation gets higher. The usefulness of these statistical tools was confirmed using real data, thus allowing to detect changes in fish behavior when exposed to the toxin microcystin-RR.
机译:对治疗效果的研究可能涉及在多个时刻评估变量。当假设平均响应随时间变化的平滑曲线时,在广义加性模型的背景下,可以通过样条回归进行估计。我们工作的新颖性在于构建假设检验,以比较几种类型的响应变量在任意时间间隔内的两条治疗曲线。主体内相关性未建模,但被认为是通过使用引导程序来获得有效推断的。我们分别基于残差或响应的重采样向量,提出了半参数和非参数引导方法。仿真研究表明,考虑到结果,指数族具有不同的分布函数并随时间变化观察值之间的相关性,测试表现良好。我们显示自举测试的大小接近标称值,而基于标准化统计量的测试的大小属性则稍好一些。功率随着曲线之间的距离的增加而增加,而当相关性变高时则减少。这些统计工具的实用性已通过实际数据得到证实,因此可以检测暴露于毒素微囊藻毒素-RR的鱼类行为的变化。

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