scholarly journals A Comparison of Confidence Interval Methods of Fixed Effect in Nested Error Regression Model

2016 ◽  
Vol 20 (2) ◽  
Author(s):  
Hatice Tül Kübra AKDUR ◽  
Deniz ÖZONUR ◽  
Hülya BAYRAK
Keyword(s):  
Confidence Interval ◽  
Regression Model ◽  
Fixed Effect ◽  
Interval Methods

scholarly journals Prediction of Hospital Mortality Rates by Admission Laboratory Tests

2006 ◽  
Vol 52 (2) ◽  
pp. 325-328 ◽  
Author(s):  
Paul Froom ◽  
Zvi Shimoni
Keyword(s):  
Internal Medicine ◽  
Logistic Regression ◽  
Confidence Interval ◽  
Regression Model ◽  
Odds Ratio ◽  
Laboratory Tests ◽  
Logistic Regression Model ◽  
Complete Blood Count ◽  
Laboratory Data ◽  
Regional Hospital

Abstract Background: The aim of this study was to explore whether electronically retrieved laboratory data can predict mortality in internal medicine departments in a regional hospital. Methods: All 10 308 patients hospitalized in internal medicine departments over a 1-year period were included in the cohort. Nearly all patients had a complete blood count and basic clinical chemistries on admission. We used logistic regression analysis to predict the 573 deaths (5.6%), including all variables that added significantly to the model. Results: Eight laboratory variables and age significantly and independently contributed to a logistic regression model (area under the ROC curve, 88.7%). The odds ratio for the final model per quartile of risk was 6.44 (95% confidence interval, 5.42–7.64), whereas for age alone, the odds ratio per quartile was 2.01 (95% confidence interval, 1.84–2.19). Conclusions: A logistic regression model including only age and electronically retrieved laboratory data highly predicted mortality in internal medicine departments in a regional hospital, suggesting that age and routine admission laboratory tests might be used to ensure a fair comparison when using mortality monitoring for hospital quality control.

scholarly journals Risk of Infective Endocarditis After Invasive Dental Treatments

Circulation ◽  
2018 ◽  
Vol 138 (4) ◽  
pp. 356-363 ◽  
Author(s):  
Tzu-Ting Chen ◽  
Yi-Chun Yeh ◽  
Kuo-Liong Chien ◽  
Mei-Shu Lai ◽  
Yu-Kang Tu
Keyword(s):  
Infective Endocarditis ◽  
Confidence Interval ◽  
Regression Model ◽  
Conditional Logistic Regression ◽  
Case Series ◽  
Crossover Design ◽  
The Self ◽  
Series Design ◽  
Case Crossover ◽  
Study Designs

Background: Invasive dental treatments (IDTs) can yield temporary bacteremia and have therefore been considered a potential risk factor of infective endocarditis (IE). It is hypothesized that, through the trauma caused by IDTs, bacteria gain entry to the bloodstream and may attach to abnormal heart valves or damaged heart tissue, giving rise to IE. However, the association between IDTs and IE remains controversial. The aim of this study is to estimate the association between IDTs and IE. Methods: The data in this study were obtained from the Health Insurance Database in Taiwan. We selected 2 case-only study designs, case-crossover and self-controlled case series, to analyze the data. The advantage of these methods is that confounding factors that do not vary with time are adjusted for implicitly. In the case-crossover design, a conditional logistic regression model with exposure to IDTs was used to estimate the risks of IE following an IDT with 4, 8, 12, and 16 weeks delay, respectively. In the self-controlled case series design, a conditional Poisson regression model was used to estimate the risk of IE for the risk periods of 1 to 4, 5 to 8, 9 to 12, and 13 to 16 weeks following an IDT. Results: In total, 9120 and 8181 patients with IE were included in case-crossover design and self-controlled case series design, respectively. In the case-crossover design, 277 cases and 249 controls received IDTs during the exposure period, and the odds ratio was 1.12 (95% confidence interval, 0.94–1.34) for 4 weeks. In the self-controlled case series design, we observed that 407 IEs occurred during the first 4 weeks after IDTs, and the age-adjusted incidence rate ratio was 1.14 (95% confidence interval, 1.02–1.26) for 1 to 4 weeks after IDTs. Conclusions: In both study designs, we did not observe a clinically larger risk for IE in the short periods after IDTs. We also found no association between IDTs and IE among patients with a high risk of IE. Therefore, antibiotic prophylaxis for the prevention of IE is not required for the Taiwanese population.

