scholarly journals Linear mixed model for weight analysis in mice infected by Trypanosoma cruzi

2020 ◽  
Vol 42 ◽  
pp. e49916
Author(s):  
Roney Peterson Pereira ◽  
Terezinha Aparecida Guedes ◽  
Érika Cristina Ferreira ◽  
Silvana Marques de Araújo ◽  
Larissa Aparecida Ricardini ◽  
...  
Keyword(s):  
Trypanosoma Cruzi ◽  
Hierarchical Linear Modeling ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Ratio Test ◽  
Linear Modeling ◽  
Components Of Variance ◽  
Treatment Groups ◽  
Random Intercept

The use of linear mixed models for nested structure longitudinal data is called hierarchical linear modeling. This modeling takes into account the dependence of existing data within each level and between hierarchical levels. The process of modeling, estimating and analyzing diagnoses was illustrated through data on the weights of mice experimentally infected by Trypanosoma cruzi, divided into different treatment groups, with the purpose of verifying the evolution of their body weight as a result of using different types of biotherapeutics produced from Gallus gallus domesticus (chicken) serum to treat Trypanosoma cruzi. Through the model selection criteria AIC and BIC and the likelihood ratio test, a model was chosen to describe the data correctly. Model diagnoses were then performed by means of residual analysis for both levels and an analysis of influential observations to verify if any observations were signaled as influencing the fixed effects, the components of variance and the adjusted values. After the analysis, it was possible to notice that the observations that were signaled as influential had little impact on the Model chosen initially, so it was maintained, with no differences being evidenced between the treatments with the biotherapeutics tested; only the Time variable and the Random intercept were necessary to describe the weight of the mice.

scholarly journals Goats Given Transdermal Flunixin Meglumine Displayed Less Pain Behavior After Castration

2020 ◽  
Author(s):  
Amanda Lee ◽  
Meggan Graves ◽  
Andrea Lear ◽  
Sherry Cox ◽  
Marc Caldwell ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Body Position ◽  
Random Effect ◽  
Pain Behavior ◽  
Bout Duration ◽  
Flunixin Meglumine ◽  
Meat Goats ◽  
Transdermal Application

AbstractPain management should be utilized with castration to reduce physiological and behavioral changes. Transdermal application of drugs require less animal management and fewer labor risks, which can occur with oral administration or injections. The objective was to determine the effects of transdermal flunixin meglumine on meat goats’ behavior post-castration. Male goats (N = 18; mean body weight ± standard deviation: 26.4 ± 1.6 kg) were housed individually in pens and randomly assigned to 1 of 3 treatments: (1) castrated, dosed with transdermal flunixin meglumine; (2) castrated, dosed with transdermal placebo; and (3) sham castrated, dosed with transdermal flunixin meglumine. Body position, rumination, and head- pressing were observed for 1 h ± 10 minutes twice daily on days −1, 0, 1, 2, and 5 around castration. Each goat was observed once every 5-minutes (scan samples) and reported as percentage of observations. Accelerometers were used to measure standing, lying, and laterality (total time, bouts, and bout duration). A linear mixed model was conducted using GLIMMIX. Fixed effects of treatment, day relative to castration, and treatment*day relative to castration and random effect of date and goat nested within treatment were included. Treatment 1 goats (32.7 ± 2.8%) and treatment 2 goats (32.5 ± 2.8%) ruminated less than treatment 3 goats (47.4 ± 2.8%, P = 0.0012). Head pressing was greater on day of castration in treatment 2 goats (P < 0.001). Standing bout duration was greatest in treatment 2 goats on day 1 post-castration (P < 0.001). Lying bout duration was greatest in treatment 2 goats on day 1 post-castration compared to treatment 1 and treatment 3 goats(P < 0.001). Transdermal flunixin meglumine improved goats’ fluidity of movement post-castration and decreased head pressing, indicating a mitigation of pain behavior.

