scholarly journals Studi Longitudinal Pada Analisis Data Gula Darah Pasien Diabetes melalui Principal Component Analysis

2022 ◽  
Vol 4 (1) ◽  
pp. 41-49
Author(s):  
Anna Islamiyati ◽  
Sitti Sahriman ◽  
Sakinah Oktoni

Multicollinearity is a relationship or correlation between predictor variables. Multicollinearity can also occur in longitudinal data, which is a combination of cross-section data and time-series data. The impact of multicollinearity causes the influence of the predictor variable on the response variable to be insignificant, the least-squares estimator, and the error to be sensitive to changes in the data. Therefore, the procedure to overcome multicollinearity uses the principal component analysis method. This study aims to model PCA longitudinal data regression with a fixed-effect model that is applied to blood sugar data of diabetic patients with a time span of January 2019 to July 2019 at Ibnu Sina Hospital Makassar City. The results of this study indicate that there are two main components formed from PCA longitudinal data regression modelling with a fixed-effect model. Obtained variable values are systolic blood pressure of -0.007, diastolic blood pressure of -0,016, the body temperature of -0.098, and platelets of 0.005 which affect blood sugar in patients with diabetes.

2020 ◽  
Vol 2 (2) ◽  
pp. 115
Author(s):  
Syafruddin Side ◽  
S. Sukarna ◽  
Raihana Nurfitrah

Penelitian ini membahas mengenai estimasi parameter model regresi data panel pada pemodelan tingkat kematian bayi di Provinsi Sulawesi Selatan dari tahun 2014 sampai dengan 2015. Data yang digunakan adalah data sekunder dari Dinas Kesehatan Provinsi Sulawesi Selatan yang berupa jumlah kematian bayi, berat bayi lahir rendah, persalinan yang ditolong oleh tenaga kesehatan, penduduk miskin, bayi yang diberi ASI ekslusif dan rumah tangga berperilaku bersih sehat di seluruh Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2014-2016. Analisis data dilakukan dengan menggunakan penghitungan manual dan dengan menggunakan software EViews 9. Pembahasan dimulai dari melakukan estimasi parameter model regresi data panel, menentukan model regresi data panel terbaik, , menguji asumsi model regresi data panel, pengujian signifikansi parameter dan interpretasi model regresi. Dalam penelitian ini diperoleh kesimpulan yaitu estimasi model regresi data panel terbaik dengan pendekatan fixed effect model.Kata kunci:Regresi Data Panel, Kematian Bayi, Fixed Effect Model, Least Square Dummy Variable. This research discusses about parameter estimation of panel data regression model of infant mortality level modelling in South Sulawesi from 2014 to 2015. The data used were secondary data from Dinas Kesehatan Provinsi Sulawesi Selatan in the form of number of infant mortality, low weight of infant, childbirth rescued by health workers, poor population, infants who were given exclusive breast milk and household that behaves well in the whole district/town in South Sulawesi year 2014-2016. Data analysis was performed using the calculation manually and by using EViews 9 software. The discussion started from doing parameter estimation of panel data regression model, determining the best panel data regression model, testing the assumption of panel data regression model, testing the signification of parameter and interpretation of regression model. Conclusion of this research are the estimation of regression model is the best panel data regression model with fixed effects model approach.Keywords:Panel Data Regression, Infant Mortality, Fixed Effect Model, Least Square Dummy Variable.


2020 ◽  
Vol 9 (3) ◽  
pp. 355-363
Author(s):  
Artanti Indrasetianingsih ◽  
Tutik Khalimatul Wasik

Poverty arises when a person or group of people is unable to meet the level of economic prosperity which is considered a minimum requirement of a certain standard of living or poverty is understood as a state of lack of money and goods to ensure survival. Panel data regression is the development of regression analysis which is a combination of time series data and cross section data. Panel data regression is usually used to make observations of data that is examined continuously for several periods. The purpose of this study is to determine the factors that influence the level of poverty in Madura Island in the period 2008 - 2017. In this study the variables used in this study are life expectancy (X1), average length of school (X2), level open unemployment (X3), and labor force participation (X4) with the Comman Effect Model (CEM) approach, Fixed Effect Model and Random Effect Model (REM). To choose the best model from the three is the chow test, the hausman test and the breusch-pagan test. In this study, the best model chosen was the Fixed Effect Model. Keywords: CEM, Fixed Effect Model, Data Panel Regression, REM, Poverty level.