Abstract 13468: The Interaction Between the Prognostic Value of Chronotropic Incompetence and Cardiorespiratory Fitness: The Henry Ford Exercise Testing (FIT) Project

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Waqas T Qureshi ◽  
Wesley T O'Neal ◽  
Clinton A Brawner ◽  
Jonathan K Ehrman ◽  
Steven J Keteyian ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Regression Model ◽  
Cardiorespiratory Fitness ◽  
Cox Regression ◽  
Stress Test ◽  
Exercise Stress ◽  
Chronotropic Incompetence ◽  
Metabolic Equivalents ◽  
Social Security Death Index ◽  
All Cause Mortality

Background: Cardiorespiratory fitness (CRF) and chronotropic incompetence (CI) have both been associated with increased all - cause mortality. We tested the hypothesis that the association of CI with all - cause mortality is modified by a CRF level above and below 10 metabolic equivalents of task (METs). Methods: We included 43,402 patients who completed a clinical exercise stress test between 1991 and 2009. Patients on AV nodal blocking agents, antiarrhythmics and with known coronary artery disease/heart failure were excluded. Patients were followed-up for a mean duration of 11.5±5.2 years for all-cause mortality ascertained by a search of the social security death index in April 2013. CRF was estimated in metabolic equivalents of task (METs). Cox proportional hazards regression models were used to assess the risk of all - cause mortality associated with CI with adjustments for confounders. Results: Compared to no CI, patients with CI were older (51±12 vs. 54±13 years), less often white (68% vs. 58%), dyslipidemic (41% vs. 37%) and more likely to be hypertensive (46% vs. 60%), diabetic (15% vs. 22%), and smoke (40% vs. 51%). On average, patients with CI had lower CRF (9.8±2.8 vs. 7.4±3.0, p <.001). In adjusted Cox regression model, CI was associated with higher risk of mortality [Hazard ratio (HR), 2.12; 95% confidence interval (2.00 -2.27); p<.001)]. The association of CI and mortality was attenuated when METs was included as a covariate in the regression model [HR, 1.49; 95% confidence interval (1.39-1.61); p<.001]. There was a significant interaction of CRF ≥ 10 METs with the association of CI and mortality (p interaction 0.004) which is shown in Figure 1. This was a subaditive and multiplicative interaction of CRF with CI. Conclusions: This study demonstrates that CRF attenuates the mortality risk associated with CI. In addition, patients with CI in the presence of CRF ≥ 10 METs have a much lower risk than patients with CI and lower CRF.

scholarly journals Association between Sleep Timing and Weight Status among 14- to 19-Year-Old Adolescents in Wuhan, China

2020 ◽  
Vol 17 (16) ◽  
pp. 5703
Author(s):  
Xinge Zhang ◽  
Yanan Chen ◽  
Rui Zhang ◽  
Justin B. Moore ◽  
Haotian Ruan ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Regression Model ◽  
Odds Ratio ◽  
Weight Status ◽  
Overweight And Obesity ◽  
Z Score ◽  
Binary Logistic Regression Model ◽  
Cross Sectional ◽  
Sleep Timing ◽  
Waking Up

