Functional analysis of glaucoma data

Hosseini‐Nasab, Mohammad; K., Zahra Mirzaei

doi:10.1002/sim.6061

Cited by 8 publications

(8 citation statements)

References 56 publications

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“…In the present study, total variation was explained ≥90% for height and weight respectively. In literature review, application of FPC analysis are reported more than 80% of the total variation in multiple studies [ 9 , 34 , 38 , 39 ]. In fact, we know that the growth outcome measurement obtained every month for 3 years exhibits 40% of the children had poor growth in the slum area, Vellore.…”

Section: Discussionmentioning

confidence: 99%

Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data

Karuppusami

Antonisamy

Premkumar

2022

BMC Med Res Methodol

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Background 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. Methods 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. Results 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. Conclusions 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.

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Section: Discussionmentioning

confidence: 99%

Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data

Karuppusami

Antonisamy

Premkumar

2022

BMC Med Res Methodol

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Section: Discussionmentioning

confidence: 99%

“…The advanced statistical procedure of Functional Principal Component Analysis was used to extract the information from functional data to identify the different pattern of the child growth function and further functional linear model was used to assess the association between factors and trajectories [13,15,16,18,[23][24][25][26][27]. The statistical analysis was performed using R studio version 3.6.1.…”

Section: Smoothing Outlying Function and Fpcamentioning

confidence: 99%

Identifying longitudinal child growth curve pattern using Functional Principal Component Analysis from birth cohort study in South India

Karuppusami¹,

Antonisamy²,

Premkumar³

2021

Preprint

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Background: Human growth studies has long been of interest to researchers and health authorities. Predominantly, the study of physical growth in children is a challenging and complex issue. The goal of a variety of studies from across the world is to develop overall health and well-being in children. It is therefore important that we need to identify an accurate and reliable 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. The purpose of this study is to identify the longitudinal child growth trajectory pattern and factors association on the growth function using an advanced statistical technique. Methods: This longitudinal birth cohort study (n=290) was conducted in three nearby urban slums communities in Vellore, South India. Pregnant women were identified during a survey conducted in 2002 and infants were recruited from birth between the period of March 2002 and August 2003 following written informed consent from the mother. Growth outcomes of height and weight measurements were recorded for each child continuously in first 36 months. Functional Principal Component Analysis was used to classify the longitudinal child growth trajectory pattern. Functional linear model was used to assess the factors association with the growth functions. Results: We have obtained four functional principal components explained by 86.5%, 3.9%, 3.1% and 2.2% of the variation respectively for the height functions. 38% of the children’s had poor growth trajectories in height. Similarly, we have obtained three functional principal components explained 76.2%, 8.8%, and 4.7% respectively for the weight functions. 44% of the children’s had poor growth in their weight trajectories. The study shows that gender, socio-economic status, parent’s education, breast feeding, and gravida are associated and, influence the growth pattern in children. Conclusions: The advanced FPC approach deals with subjects’ dynamics of growth and not with specific values at given times. FPCA may be a better alternative approach in the sense of both dimension reduction and pattern detection. FPCA may be used to offer greater insight for classification.

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“…This was in part due to the known clinical utility of some clinical parameters for the diagnosis of glaucoma, compared with others that are comparatively low yield. The principal components in this study represent clinical variables that may be correlated in their contributions for explaining differences between high‐ and low‐tension glaucoma 23,36–38 …”

Section: Methodsmentioning

confidence: 99%

Visualising structural and functional characteristics distinguishing between newly diagnosed high‐tension and low‐tension glaucoma patients

Rafla

Khuu

Kashyap

et al. 2023

Ophthalmic Physiologic Optic

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Purpose To determine whether there are quantifiable structural or functional differences that can distinguish between high‐tension glaucoma (HTG; intraocular pressure [IOP] > 21 mm Hg) and low‐tension glaucoma (LTG; IOP ≤ 21 mm Hg) at diagnosis. Method This was a retrospective, cross‐sectional study. Clinical results of one eye from 90 newly diagnosed HTG and 319 newly diagnosed LTG patients (117 with very‐low‐tension glaucoma [vLTG; ≤15 mm Hg] and 202 with middling LTG [mLTG; >15 mm Hg, ≤21 mm Hg]) were extracted, which included relevant demographic covariates of glaucoma, quantitative optical coherence tomography (including the optic nerve head, retinal nerve fibre layer and ganglion cell‐inner plexiform layer) measurements and standard automated perimetry global metrics. We used binary logistic regression analysis to identify statistically significant clinical parameters distinguishing between phenotypic groups for inclusion in principal component (PC) (factor) analysis (PCA). The separability between each centroid for each cohort was calculated using the Euclidean distance (d(x,y)). Results The binary logistic regression comparing HTG and all LTG identified eight statistically significant clinical parameters. Subsequent PCA results included three PCs with an eigenvalue >1. PCs 1 and 2 accounted for 21.2% and 20.2% of the model, respectively, with a d(x,y) = 0.468, indicating low separability between HTG and LTG. The analysis comparing vLTG, mLTG and HTG identified 15 significant clinical parameters, which were subsequently grouped into five PCs. PCs 1 and 2 accounted for 24.1% and 17.8%, respectively. The largest separation was observed between vLTG and HTG (d(x,y) = 0.581), followed by vLTG and mLTG (d(x,y) = 0.435) and lastly mLTG and HTG (d(x,y) = 0.210). Conclusion Conventional quantitative structural or functional parameters could not distinguish between pressure‐defined glaucoma phenotypes at the point of diagnosis and are therefore not contributory to separating cohorts. The overlap in findings highlights the heterogeneity of the primary open‐angle glaucoma clinical presentations among pressure‐defined groups at the cohort level.

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Functional analysis of glaucoma data

Cited by 8 publications

References 56 publications

Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data

Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data

Identifying longitudinal child growth curve pattern using Functional Principal Component Analysis from birth cohort study in South India

Visualising structural and functional characteristics distinguishing between newly diagnosed high‐tension and low‐tension glaucoma patients

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