An Approach to Multivariate Covariate-Dependent Quantile Contours With Application to Bivariate Conditional Growth Charts
协变量依赖的多元分位数轮廓与二元条件生长图AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究多元条件分位数轮廓，并使用纵向数据分位数回归处理时间效应。
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协变量依赖的多元分位数轮廓与二元条件生长图
- 研究问题
- 如何在不施加强分布假设的情况下，构造随年龄及协变量变化的二维分位数轮廓，用于儿童身高与体重的联合筛查？
- 方法与思路
- 作者提出新的二元分位数轮廓及两阶段估计程序，依托纵向数据分位数回归处理时间效应，并提供可视化评估与模型选择建议。
- 材料陈述的贡献
- 摘要称，嵌套的年龄和协变量依赖轮廓可定位个体相对参考人群的百分位，并在模拟和儿童应用中展示联合生长监测价值。
- 阅读关注点
- 应核对多元分位数轮廓与百分位的定义、纵向依赖处理、嵌套性质，以及联合生长图相较单变量图的信息增益依据。
- 阅读边界
- 仅据版本未注明的索引摘要，两阶段程序的条件与具体比较结果未说明；更丰富的读数不等于已证明更高的临床诊断准确性。
原文摘要
Multivariate quantile contours are useful in numerous applications and have been studied in different contexts. However, no easy solutions exist when dynamic and conditional quantile contours are needed without strong distributional assumptions. In this article we propose a new form of bivariate quantile contours and a two-stage estimation procedure to take time effect into account. The proposed procedure relies on quantile regression for longitudinal data and is flexible to include potentially important covariates as necessary. In addition, we propose a visual model assessment tool and discuss a practical guideline for model selection. The performance of the proposed methodology is demonstrated by a simulation study, as well as an application to joint height–weight screening of young children in the United States. We construct bivariate growth charts by a nested sequence of age-dependent and covariate-varying quantile contours of height and weight, and use it to locate an individual subject's percentile rank with respect to a reference population. Our work shows that the proposed method is valuable for pediatric growth monitoring and provides more informative readings than the conventional approach based on univariate growth charts.