Department of Biostatistics, Christian Medical College, Vellore, 632002, India.
BMC Med Res Methodol. 2022 Mar 21;22(1):76. doi: 10.1186/s12874-022-01566-0.
Longitudinal studies are important to understand patterns of growth in children and limited in India. It is important to identify an approach for characterising growth trajectories to distinguish between children who have healthy growth and those growth is poor. Many statistical approaches are available to assess the longitudinal growth data and which are difficult to recognize the pattern. In this research study, we employed functional principal component analysis (FPCA) as a statistical method to find the pattern of growth data. The purpose of this study is to describe the longitudinal child growth trajectory pattern under 3 years of age using functional principal component method.
Children born between March 2002 and August 2003 (n = 290) were followed until their third birthday in three neighbouring slums in Vellore, South India. Field workers visited homes to collect details of morbidity twice a week. Height and weight were measured monthly from 1 month of age in a study-run clinic. Longitudinal child growth trajectory pattern were extracted using Functional Principal Component analysis using B-spline basis functions with smoothing parameters. Functional linear model was used to assess the factors association with the growth functions.
We have obtained four FPCs explained by 86.5, 3.9, 3.1 and 2.2% of the variation respectively for the height functions. For height, 38% of the children's had poor growth trajectories. Similarly, three FPCs explained 76.2, 8.8, and 4.7% respectively for the weight functions and 44% of the children's had poor growth in their weight trajectories. Results show that gender, socio-economic status, parent's education, breast feeding, and gravida are associated and, influence the growth pattern in children.
The FPC approach deals with subjects' dynamics of growth and not with specific values at given times. FPC could be a better alternate approach for both dimension reduction and pattern detection. FPC may be used to offer greater insight for classification.
了解儿童的生长模式对于纵向研究很重要，但在印度，此类研究却很有限。因此，我们需要确定一种方法来描述生长轨迹，以区分生长健康和生长不良的儿童。有许多统计方法可用于评估纵向生长数据，但这些方法很难识别模式。在这项研究中，我们采用了功能主成分分析（FPCA）作为一种统计方法来寻找生长数据的模式。本研究的目的是使用功能主成分法描述 3 岁以下儿童的纵向生长轨迹模式。
对 2002 年 3 月至 2003 年 8 月间出生的 290 名儿童进行了随访，随访时间直到他们 3 岁生日，这些儿童均来自印度南部维洛尔的三个相邻贫民窟。现场工作人员每两周家访一次，以收集疾病的详细信息。从出生后 1 个月起，在研究诊所每月测量一次身高和体重。使用 B 样条基函数和平滑参数的功能主成分分析提取纵向儿童生长轨迹模式。使用功能线性模型评估与生长函数相关的因素。
我们获得了四个功能主成分，分别解释了身高函数的 86.5%、3.9%、3.1%和 2.2%的变异。对于身高，38%的儿童的生长轨迹较差。同样，体重函数也有三个功能主成分，分别解释了 76.2%、8.8%和 4.7%的变异，44%的儿童的体重生长轨迹较差。结果表明，性别、社会经济地位、父母教育程度、母乳喂养和胎次与儿童的生长模式有关，并对其产生影响。
FPC 方法处理的是研究对象的生长动态，而不是特定时间的具体值。FPC 可能是一种更好的替代方法，可用于降维和模式检测。FPC 可用于提供更好的分类洞察力。