This study examined the cross-sectional and longitudinal association of sleep timing with weight status in 14- to 19-year-old adolescents in Wuhan, China. A prospective school-based study was conducted in Wuhan, China between 28 May and 29 September 2019. Data on sociodemographic information, academic performance, diet, mental health status, physical activity, sleep characteristics, body weight, and height were collected. A linear regression model and binary logistic regression model were performed. A total of 1194 adolescents were included in the analysis. Adolescents who woke up before 05:45 had higher body mass index (BMI) Z-score (odds ratio (OR) with 95% confidence interval (CI) = 1.28 (1.05, 1.57), p = 0.02) and higher odds of overweight/obesity (odds ratio (OR) with 95% confidence interval (CI) = 1.74 (1.10, 2.76), p = 0.02) at baseline after fully adjustment for covariates, compared with those who woke up after 05:45. Longitudinal data showed a nonsignificant association between waking up time and change in BMI Z-score (p = 0.18). No association of bedtime with weight status was observed in this sample after full adjustment (p > 0.1). Earlier waking up time might contribute to overweight and obesity in adolescents; however, more data are needed to test and elucidate this relationship.

scholarly journals Discrepancies in Electrolyte Measurements by Direct and Indirect Ion Selective Electrodes due to Interferences by Proteins and Lipids

2020 ◽  
Vol 12 (02) ◽  
pp. 084-091
Author(s):  
Parul Chopra ◽  
Sudip Kumar Datta
Keyword(s):  
Confidence Interval ◽  
Regression Model ◽  
Analysis Of Variance ◽  
Total Protein ◽  
Multivariate Regression ◽  
Ion Selective Electrodes ◽  
Serum Samples ◽  
Serum Total Protein ◽  
Clinically Significant ◽  
Patient Subgroups

Abstract Objectives We aim to report the simultaneous effect of different protein and lipid concentrations on sodium (Na+) and potassium (K+) measurement by direct and indirect ion selective electrodes (dISE and iISE) in patient samples. Materials and Methods Na+ and K+ were measured in 195 serum samples received in the laboratory using iISE by Roche Modular P800 autoanalyzer and using dISE by XI-921 ver. 6.0 Caretium electrolyte analyzer. Serum total protein (TP), cholesterol (Chol), and triglycerides (TG) were measured using conventional photometric methods on Roche Modular P800 autoanalyzer. Differences for each pair of results for Na+ (Diff_Na+ = [Na+ dISE–Na+ iISE]) and K+ (Diff_K+ = [K+ dISE–K+ iISE]) were calculated. Patient subgroups with high, normal, or low TP (< 5, 5–7.9, or ≥ 8 g/dL), Chol (< 150, 150–299, or ≥300 mg/dL), or TG (< 150, 150–299, or ≥300 mg/dL) were compared using analysis of variance. Note that 95% confidence interval of Diff_Na+ and Diff_K+ were calculated to see the number of samples showing clinically significant differences. Results Diff_Na+ (p = 0.007) and Diff_K+ (p = 0.002) were found significant between samples with normal and high TP. However, effect of TG was not significant. Chol concentration affected Diff_Na+ significantly between low versus normal (p = 0.002), and high versus normal (p = 0.031) Chol groups. Diff_K+ was significant (p = 0.009) between low versus normal Chol. Clinically relevant disagreement of ≥|5| mmol/L for Na+ was observed in high percentage of samples including all subcategories; however, for K+ only 3.6% of the total samples showed disagreement of ≥ |0.5| mmol/L. A multivariate regression equation based on fit regression model was also derived. Conclusion Summarily, interchangeable use of electrolyte results from dISE and iISE is not advisable, especially in a setting of hyperproteinemia (≥8 g/dL) or hypercholesterolemia (≥300 mg/dL); more so for Na+.

scholarly journals Analisis Regresi Panel pada Pemodelan Tingkat Kematian Bayi di Provinsi Sulawesi Selatan

2020 ◽  
Vol 2 (2) ◽  
pp. 115
Author(s):  
Syafruddin Side ◽  
S. Sukarna ◽  
Raihana Nurfitrah
Keyword(s):  
Panel Data ◽  
Infant Mortality ◽  
Regression Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Dummy Variable ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

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.

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