Z-score standard growth chart design of toddler weight using least square spline semiparametric regression

2021 ◽  
Author(s):  
Nur Chamidah ◽  
Budi Lestari ◽  
Anies Y. Wulandari ◽  
Lailatul Muniroh
Keyword(s):  
Semiparametric Regression ◽  
Least Square ◽  
Growth Chart ◽  
Z Score ◽  
Chart Design

Growth chart design

1988 ◽  
Vol 55 (S1) ◽  
pp. S26-S29 ◽  
Author(s):  
John O’Malley Burns ◽  
Rolf C. Carriere ◽  
Jon E Rohde
Keyword(s):  
Growth Chart ◽  
Chart Design

scholarly journals Estimation of Heteroskedasticity Semiparametric Regression Curve Using Fourier Series Approach

2020 ◽  
Vol 2 (1) ◽  
pp. 14-20
Author(s):  
Rahmawati Pane ◽  
Sutarman
Keyword(s):  
Fourier Series ◽  
Regression Model ◽  
Variable Parameter ◽  
Predictor Variable ◽  
Semiparametric Regression ◽  
Least Square ◽  
Parameter Vector ◽  
Regression Curve ◽  
Semiparametric Regression Model ◽  
Main Components

A heteroskedastic semiparametric regression model consists of two main components, i.e. parametric component and nonparametric component. The model assumes that any data (x̰ i′ , t i , y i ) follows y i = x̰ i′ β̰+ f(t i ) + σ i ε i , where i = 1,2, … , n , x̰ i′ = (1, x i1 , x i2 , … , x ir ) and t i is the predictor variable. Parameter vector β̰ = (β 1 , β 2 , … , β r ) ′ ∈ ℜ r is unknown and f(t i ) is also unknown and is assumed to be in interval of C[0,π] . Random error ε i is independent on zero mean and varianceσ 2 . Estimation of the heteroskedastic semiparametric regression model was conducted to evaluate the parametric and nonparametric components. The nonparametric component f(t i ) regression was approximated by Fourier series F(t) = bt + 12 α 0 + ∑ α k 𝑐 𝑜𝑠 kt Kk=1 . The estimation was obtained by means of Weighted Penalized Least Square (WPLS): min f∈C(0,π) {n −1 (y̰− Xβ̰−f̰) ′ W −1 (y̰− Xβ̰− f̰) + λ ∫ 2π [f ′′ (t)] 2 dt π0 } . The WPLS solution provided nonparametric component f̰̂ λ (t) = M(λ)y̰ ∗ for a matrix M(λ) and parametric component β̰̂ = [X ′ T(λ)X] −1 X ′ T(λ)y̰

scholarly journals Perbedaan arus puncak ekspirasi antara anak asma dengan obesitas dan anak asma tanpa obesitas

2013 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Nurul Hadi ◽  
Madarina Julia ◽  
Roni Naning
Keyword(s):  
Peak Flow ◽  
Pearson Correlation ◽  
Cross Sectional Study ◽  
Growth Chart ◽  
Z Score ◽  
Flow Meter ◽  
Cross Sectional ◽  
Asthmatic Children ◽  
Increased Risk ◽  
Significant Difference

Background: Obesity in children is associated with impairment of pulmonary function and increased risk of asthma. Obesity in asthmatic children may reduce lung function, that can be assessed by peak flow meter, a practical and an inexpensive tool.Objectives: To compare the peak expiratory flow (PEF) between obese and non-obese asthmatic children.Method: We conducted a cross sectional study in Yogyakarta during March 2010-September 2012. Fifty obese asthmatic patients and 50 non obese asthmatic control subjects participated in this study. Inclusion criteria were asthmatic patient, according to Pedoman Nasional Asma Anak (PNAA), and 6-18 years of age. Exclusion criteria were asthmatic attack, respiratory disease, heart disease and congenital chest malformation. Obesity is defined as body mass index (BMI) for age more than +3 SD WHO growth chart standards BMI for age 2007 z-score. Z-score is calculated with WHO AnthroPlus for Personal Computers. Data PEF is taken with electrical peak flow meter when the patient was not suffering from asthma attack. Normal PEF was defined as PEF ≥80% average (predicted) value for height.Results: The mean of age of asthmatic children in this study was 9.38 years and 9.50 years for non obese and obese respectively. The PFR was not different between obese asthmatic children and non obese asthmatic children (p=0,83). Pearson correlation of PFR and z-score BMI for age was positive weak correlation (r=0.12). There was significant difference of PFR between z-score BMI for age <3,20 and z-score BMI for age ≥3.20 (p=0.03). Significant difference of PFR also appears in duration of illness (p<0.001).Conclusion: There is no PFR difference between obese asthmatic children and non-obese asthmatic children. The difference of PFR emerges when statistic analysis performed using z-score BMI ≥3.20.