2018 ◽  
Vol 28 (4) ◽  
pp. 1216-1229
Author(s):  
Xiao Lin ◽  
Ruosha Li ◽  
Fangrong Yan ◽  
Tao Lu ◽  
Xuelin Huang

Optimal therapeutic decisions can be made according to disease prognosis, where the residual lifetime is extensively used because of its straightforward interpretation and formula. To predict the residual lifetime in a dynamic manner, a longitudinal biomarker that is repeatedly measured during the post-baseline follow-up period should be included. In this article, we use functional principal component analysis, a powerful and flexible tool, to handle irregularly measured longitudinal data and extract the dominant features over a specific time interval. To capture the time-dependent trajectory pattern, a series of moving time windows are used to estimate window-specific functional principal component analysis scores, which are then combined with a quantile residual lifetime regression model to facilitate dynamic prediction. Estimation of this regression model can be achieved by solving estimating equations with the help of locating the minimizer of the L1-type function. Simulation studies demonstrate the advantages of our proposed method in both calibration and discrimination under various scenarios. The proposed method is applied to data from patients with chronic myeloid leukemia to illustrate its practicality, where we dynamically predict quantile residual lifetimes with longitudinal expression levels of an oncogene, BCR-ABL.


2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
O Greaves ◽  
S L Harrison ◽  
D A Lane ◽  
M Banach ◽  
M Mastej ◽  
...  

Abstract Background The National Health Service in England “Long Term Plan” aims to prevent 150,000 strokes and myocardial infarctions over the next 10 years. To achieve this, resources are being allocated to improve early detection of conditions strongly associated with cardiovascular disease. This includes working towards people routinely knowing their “ABC” risk factors (“A”: atrial fibrillation (AF), “B': hypertension and “C”: high cholesterol) (1). Purpose The aims of this study were to: 1) determine the proportion of participants with “A”, “B”, and “C” criteria; and 2) to identify risk factors for patients fulfilling any of these criteria. Methods LIPIDOGRAM2015 was a nationwide cross-sectional survey for adults in Poland. Adults were recruited in 2015 and 2016 by 438 family physicians. For the ABC criteria, “A” was defined as AF identified in the medical records of the participant, “B” was defined as either systolic blood pressure greater than 140mmHg or diastolic blood pressure greater than 90mmHg or both, and “C” was defined as total cholesterol greater than 200mg/dL (5.17mmol/L). The scaled and centred dataset underwent principal component analysis using singular value decomposition to achieve dimensionality reduction. K-means clustering was used to stratify patients with Hartigan's rule being used to identify optimal K number (2–4). The p-value for statistical significance used in this study was p<0.01 unless otherwise specified. Results 13,724 patients were included in the study. 71.0% (n=9,747) of participants fulfilled the criteria for one or more of the “A”, “B” or “C” components (Fig. 1). 26 variables were used in this analysis with Principal Component Analysis showing 7 principal components explaining over 50% of the variance with 20 components explaining over 90%. K-means clustering was also performed, finding 39 separate clusters. Correlations and statistical significance tests showed a high degree of variability between variables. Participants with AF were older (mean (SD) 67.7 (9.5) vs 55.7 (13.7), p<0.0001), with higher prevalence of concomitant coronary heart disease (CHD) (OR 6.73, 95% CL 5.75, 7.87) and ischaemic stroke (OR 13.45, 95% CL 7.66, 23.6). Participants with hypertension were older (mean (SD) 60.1 (SD 12.4) vs 53.8 (14.0), p<0.0001), with a higher BMI (mean (SD) 29.9 (5.1) vs 27.5 (4.8), p<0.0001) and resting heart rate (mean (SD) 75.7 (10.7) vs 72.7 (8.9), p<0.0001), more likely to be male (OR 1.42, 95% CL 1.32, 1.53) and have diabetes (OR 1.61, 95% CL 1.46, 1.78). Participants with high cholesterol showed an inverse correlation with prevalence of both concomitant diabetes (OR 0.85, 95% CL 0.77, 0.94) and CHD (OR 0.85, 95% CL 0.76, 0.94) (Fig. 2). Conclusion Simple demographic and clinical variables could be used to guide targeted screening to increase population awareness of “ABC” status, allowing for a greater proportion of the population to be appropriately managed with cardiovascular prevention strategies. FUNDunding Acknowledgement Type of funding sources: None. “ABC” Venn diagram Correlogram and significance plot


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