Bayesian Piecewise Linear Mixed Models With a Random Change Point An Application to BMI Rebound in Childhood
Bayesian Piecewise Linear Mixed Models With a Random Change Point An Application to BMI Rebound in Childhood
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DOI：
10.1097/ede.0000000000000723
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发表时间：
2017-11-01
期刊：
影响因子：
5.4
通讯作者：
Tilling, Kate
中科院分区：
文献类型：
--
作者：
Brilleman, Samuel L.;Howe, Laura D.;Tilling, Kate
背景：体重指数（BMI）反弹是指儿童期BMI第二次上升的开始。准确估计个体 BMI 反弹的时间非常重要，因为它与以后生活的健康结果相关。方法：我们根据雅芳家长和儿童纵向研究估计了 6545 名儿童的 BMI 轨迹。我们使用了一种新颖的贝叶斯两相分段线性混合模型，其中“变化点”是个体水平的随机效应，对应于个体特定的 BMI 反弹时间。该模型的个体水平随机效应（截距、变化前斜率、变化后斜率、变化点）呈多元正态分布，具有非结构化方差-协方差矩阵，从而允许所有随机效应之间存在相关性。结果：BMI 反弹时的平均年龄（平均变化点）为 6.5（95% 可信区间：6.4 至 6.6）岁。 BMI 反弹的个体特异性时间（随机效应）的标准差为：女性 2.0 年，男性 1.6 年。变化前斜率和变化点之间的相关性为 0.57，表明反弹前 BMI 下降速度较快与反弹发生在较早的年龄相关。模拟表明，假设所有个体有一个共同的变化点或基于分数多项式的非线性轨迹，模型的估计值比模型的估计值偏差更小。结论：我们的模型灵活地估计了个体特定的 BMI 反弹时间，同时保留了有意义且易于解释的参数。它适用于任何希望估计个体之间变化的变化点过程的情况。
Background: Body mass index (BMI) rebound refers to the beginning of the second rise in BMI during childhood. Accurate estimation of an individual's timing of BMI rebound is important because it is associated with health outcomes in later life.Methods: We estimated BMI trajectories for 6545 children from the Avon Longitudinal Study of Parents and Children. We used a novel Bayesian two-phase piecewise linear mixed model where the "change point" was an individual-level random effect corresponding to the individual-specific timing of BMI rebound. The model's individual-level random effects (intercept, prechange slope, postchange slope, change point) were multivariate normally distributed with an unstructured variance-covariance matrix, thereby, allowing for correlation between all random effects.Results: Average age at BMI rebound (mean change point) was 6.5 (95% credible interval: 6.4 to 6.6) years. The standard deviation of the individual-specific timing of BMI rebound (random effects) was 2.0 years for females and 1.6 years for males. Correlation between the prechange slope and change point was 0.57, suggesting that faster rates of decline in BMI prior to rebound were associated with rebound occurring at an earlier age. Simulations showed that estimates from the model were less biased than those from models, assuming a common change point for all individuals or a nonlinear trajectory based on fractional polynomials.Conclusions: Our model flexibly estimated the individual-specific timing of BMI rebound, while retaining parameters that are meaningful and easy to interpret. It is applicable in any situation where one wishes to estimate a change-point process which varies between individuals.