Appetite-stimulating effect of gabapentin vs mirtazapine in healthy cats post-ovariectomy

2020 ◽  
Vol 22 (12) ◽  
pp. 1176-1183
Author(s):  
Marco Fantinati ◽  
Julien Trnka ◽  
Amélia Signor ◽  
Séverine Dumond ◽  
Géraldine Jourdan ◽  
...  
Keyword(s):  
Placebo Group ◽  
Food Intake ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Repeated Measure ◽  
Mixed Model Analysis ◽  
Treatment Groups ◽  
Significant Difference ◽  
Post Hoc ◽  
Difference Test

Objectives The aim of the study was to evaluate the appetite-stimulating effect of gabapentin by comparing it with mirtazapine in healthy cats in the first 8 h after ovariectomy surgery. Methods This double-masked, placebo-controlled, prospective clinical trial included 60 healthy cats presented to the hospital for ovariectomy: 20 received gabapentin, 21 received mirtazapine and 19 received a placebo immediately before and 6 h after surgery. Food was offered at 2, 4, 6 and 8 h post-ovariectomy. After each meal, food intake was measured. Data were analysed using repeated-measure ANOVA and a linear mixed-model analysis. Post-hoc Tukey’s honest significant difference test was performed for multiple comparisons. Results Food intake increased in both treatment groups vs placebo. No statistically significant difference was found between cats treated with gabapentin or mirtazapine. Conclusions and relevance Cats receiving gabapentin ate more than cats in the placebo group. Thirty percent of cats in the gabapentin group covered their resting energy requirements, while none of the cats in the placebo group did. Gabapentin and mirtazapine produced similar effects on food intake.

Generalized linear mixed models for multi-reader multi-case studies of diagnostic tests

2015 ◽  
Vol 26 (3) ◽  
pp. 1373-1388 ◽  
Author(s):  
Wei Liu ◽  
Norberto Pantoja-Galicia ◽  
Bo Zhang ◽  
Richard M Kotz ◽  
Gene Pennello ◽  
...  
Keyword(s):  
Mixed Models ◽  
Diagnostic Tests ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Models ◽  
Characteristic Curve ◽  
Linear Mixed Models ◽  
Parametric Bootstrap ◽  
Pseudo Likelihood

Diagnostic tests are often compared in multi-reader multi-case (MRMC) studies in which a number of cases (subjects with or without the disease in question) are examined by several readers using all tests to be compared. One of the commonly used methods for analyzing MRMC data is the Obuchowski–Rockette (OR) method, which assumes that the true area under the receiver operating characteristic curve (AUC) for each combination of reader and test follows a linear mixed model with fixed effects for test and random effects for reader and the reader–test interaction. This article proposes generalized linear mixed models which generalize the OR model by incorporating a range-appropriate link function that constrains the true AUCs to the unit interval. The proposed models can be estimated by maximizing a pseudo-likelihood based on the approximate normality of AUC estimates. A Monte Carlo expectation-maximization algorithm can be used to maximize the pseudo-likelihood, and a non-parametric bootstrap procedure can be used for inference. The proposed method is evaluated in a simulation study and applied to an MRMC study of breast cancer detection.

scholarly journals Quantifying the Effects of Fusarium Head Blight on Grain Yield and Test Weight in Soft Red Winter Wheat

2015 ◽  
Vol 105 (3) ◽  
pp. 295-306 ◽  
Author(s):  
Jorge David Salgado ◽  
Laurence V. Madden ◽  
Pierce A. Paul
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Fusarium Head Blight ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Test Weight ◽  
Head Blight ◽  
Soft Red Winter Wheat ◽  
Red Winter Wheat

