scholarly journals Landmark-based estimation vs. linear regression model: a retrospective comparison for prediction of optimal length of right subclavian vein catheterization in infants

2021 ◽  
Author(s):  
Chahyun Oh ◽  
Boohwi Hong ◽  
Yumin Jo ◽  
Woosuk Chung ◽  
Hoseop Kim ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Subclavian Vein ◽  
Pediatric Patients ◽  
Optimal Length ◽  
The Mean ◽  
The Right ◽  
Insertion Length ◽  
Vein Catheterization

Background: The optimal insertion length for right subclavian vein catheterization in infants has not been determined. This study retrospectively compared landmark-based and linear regression model-based estimation of optimal insertion length for right subclavian vein catheterization in pediatric patients of corrected age < 1 year. Methods: Fifty catheterizations of the right subclavian vein were analyzed. The landmark related distances were: from the needle insertion point (I) to the tip of the sternal head of the right clavicle (A) and from A to the midpoint (B) of the perpendicular line drawn from the sternal head of the right clavicle to the line connecting the nipples. The optimal length of insertion was retrospectively determined by reviewing post-procedural chest radiographs. Estimates using a landmark-based equation (IA + AB – intercept) and a linear regression model were compared with the optimal length of insertion. Results: A landmark-based equation was determined as IA + AB – 5. The mean difference between the landmark-based estimate and the optimal insertion length was 1.0 mm (95% limits of agreement –18.2 to 20.3 mm). The mean difference between the linear regression model (26.681 – 4.014 × weight + 0.576 × IA + 0.537 × AB – 0.482 × postmenstrual age) and the optimal insertion length was 0 mm (95% limits of agreement –16.7 to 16.7 mm). The difference between the estimates using these two methods was not significant. Conclusion: A simple landmark-based equation may be useful for estimating optimal insertion length in pediatric patients of corrected age < 1 year undergoing right subclavian vein catheterization.

scholarly journals On the Stochastic Restrictedr-kClass Estimator and Stochastic Restrictedr-dClass Estimator in Linear Regression Model

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Jibo Wu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mean Squared Error ◽  
Multiple Linear Regression Model ◽  
Error Matrix ◽  
Squared Error ◽  
The Mean ◽  
Linear Restrictions ◽  
Theoretical Results

The stochastic restrictedr-kclass estimator and stochastic restrictedr-dclass estimator are proposed for the vector of parameters in a multiple linear regression model with stochastic linear restrictions. The mean squared error matrix of the proposed estimators is derived and compared, and some properties of the proposed estimators are also discussed. Finally, a numerical example is given to show some of the theoretical results.

scholarly journals Fiducial-based image-guided SBRT for pancreatic adenocarcinoma: Does inter-and intra-fraction treatment variation warrant adaptive therapy?

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Colin S. Hill ◽  
Sarah Han-Oh ◽  
Zhi Cheng ◽  
Ken Kang-Hsin Wang ◽  
Jeffrey J. Meyer ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Pancreatic Adenocarcinoma ◽  
Linear Regression Model ◽  
Organs At Risk ◽  
Motion Management ◽  
The Mean ◽  
Adaptive Therapy ◽  
Mean Vector ◽  
Vector Shift

Abstract Purpose Variation in target positioning represents a challenge to set-up reproducibility and reliability of dose delivery with stereotactic body radiation therapy (SBRT) for pancreatic adenocarcinoma (PDAC). While on-board imaging for fiducial matching allows for daily shifts to optimize target positioning, the magnitude of the shift as a result of inter- and intra-fraction variation may directly impact target coverage and dose to organs-at-risk. Herein, we characterize the variation patterns for PDAC patients treated at a high-volume institution with SBRT. Methods We reviewed 30 consecutive patients who received SBRT using active breathing coordination (ABC). Patients were aligned to bone and then subsequently shifted to fiducials. Inter-fraction and intra-fraction scans were reviewed to quantify the mean and maximum shift along each axis, and the shift magnitude. A linear regression model was conducted to investigate the relationship between the inter- and intra-fraction shifts. Results The mean inter-fraction shift in the LR, AP, and SI axes was 3.1 ± 1.8 mm, 2.9 ± 1.7 mm, and 3.5 ± 2.2 mm, respectively, and the mean vector shift was 6.4 ± 2.3 mm. The mean intra-fraction shift in the LR, AP, and SI directions were 2.0 ± 0.9 mm, 2.0 ± 1.3 mm, and 2.3 ± 1.4 mm, respectively, and the mean vector shift was 4.3 ± 1.8 mm. A linear regression model showed a significant relationship between the inter- and intra-fraction shift in the AP and SI axis and the shift magnitude. Conclusions Clinically significant inter- and intra-fraction variation occurs during treatment of PDAC with SBRT even with a comprehensive motion management strategy that utilizes ABC. Future studies to investigate how these variations could lead to variation in the dose to the target and OAR should be investigated. Strategies to mitigate the dosimetric impact, including real time imaging and adaptive therapy, in select cases should be considered.

