Nonlinear growth curve modeling using penalized spline models: A gentle introduction.

2019 ◽  
Vol 24 (3) ◽  
pp. 269-290 ◽  
Author(s):  
Hye Won Suk ◽  
Stephen G. West ◽  
Kimberly L. Fine ◽  
Kevin J. Grimm
Keyword(s):  
Growth Curve ◽  
Growth Curve Modeling ◽  
Nonlinear Growth ◽  
Penalized Spline ◽  
Spline Models ◽  
Curve Modeling

Perbandingan Linieritas Dalam Pendeteksian Faktor-Faktor Yang Mempengaruhi Angka Kematian Bayi Di Provinsi Aceh

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Desfira Ahya ◽  
Inas Salsabila ◽  
Miftahuddin
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Linear Model ◽  
Health Workers ◽  
Additive Model ◽  
Infant Mortality Rate ◽  
Mortality Data ◽  
Important Indicator ◽  
Penalized Spline ◽  
Aceh Province

Angka Kematian Bayi/ Infant Mortality Rate (IMR) merupakan indikator penting dalam mengukur keberhasilan pengembangan kesehatan. Nilai IMR juga dapat digunakan untuk mengetahui tingkat kesehatan ibu, kondisi kesehatan lingkungan dan secara umum, tingkat pengembangan sosio-ekonomi masyarakat. Penelitian ini bertujuan untuk memperoleh model IMR terbaik menggunakan tiga pendekatan: Model Linear, Model Linear Tergeneralisir dan Model Aditif Tergeneralisir dengan basis P-spline. Sebagai tambahan, berdasarkan model tersebut akan terlihat variabel yang mempengaruhi tingkat kematian bayi di provinsi Aceh. Penelitian ini menggunakan data jumlah kematian bayi di tahun 2013-2015. Data dalam penelitian ini diperoleh dari Profil Kesehatan Aceh. Hasil menunjukkan bahwa model terbaik dalam menjelaskan angka kematian bayi di provinsi Aceh tahun 2013-2015 ialah Model Linear Tergeneralisir dengan basis P-spline menggunakan parameter penghalusan 100 dan titik knots 8. Faktor yang sangat mempengaruhi angka kematian ialah jumlah pekerja yang sehat.   Infant mortality rate (IMR) is an important indicator in measuring the success of health development. IMR also can be used to knowing the level of maternal health, environmental health conditions and generally the level of socio-economic development in community. This research aims to get the best model of infant mortality data using three approaches: Linear Model, Generalized Linear Model and Generalized Additive Model with Penalized Spline (P-spline) base. In addition, based on the model can be seen the variables that affect to infant mortality in Aceh Province. This research uses data number of infant mortality in Aceh Province period 2013-2015. The data in this research were obtained from Aceh’s Health Profile. The results show that the best model can be explain infant mortality rate in Aceh Province period 2013-2015 is GAM model with P-spline base using smoothing parameter 100 and knots 8. Factor that high effect to infant mortality is number of health workers.

scholarly journals Baseline Low-Density-Lipoprotein Cholesterol Modifies the Risk of All-Cause Death Associated With Elevated Lipoprotein(a) in Coronary Artery Disease Patients

2022 ◽  
Vol 8 ◽  
Author(s):  
Younan Yao ◽  
Jin Liu ◽  
Bo Wang ◽  
Ziyou Zhou ◽  
Xiaozhao Lu ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol ◽  
Penalized Spline ◽  
All Cause Mortality ◽  
Artery Disease

Background: The prognostic value of elevated lipoprotein(a) [Lp(a)] in coronary artery disease (CAD) patients is inconsistent in previous studies, and whether such value changes at different low-density-lipoprotein cholesterol (LDL-C) levels is unclear.Methods and Findings: CAD patients treated with statin therapy from January 2007 to December 2018 in the Guangdong Provincial People's Hospital (NCT04407936) were consecutively enrolled. Individuals were categorized according to the baseline LDL-C at cut-off of 70 and 100 mg/dL. The primary outcome was 5-year all-cause death. Multivariate Cox proportional models and penalized spline analyses were used to evaluate the association between Lp(a) and all-cause mortality. Among 30,908 patients, the mean age was 63.1 ± 10.7 years, and 76.7% were men. A total of 2,383 (7.7%) patients died at 5-year follow-up. Compared with Lp(a) <50 mg/dL, Lp(a) ≥ 50 mg/dL predicted higher all-cause mortality (multivariable adjusted HR = 1.19, 95% CI 1.07–1.31) in the total cohort. However, when analyzed within each LDL-C category, there was no significant association between Lp(a) ≥ 50 mg/dL and higher all-cause mortality unless the baseline LDL-C was ≥ 100 mg/dL (HR = 1.19, 95% CI 1.04–1.36). The results from penalized spline analyses were robust.Conclusions: In statin-treated CAD patients, elevated Lp(a) was associated with increased risks of all-cause death, and such an association was modified by the baseline LDL-C levels. Patients with Lp(a) ≥ 50 mg/dL had higher long-term risks of all-cause death compared with those with Lp(a) <50 mg/dL only when their baseline LDL-C was ≥ 100 mg/dL.

scholarly journals ESTIMASI PARAMETER REGRESI SPLINE DENGAN METODE PENALIZED SPLINE

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Wahyu Kurniasari, Dadan Kusnandar, Evy Sulistianingsih
Keyword(s):  
Cross Validation ◽  
Generalized Cross Validation ◽  
Penalized Spline

Regresi spline merupakan suatu pendekatan ke arah pencocokan data dengan tetap memperhitungkan kemulusan kurva. Salah satu bentuk estimator dari regresi spline ialah penalized spline. Tujuan dari penelitian ini adalah untuk mengestimasi parameter regresi spline dengan metode penalized spline untuk data yang tidak memiliki pola tertentu. Data penelitian ini menggunakan data sekunder yang diperoleh dari Badan Pusat Statistik Indonesia pada tahun 2015 yaitu indeks pembangunan manusia, gini rasio, harapan lama sekolah, penduduk miskin, dan kepadatan penduduk. Hasil regresi spline yang diperoleh untuk model terbaik yaitu model spline linier pada setiap variabel dengan nilai Generalized Cross Validation (GCV) minimum. Hasil penelitian menunjukkan bahwa regresi spline dengan metode penalized spline menghasilkan estimasi parameter yang signifikan dan memperoleh nilai koefisien determinasi terkoreksi  sebesar 76,66% serta nilai MAPE untuk model regresi spline sebesar 1,415%. Kata Kunci: regresi nonparametrik, regresi spline, penalized spline.

‘Asymptotic properties of penalized spline estimators’

Biometrika ◽  
2018 ◽  
Vol 105 (2) ◽  
pp. 503-503 ◽  
Author(s):  
G Claeskens ◽  
T Krivobokova ◽  
J D Opsomer
Keyword(s):  
Asymptotic Properties ◽  
Penalized Spline
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