Generalized inverse and its applications in classical normal multivariate regression theory

1971 ◽  
Vol 22 (1-2) ◽  
pp. 137-146
Author(s):  
J. K. Wani ◽  
D. K. Kabe
Keyword(s):  
Multivariate Regression ◽  
Generalized Inverse ◽  
Regression Theory

Confidence ellipsoids for ASCA models based on multivariate regression theory

2018 ◽  
Vol 32 (5) ◽  
pp. e2990 ◽  
Author(s):  
Kristian Hovde Liland ◽  
Age Smilde ◽  
Federico Marini ◽  
Tormod Naes
Keyword(s):  
Multivariate Regression ◽  
Regression Theory ◽  
Confidence Ellipsoids

scholarly journals Development of dose-response functions for historic paper degradation using exposure to natural conditions and multivariate regression

2019 ◽  
Vol 168 ◽  
pp. 108944 ◽  
Author(s):  
Gianluca Pastorelli ◽  
Shuo Cao ◽  
Irena Kralj Cigić ◽  
Costanza Cucci ◽  
Abdelrazek Elnaggar ◽  
...  
Keyword(s):  
Dose Response ◽  
Multivariate Regression ◽  
Response Functions ◽  
Natural Conditions ◽  
Paper Degradation ◽  
Historic Paper

Use of Damage Control and the Open Abdomen in Combat

2013 ◽  
Vol 79 (8) ◽  
pp. 747-753 ◽  
Author(s):  
Benjamin Bograd ◽  
Carlos Rodriguez ◽  
Richard Amdur ◽  
Fred Gage ◽  
Eric Elster ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Multivariate Regression ◽  
Injury Severity ◽  
Open Abdomen ◽  
Damage Control ◽  
Unit Length ◽  
Significant Risk ◽  
Hospital Length Of Stay ◽  
Study Cohort ◽  
Regression Analyses

Despite the well-documented use of damage control laparotomy (DCL) in civilian trauma, its use has not been well described in the combat setting. Therefore, we sought to document the use of DCL and to investigate its effect on patient outcome. Prospective data were collected on 1603 combat casualties injured between April 2003 and January 2009. One hundred seventy patients (11%) underwent an exploratory laparotomy (ex lap) in theater and comprised the study cohort. DCL was defined as an abbreviated ex lap resulting in an open abdomen. Patients were stratified by age, Injury Severity Score (ISS), Glasgow Coma Score (GCS), mechanism of injury, and blood product administration. Multivariate regression analyses were used to determine risks factors for intensive care unit length of stay (ICU LOS), hospital length of stay (HLOS), and the need for DCL. Mean age of the cohort was 24 ± 5 years, ISS was 21 ± 11, and 94 per cent sustained penetrating injury. Patients with DCL comprised 50.6 per cent (n = 86) of the study cohort and had significant increases in ICU admission ( P < 0.001), ICU LOS ( P < 0.001), HLOS ( P < 0.05), ventilator days ( P < 0.001), abdominal complications ( P < 0.05), but not mortality ( P = 0.65) compared with patients without DCL. When compared with the non-DCL group, patients undergoing DCL required significantly more blood products (packed red blood cells, fresh-frozen plasma, platelets, and cryoprecipitate; P < 0.001). Multivariate regression analyses revealed blood transfusion and GCS as significant risk factors for DCL ( P < 0.05). Patients undergoing DCL had increased complications and resource use but not mortality compared with patients not undergoing DCL. The need for combat DCL may be different compared with civilian use. Prospective studies to evaluate outcomes of DCL are warranted.

scholarly journals 222 Partitioning of retained energy between protein and fat gain, and heat of product formation and support metabolism in growing beef cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 158-158
Author(s):  
Phillip A Lancaster
Keyword(s):  
Linear Regression ◽  
Mixed Models ◽  
Multivariate Regression ◽  
Literature Search ◽  
Energy Use ◽  
Random Variable ◽  
Product Formation ◽  
Metabolizable Energy ◽  
Overall Efficiency ◽  
Metabolizable Energy Intake