Fusarium head blight (FHB), caused by the fungus Fusarium graminearum, is known to negatively affect wheat grain yield (YLD) and test weight (TW). However, very little emphasis has been placed on formally quantifying FHB–YLD and FHB–TW relationships. Field plots of three soft red winter wheat cultivars—‘Cooper’ (susceptible to FHB), ‘Hopewell’ (susceptible), and ‘Truman’ (moderately resistant)—were grown during the 2009, 2010, 2011, and 2012 seasons, and spray inoculated with spore suspensions of F. graminearum and Parastagonospora nodorum to generate a range of FHB and Stagonospora leaf blotch (SLB) levels. FHB index (IND) and SLB were quantified as percent diseased spike and flag leaf area, respectively, and YLD (kg ha−1) and TW (kg m−3) data were collected. Using IND as a continuous covariate and cultivar (CV) and SLB as categorical fixed effects, linear mixed-model regression analyses (LMMR) were used to model the IND–YLD and IND–TW relationship and to determine whether these relationships were influenced by CV and SLB. The final models fitted to the data were of the generic form y = a + b (IND), where a (intercept) or b (slope) could also depend on other factors. LMMR analyses were also used to estimate a and b by combining the studies from these 4 years with an additional 16 experiments conducted from 2003 to 2013, and bivariate random-effects meta-analysis was used to estimate population mean b ([Formula: see text]) and a (ā) for the IND–YLD relationship. YLD and TW decreased as IND increased, with b ranging from −3.2 to −2.3 kg m−3 %−1 for TW. For the IND–YLD relationship, [Formula: see text] was −51.7 kg ha−1 %IND−1 and ā was 4,426.7 kg ha−1. Neither cultivar nor SLB affected the IND–YLD relationship but SLB affected a of the IND–TW regression lines, whereas cultivar affected b. Plots with the highest levels of SLB (based on ordinal categories for SLB) had the lowest a and Hopewell had the highest b. The level of IND at which a 50-kg m−3 reduction in TW was predicted to occur was 19, 16, and 22% for Cooper, Hopewell, and Truman, respectively. A yield loss of 1 MT ha−1 was predicted to occur at 19% IND. The rate of reduction in relative TW or YLD per unit increase in IND was between −0.39 and −0.32%−1 for TW and −1.17%−1 for YLD. Results from this study could be integrated into more general models to evaluate the economics of FHB management strategies.

Misspecification of the random effect structure: Implications for the linear mixed model

2020 ◽  
Author(s):  
Brandon LeBeau
Keyword(s):  
Fixed Effects ◽  
Serial Correlation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Type I Error ◽  
Random Effect ◽  
Correlated Data ◽  
Unbiased Estimation ◽  
Type I ◽  
Nested Data

<p>The linear mixed model is a commonly used model for longitudinal or nested data due to its ability to account for the dependency of nested data. Researchers typically rely on the random effects to adequately account for the dependency due to correlated data, however serial correlation can also be used. If the random effect structure is misspecified (perhaps due to convergence problems), can the addition of serial correlation overcome this misspecification and allow for unbiased estimation and accurate inferences? This study explored this question with a simulation. Simulation results show that the fixed effects are unbiased, however inflation of the empirical type I error rate occurs when a random effect is missing from the model. Implications for applied researchers are discussed.</p>

scholarly journals Exercise-induced hypoalgesia in women with varying levels of menstrual pain

2018 ◽  
Vol 18 (2) ◽  
pp. 303-310 ◽  
Author(s):  
Mervyn Travers ◽  
Penny Moss ◽  
William Gibson ◽  
Dana Hince ◽  
Sheree Yorke ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Activity Levels ◽  
Menstrual Pain ◽  
Isometric Handgrip ◽  
Pressure Pain Thresholds ◽  
Significant Difference ◽  
Exercise Induced