scholarly journals PediTools Electronic Growth Chart Calculators: Applications in Clinical Care, Research, and Quality Improvement

10.2196/16204 ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. e16204 ◽  
Author(s):  
Joseph H Chou ◽  
Sergei Roumiantsev ◽  
Rachana Singh
Keyword(s):  
Quality Improvement ◽  
Gestational Age ◽  
Anthropometric Measurement ◽  
Growth Chart ◽  
Growth Charts ◽  
Z Score ◽  
Growth Outcomes ◽  
Retrospective Clinical Study ◽  
Gestational Age At Birth ◽  
Age At Birth

Background Parameterization of pediatric growth charts allows precise quantitation of growth metrics that would be difficult or impossible with traditional paper charts. However, limited availability of growth chart calculators for use by clinicians and clinical researchers currently restricts broader application. Objective The aim of this study was to assess the deployment of electronic calculators for growth charts using the lambda-mu-sigma (LMS) parameterization method, with examples of their utilization for patient care delivery, clinical research, and quality improvement projects. Methods The publicly accessible PediTools website of clinical calculators was developed to allow LMS-based calculations on anthropometric measurements of individual patients. Similar calculations were applied in a retrospective study of a population of patients from 7 Massachusetts neonatal intensive care units (NICUs) to compare interhospital growth outcomes (change in weight Z-score from birth to discharge [∆Z weight]) and their association with gestational age at birth. At 1 hospital, a bundle of quality improvement interventions targeting improved growth was implemented, and the outcomes were assessed prospectively via monitoring of ∆Z weight pre- and postintervention. Results The PediTools website was launched in January 2012, and as of June 2019, it received over 500,000 page views per month, with users from over 21 countries. A retrospective analysis of 7975 patients at 7 Massachusetts NICUs, born between 2006 and 2011, at 23 to 34 completed weeks gestation identified an overall ∆Z weight from birth to discharge of –0.81 (P<.001). However, the degree of ∆Z weight differed significantly by hospital, ranging from –0.56 to –1.05 (P<.001). Also identified was the association between inferior growth outcomes and lower gestational age at birth, as well as that the degree of association between ∆Z weight and gestation at birth also differed by hospital. At 1 hospital, implementing a bundle of interventions targeting growth resulted in a significant and sustained reduction in loss of weight Z-score from birth to discharge. Conclusions LMS-based anthropometric measurement calculation tools on a public website have been widely utilized. Application in a retrospective clinical study on a large dataset demonstrated inferior growth at lower gestational age and interhospital variation in growth outcomes. Change in weight Z-score has potential utility as an outcome measure for monitoring clinical quality improvement. We also announce the release of open-source computer code written in R to allow other clinicians and clinical researchers to easily perform similar analyses.

Growth chart design

1989 ◽  
Vol 56 (4) ◽  
pp. 555-555
Author(s):  
Daljlt Singh ◽  
Tejinder Singh
Keyword(s):  
Growth Chart ◽  
Chart Design

scholarly journals PEMODELAN JUB DAN BI RATE TERHADAP INFLASI DAN KURS RUPIAH MENGGUNAKAN REGRESI SEMIPARAMETRIK BIRESPON BERDASARKAN ESTIMATOR PENALIZED SPLINE

2020 ◽  
Vol 9 (2) ◽  
pp. 204-216
Author(s):  
Siti Fadhilla Femadiyanti ◽  
Suparti Suparti ◽  
Budi Warsito
Keyword(s):  
Exchange Rate ◽  
Money Supply ◽  
Semiparametric Regression ◽  
Least Square ◽  
Penalized Spline ◽  
R Program ◽  
Us Dollar ◽  
The Exchange Rate ◽  
The Right ◽  
The Relationship