Abstract Multiple linear regression inaccurately computes the efficiency of energy use for protein and fat gain. The objective was to quantify efficiency of metabolizable energy use for protein and fat gain along with heats of product formation and support metabolism. A literature search was performed to compile data (31 studies, 214 treatment means) on metabolizable energy intake (MEI) and composition of empty body gain in growing steers and heifers. Data analyses were performed using R statistical package for mixed models with study as random variable. Linear regression of MEI on energy gain (EG; P &lt; 0.001; R2 = 0.627) resulted in an estimate of metabolizable energy for maintenance (MEm) of 156 kcal/kg.75 and efficiency of ME use for gain of 0.518. Linear regression of MEI on EG as protein and fat (P &lt; 0.001; R2 = 0.623) resulted in an estimate of MEm of 149 kcal/kg.75, and efficiency of protein (kp) and fat (kf) gain of 0.274 and 0.585, respectively, resulting in an overall efficiency of EG of 0.520. Nonlinear regression model (EG = kg*(MEI-MEm)) resulted in an estimate of MEm of 103 kcal/kg.75 and efficiency of EG of 0.342. The heat of product formation was assumed to be 0.48 (1 – 0.52) and the heat of support metabolism (HiEv) 0.18 (0.52 – 0.34). Multivariate regression was used to fit simultaneous models for EG as protein (EGp = (kp+HiEvp)*k*MEA) and fat (EGf = (kf+(0.18-HiEvp))*(1-k)*MEA). Estimates (P &lt; 0.001) of kp and kf were 0.12 ± 0.01 and 0.63 ± 0.02, and HiEvp and proportion of ME available for protein gain (k) were 0.11 ± 0.01 and 0.75 ± 0.01, respectively. The heat of product formation and support metabolism for protein were 0.77 and 0.11, and fat were 0.30 and 0.07, respectively. In conclusion, efficiency of ME use for protein was lesser than for fat gain, and heat of support metabolism was greater for protein than fat gain.

scholarly journals 1574. Multivariate Regression Analysis to Determine Independent Predictors of Treatment Outcomes in the RESTORE-IMI 2 Trial

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S785-S786
Author(s):  
Robert Tipping ◽  
Jiejun Du ◽  
Maria C Losada ◽  
Michelle L Brown ◽  
Katherine Young ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Clinical Response ◽  
Treatment Outcomes ◽  
Multivariate Regression ◽  
Multivariate Regression Analysis ◽  
Apache Ii Score ◽  
Double Blind ◽  
Mitt Population ◽  
Study Results ◽  
All Cause Mortality

Abstract Background In the RESTORE-IMI 2 trial, imipenem/cilastatin/relebactam (IMI/REL) was non-inferior to PIP/TAZ for treating hospital-acquired/ventilator-associated bacterial pneumonia (HABP/VABP) in the primary endpoint of Day 28 all-cause mortality (D28 ACM) and the key secondary endpoint of clinical response (CR) at early follow-up (EFU; 7-14 d after end of therapy). We performed a multivariate regression analysis to determine independent predictors of treatment outcomes in this trial. Methods Randomized, controlled, double-blind, phase 3, non-inferiority trial comparing IMI/REL 500 mg/250 mg vs PIP/TAZ 4 g/500 mg, every 6 h for 7-14 d, in adult patients (pts) with HABP/VABP. Stepwise-selection logistic regression modeling was used to determine independent predictors of D28 ACM and favorable CR at EFU, in the MITT population (randomized pts with ≥1 dose of study drug, except pts with only gram-positive cocci at baseline). Baseline variables (n=19) were pre-selected as candidates for inclusion (Table 1), based on clinical relevance. Variables were added to the model if significant (p &lt; 0.05) and removed if their significance was reduced (p &gt; 0.1) by addition of other variables. Results Baseline variables that met criteria for significant independent predictors of D28 ACM and CR at EFU in the final selected regression model are in Fig 1 and Fig 2, respectively. As expected, APACHE II score, renal impairment, elderly age, and mechanical ventilation were significant predictors for both outcomes. Bacteremia and P. aeruginosa as a causative pathogen were predictors of unfavorable CR, but not of D28 ACM. Geographic region and the hospital service unit a patient was admitted to were found to be significant predictors, likely explained by their collinearity with other variables. Treatment allocation (IMI/REL vs PIP/TAZ) was not a significant predictor for ACM or CR; this was not unexpected, since the trial showed non-inferiority of the two HABP/VABP therapies. No interactions between the significant predictors and treatment arm were observed. Conclusion This analysis validated known predictors for mortality and clinical outcomes in pts with HABP/VABP and supports the main study results by showing no interactions between predictors and treatment arm. Table 1. Candidate baseline variables pre-selected for inclusion Figure 1. Independent predictors of greater Day 28 all-cause mortality (MITT population; N=531) Figure 2. Independent predictors of favorable clinical response at EFU (MITT population; N=531) Disclosures Robert Tipping, MS, Merck & Co., Inc. (Employee, Shareholder) Jiejun Du, PhD, Merck & Co., Inc. (Employee, Shareholder) Maria C. Losada, BA, Merck & Co., Inc. (Employee, Shareholder) Michelle L. Brown, BS, Merck & Co., Inc. (Employee, Shareholder) Katherine Young, MS, Merck & Co., Inc. (Employee, Shareholder)Merck & Co., Inc. (Employee, Shareholder) Joan R. Butterton, MD, Merck & Co., Inc. (Employee, Shareholder) Amanda Paschke, MD MSCE, Merck & Co., Inc. (Employee, Shareholder) Luke F. Chen, MBBS MPH MBA FRACP FSHEA FIDSA, Merck & Co., Inc. (Employee, Shareholder)Merck & Co., Inc. (Employee, Shareholder)

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