Abstract Background and aims: Exercise-induced hypoalgesia (EIH) is a well-established phenomenon in pain-free individuals that describes a decrease in pain sensitivity after an acute bout of exercise. The EIH response has been demonstrated to be sub-optimal in the presence of persisting pain. Menstrual pain is a common recurrent painful problem with many women experiencing high levels of pain each cycle. However, the EIH response has not been examined in a cohort of women with high levels of menstrual pain. This research aimed to examine whether EIH manifests differently in women with varying levels of menstrual pain. The primary hypothesis was that women with high levels of menstrual pain would demonstrate compromised EIH. Secondary aims were to explore relationships between EIH and emotional state, sleep quality, body mass index (BMI) or physical activity levels. Methods: Pressure pain thresholds (PPT) were measured in 64 participants using a digital handheld algometer before and after a submaximal isometric-handgrip exercise. EIH index was compared between low (VAS 0–3), moderate (VAS 4–7) and high (VAS 8–10) pain groups, using a linear mixed model analysis with participant as a random effect, and site, menstrual pain category and the interaction between the two, as fixed effects. Results: EIH was consistently induced in all groups. However, there was no statistically significant difference between the pain groups for EIH index (p=0.835) or for any co-variates (p>0.05). Conclusions: EIH was not found to differ between women who report regular low, moderate or high levels of menstrual pain, when measured at a point in their menstrual cycle when they are pain free. Implications: This study provides insight that EIH does not vary in women with differing levels of menstrual pain when they are not currently experiencing pain. The current findings indicate that, although menstrual pain can involve regular episodes of high pain levels, it may not be associated with the same central nervous system dysfunctions as seen in sustained chronic pain conditions.

scholarly journals A generalized linear mixed model for understanding determinant factors of student's interest in pursuing bachelor's degree at Universitas Syiah Kuala

2021 ◽  
Vol 21 (2) ◽  
pp. 72-80
Author(s):  
ASEP RUSYANA ◽  
KHAIRIL ANWAR NOTODIPUTRO ◽  
BAGUS SARTONO
Keyword(s):  
High Schools ◽  
Normal Distribution ◽  
Random Effects ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Likelihood Method ◽  
Response Variable ◽  
Banda Aceh

Generalized Linear Mixed Model (GLMM) is a framework that has a response variable, fixed effects, and random effects. The response variable comes from an exponential family, whereas random effects have a normal distribution. Estimating parameters can be calculated using the maximum likelihood method using the Laplace approach or the Gauss-Hermite Quadrature (GHQ) approach. The purpose of this study was to identify factors that trigger student's interest to continue studying at Universitas Syiah Kuala (USK) using both techniques.  The GLMM is suitable for the data because the variable response has a Bernoulli distribution, and the random effects are assumed to be having a normal distribution. Also, the model helps identify the relationship between the dependent variable and the predictors. This study utilizes data from six high schools in Banda Aceh city drawn using a two-stage sampling technique. Stage 1, we randomly chose six out of sixteen public senior high schools in Banda Aceh. Stage 2, we selected students from each school from four different major classes. The GLMM model includes one binary response variable, five numerical fixed-effects, and two random effects. The response variable is the interest of high school students to continue study at USK (yes or no). The five fixed effects in the model including scores of collaboration (C), Action (A), Emotion (E), Purposes (P), and Hope (H).  Finally, the random effects are schools (S) and majors (M). In this study, both Laplace and GHQ techniques produce identical results. The predictors that can explain student interest are A, E, and H. These predictors have a positive effect. The random effects of schools and majors are not significantly different from zero. The model with three significant predictors is better than the complete predictor model.

scholarly journals PSVIII-10 Effects of enzyme supplementation in corn distiller’s dried grains with solubles (DDGS) diets on growth performance in broilers: a meta-analysis

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 211-211
Author(s):  
Jae-Cheol Jang ◽  
Zhikai Zeng ◽  
Pedro E Urriola ◽  
Gerald C Shurson
Keyword(s):  
Growth Performance ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Meta Analysis ◽  
Growth Responses ◽  
Exogenous Enzymes ◽  
The Mean ◽  
Enzyme Supplementation ◽  
Mean Differences