Some indicators of the Indonesian economy are inflation and the exchange rate of rupiah against US dollar. Inflation and the rupiah exchange rate are thought to be influenced by the money supply (JUB) and the BI Rate. The money supply has a nonparametric relationship pattern to inflation and the rupiah exchange rate, while the BI Rate has a parametric relationship pattern  to inflation and the rupiah exchange rate. The right method for detecting the relationship between inflation and the exchange rate with JUB and BI Rate is birespon semiparametric regression with a splined penalized estimator. The semiparametric regression coefficient of birespon spline penalized is estimated using the Weighted Least square (WLS) method which is determined based on the degree of polynomials, the number and location of the optimal knot points, and the optimal lambda determined based on the minimum of Generalized Cross Validation (GCV). This research uses the R Program. Based on the results of the analysis, the best spline penalized birespon semiparametric regression model is located in the number of knots is 5 at the knot points of 5257,783; 6649,469; 8976,871; 11099,19 and 13535,51 found in the first degree of response is 1 and the second degree of response is 2 with an optimal lambda of 99,99. The results of the performance evaluation of the model produce value of  is 99,9007%, meaning that the model's performance is very good for out samples of the data and the MAPE value of 2.89169% is less than 10% which means the model's performance is very good.  

scholarly journals PediTools Electronic Growth Chart Calculators: Applications in Clinical Care, Research, and Quality Improvement (Preprint)

2019 ◽  
Author(s):  
Joseph H Chou ◽  
Sergei Roumiantsev ◽  
Rachana Singh
Keyword(s):  
Quality Improvement ◽  
Gestational Age ◽  
Anthropometric Measurement ◽  
Growth Chart ◽  
Growth Charts ◽  
Z Score ◽  
Growth Outcomes ◽  
Retrospective Clinical Study ◽  
Gestational Age At Birth ◽  
Age At Birth

BACKGROUND Parameterization of pediatric growth charts allows precise quantitation of growth metrics that would be difficult or impossible with traditional paper charts. However, limited availability of growth chart calculators for use by clinicians and clinical researchers currently restricts broader application. OBJECTIVE The aim of this study was to assess the deployment of electronic calculators for growth charts using the lambda-mu-sigma (LMS) parameterization method, with examples of their utilization for patient care delivery, clinical research, and quality improvement projects. METHODS The publicly accessible PediTools website of clinical calculators was developed to allow LMS-based calculations on anthropometric measurements of individual patients. Similar calculations were applied in a retrospective study of a population of patients from 7 Massachusetts neonatal intensive care units (NICUs) to compare interhospital growth outcomes (change in weight Z-score from birth to discharge [∆Z weight]) and their association with gestational age at birth. At 1 hospital, a bundle of quality improvement interventions targeting improved growth was implemented, and the outcomes were assessed prospectively via monitoring of ∆Z weight pre- and postintervention. RESULTS The PediTools website was launched in January 2012, and as of June 2019, it received over 500,000 page views per month, with users from over 21 countries. A retrospective analysis of 7975 patients at 7 Massachusetts NICUs, born between 2006 and 2011, at 23 to 34 completed weeks gestation identified an overall ∆Z weight from birth to discharge of –0.81 (<i>P</i>&lt;.001). However, the degree of ∆Z weight differed significantly by hospital, ranging from –0.56 to –1.05 (<i>P</i>&lt;.001). Also identified was the association between inferior growth outcomes and lower gestational age at birth, as well as that the degree of association between ∆Z weight and gestation at birth also differed by hospital. At 1 hospital, implementing a bundle of interventions targeting growth resulted in a significant and sustained reduction in loss of weight Z-score from birth to discharge. CONCLUSIONS LMS-based anthropometric measurement calculation tools on a public website have been widely utilized. Application in a retrospective clinical study on a large dataset demonstrated inferior growth at lower gestational age and interhospital variation in growth outcomes. Change in weight Z-score has potential utility as an outcome measure for monitoring clinical quality improvement. We also announce the release of open-source computer code written in R to allow other clinicians and clinical researchers to easily perform similar analyses.

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