scholarly journals A Study of Relationship Between Serum Uric Acid, and Fasting Plasma Glucose in Type 2 Diabetes Mellitus Patients

2020 ◽  
Vol 11 (4) ◽  
pp. 7229-7233
Author(s):  
Anuradha G ◽  
Santhini Gopalakrishnan S ◽  
Vinodakumar H R
Keyword(s):  
Uric Acid ◽  
Linear Regression ◽  
Regression Model ◽  
Serum Uric Acid ◽  
Plasma Glucose ◽  
Linear Regression Model ◽  
Fasting Plasma Glucose ◽  
The Mean ◽  
The Relationship

The current study was conducted to observe the relationship between serum uric acid, lipid profile and fasting plasma glucose in type 2 DM patients. It was a cross-sectional study. A total of 618 participants were included in the study (203-healthy, 206-prediabetic and 209-T2DM). One way analysis of variance was used to compare the mean between these three groups. A linear regression model was used to find the relationship between SUA and FPG in T2DM. The mean values of serum uric acid in pre-diabetes and T2DM (4.929±1.33 and 4.69±1.41 mg/dl, respectively) were lower compared to healthy (5.40±1.08 mg/dl). SUA showed a significant positive correlation with serum triglycerides in T2DM (p<0.05). The linear regression model showed that SUA was inversely associated with FPG in T2DM after adjustment for age and gender. The biological interrelationship observed in the current study raises the possibility of potential pathogenic overlap between SUA and FPG. SUA might be involved in a metabolic imbalance which in turn leads to T2DM.

scholarly journals Consistency of the Modified Semi-Parametric MLE under the Linear Regression Model with Right-Censored Data

2021 ◽  
Vol 25 (2) ◽  
pp. 83-108
Author(s):  
Qiqing Yu ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Censored Data ◽  
Linear Regression Model ◽  
Regularity Conditions ◽  
Necessary And Sufficient Condition ◽  
Right Censored Data ◽  
Right Censorship ◽  
The Right ◽  
Necessary And Sufficient

Under the right censorship model and under the linear regression model where may not exist, the modified semi-parametric MLE (MSMLE) was proposed by Yu and Wong [17]. The MSMLE of satisfying infinitely often) if is discontinuous, and the simulation study suggests that it is also consistent and efficient under certain regularity conditions. In this paper, we establish the consistency of the MSMLE under the necessary and sufficient condition that is identifiable. Notice that under the latter assumption, the Buckley-James estimator and the median regression estimator can be inconsistent (see Yu and Dong [20]).

scholarly journals The Performance of Some Restricted Estimators In Restricted Linear Regression Model

2021 ◽  
Vol 26 (2) ◽  
Author(s):  
Bader Aboud ◽  
Mustafa Ismaeel Naif
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Simulation Study ◽  
Linear Regression Model ◽  
Regression Estimator ◽  
Mean Square ◽  
Ridge Regression Estimator ◽  
The Mean ◽  
Biased Estimators ◽  
Better Than

In the linear regression model, the restricted biased estimation as one of important  methods to addressing the high variance and the  multicollinearity problems. In this paper, we make the simulation study of the some restricted biased estimators. The mean square error (MME) criteria are used to make a comparison  among them. According to the simulation study we observe that, the performance of the restricted modified unbiased  ridge regression estimator (RMUR) was proposed by  Bader and Alheety (2020)  is better than  of these estimators. Numerical example have been considered to illustrate the performance of the estimators.

scholarly journals CONCEPT TO ANALYZE THE DISPLACEMENT TIME SERIES OF INDIVIDUAL PERSISTENT SCATTERERS

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 147-154
Author(s):  
M. Evers ◽  
A. Thiele ◽  
H. Hammer ◽  
E. Cadario ◽  
K. Schulz ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Piecewise Linear ◽  
Deformation Pattern ◽  
Mean Velocity ◽  
Persistent Scatterers ◽  
Persistent Scatterer ◽  
The Mean

Abstract. Persistent Scatterer Interferometry (PSInSAR) exploits a time series of Synthetic Aperture Radar (SAR) images to estimate the mean velocity with which the surface of the earth is deforming. However, most PSInSAR algorithms estimate the mean velocities using a linear regression model. Since some deformation phenomena can exhibit a more complex behavior over time, using a linear regression model leads to potentially wrong estimations for the mean velocity. For example, the velocity of a landslide moving down a steep slope can change depending on the water content of the material of the landslide, or an inactive landslide can reactivate due to an earthquake. Both scenarios would not result in a time series with a constant linear slope but in a piecewise linear time series.This paper presents a Matlab-based tool to analyze an individual Persistent Scatterer (PS) time series. The Persistent Scatterer Deformation Pattern Analysis Tool (PSDefoPAT) aims to build a mathematical model that sufficiently describes the time series trend and seasonal and noise components. The trend component is estimated using polynomial regression and piecewise linear models, while a sine function approximates the seasonal component. The goal is to identify the best fitting model for the displacement time series of a PS. PSDefoPAT is introduced by examine the time series of three different PS located in the region surrounding Patras, Greece. Based on the derived models, we discuss the nature of their deformation patterns.

Adults' Performance on a Measure of Visual Closure

1982 ◽  
Vol 55 (3) ◽  
pp. 943-952 ◽  
Author(s):  
Mark P. Vigen ◽  
Ronald A. Goebel ◽  
Larry J. Embree
Keyword(s):  
College Students ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Educational Level ◽  
Entire Sample ◽  
Technical Students ◽  
The Mean ◽  
Neurologically Intact ◽  
Normal Performance

The Mooney Closure Faces Test has been employed in several studies of right temporal-lobe function. However, the information provided by these studies is somewhat restricted because there has been no systematic attempt to determine what constitutes “normal” performance on this instrument. The present study attempts to rectify partially this situation by testing samples of three distinct populations (college students, vocational-technical students, and relatives of indigent patients) of neurologically intact subjects who differed in age, IQ, educational level, sex, and handedness. Regression showed all of these variables to be significantly correlated with performance on the Mooney Closure Faces Test. The final linear regression model was correlated 0.718 with the Mooney scores and resulted in predictions which were approximately 30% more accurate than those made using the mean of the entire sample alone.

Association of reductions in PSA screening across states with increased metastatic prostate cancer in the United States.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 228-228
Author(s):  
Vidit Sharma ◽  
Abhishek Venkataramana ◽  
W. Scott Comulada ◽  
Mark S. Litwin ◽  
Christopher Saigal
Keyword(s):  
Prostate Cancer ◽  
United States ◽  
Linear Regression ◽  
Regression Model ◽  
Random Effects ◽  
Linear Regression Model ◽  
Metastatic Prostate Cancer ◽  
The United States ◽  
Psa Screening ◽  
The Mean

228 Background: While PSA screening was found to reduce prostate cancer metastasis and mortality in a large European randomized trial, PSA screening has also resulted in over-treatment of prostate cancer with significant quality-of-life implications. As a result, the US Preventive Services Task Force (USPSTF) did not recommend PSA screening in 2008 and 2012. It is unknown if reductions in PSA screening were responsible for increased metastatic prostate cancer in the United States. We test this hypothesis by associating longitudinal variations across individual states in PSA screening with their incidence of metastatic prostate cancer at diagnosis from 2002 to 2016. Methods: Age-adjusted incidences of metastatic prostate cancer at diagnosis per 100,000 men were obtained from the North American Association of Central Cancer Registries in 2002 – 2016 for each state. Survey-weighted PSA screening estimates for each state were extracted from the Behavioral Risk Factor Surveillance System, which collects this information for men at least 40 years of age every 2 years from 2002 onward. PSA screening and metastasis data were collated as a multi-panel time series and then analyzed using a random-effects linear regression model with random effects at the state level. Results: There was significant variation between states in the percent of men age >40 years who reported ever receiving PSA screening (range 40.1% to 70.3%) and in the age-adjusted incidence of metastatic prostate cancer at diagnosis (range 3.3 to 14.3 per 100,000). From 2008 to 2016, the mean percentage of men screened decreased (61.8% to 50.5%) whereas the mean incidence of metastatic prostate cancer at diagnosis increased (6.4 to 9.0 per 100,000; Bonferroni adjusted p < 0.001 for both). A random-effects linear regression model demonstrated that longitudinal reductions across states in PSA screening were associated with increased metastatic prostate cancer (regression coefficient per 100,000 men: 14.9, 95% CI 12.3 – 17.5, p < 0.001). This indicated that states with larger declines in PSA screening had larger increases in the incidence of metastatic prostate cancer at diagnosis. Variation in PSA screening explained 27% of the longitudinal variation in metastatic prostate cancer within states. Conclusions: In the context of randomized trial data demonstrating a metastasis reduction with PSA screening, our study strengthens the epidemiologic evidence that reductions in PSA screening may explain some of the recent increase in metastatic prostate cancer at diagnosis in the United States. The trend of rising metastatic disease at diagnosis is a worrisome consequence that needs attention. Thus, we support shared-decision making policies, such as the 2018 USPSTF update, that may optimize PSA screening utilization to reduce the incidence of metastatic prostate cancer in the United States.

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