Abstract The objective of this study was to conduct a meta-analysis to quantitatively summarize the growth responses of broilers fed cDDGS and the efficacy of various types of dietary enzyme supplementation. A total of 12 publications with 69 observations were included in the database. Individual observations were analyzed using a multivariable linear mixed model. The mean differences (MD) of BWG, FI, and gain efficiency (G/F) were calculated by subtracting either the enzyme response in corn-soybean meal (CSB) or CSB+cDDGS based diets to the control, and was expressed as a percentage (MD = (enzyme – control)/control ×100%). A type of exogenous enzymes (xylanase; protease; carbohydrases; cocktail = proteases + carbohydrases), and feeding phase (starter = d 0 to d 21; finisher = d 21 to d 42 or 49; overall = d 0 to d 42 or more) were included as fixed effects. Dietary enzyme inclusion showed significant improvement on BWG (3.19%, P &lt; 0.01) and G/F (5.69%, P &lt; 0.01) in broilers fed cDDGS diet. However, no significant enzyme responses were observed in broilers fed CSB diet on growth performance. Broilers fed cDDGS diet had increased (P &lt; 0.01) BWG with the addition of protease (3.32 %) and cocktail (3.27 %), whereas addition of xylanased improved (P &lt; 0.01) G/F by (3.56 %) and carbohydrases (1.90 %). Broilers fed cDDGS diet with enzyme supplementation showed greater improvement in BWG (3.71 %, P &lt; 0.01) and G/F (3.78 %, P &lt; 0.01) at finisher phase compared with starter phase. Likewise, Broilers fed CSB diet with enzyme supplementation increased BWG (9.40 %, P &lt; 0.01) and G/F (3.11 %, P &lt; 0.01) at finisher phase. In conclusion, supplementation of xylanase and carbohydrases in cDDGS diet improved G/F, and the enzyme response can be maximized when fed during the finisher phase diet compared with the starter phase diet.

scholarly journals Detection of Methane Eructation Peaks in Dairy Cows at a Robotic Milking Station Using Signal Processing

Animals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Ali Hardan ◽  
Philip C. Garnsworthy ◽  
Matt J. Bell
Keyword(s):  
Signal Processing ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Air Sampling ◽  
Analysis Software ◽  
Sampling Volume ◽  
Ch4 Concentration ◽  
Robotic Milking

The aim of this study was to investigate the use of signal processing to detect eructation peaks in CH4 released by cows during robotic milking, and to compare recordings from three gas analysers (Guardian SP and NG, and IRMAX) differing in volume of air sampled and response time. To allow comparison of gas analysers using the signal processing approach, CH4 in air (parts per million) was measured by each analyser at the same time and continuously every second from the feed bin of a robotic milking station. Peak analysis software was used to extract maximum CH4 amplitude (ppm) from the concentration signal during each milking. A total of 5512 CH4 spot measurements were recorded from 65 cows during three consecutive sampling periods. Data were analysed with a linear mixed model including analyser × period, parity, and days in milk as fixed effects, and cow ID as a random effect. In period one, air sampling volume and recorded CH4 concentration were the same for all analysers. In periods two and three, air sampling volume was increased for IRMAX, resulting in higher CH4 concentrations recorded by IRMAX and lower concentrations recorded by Guardian SP (p < 0.001), particularly in period three, but no change in average concentrations measured by Guardian NG across periods. Measurements by Guardian SP and IRMAX had the highest correlation; Guardian SP and NG produced similar repeatability and detected more variation among cows compared with IRMAX. The findings show that signal processing can provide a reliable and accurate means to detect CH4 eructations from animals when using different gas analysers.

scholarly journals The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling

2021 ◽  
Vol 12 ◽  
Author(s):  
Soyoung Kim ◽  
Yoonhwa Jeong ◽  
Sehee Hong
Keyword(s):  
Random Effects ◽  
Hierarchical Linear Modeling ◽  
Fixed Effects ◽  
Random Effect ◽  
Information Criteria ◽  
Linear Modeling ◽  
Random Effect Modeling ◽  
Error Bias ◽  
The Impact ◽  
Level 2

The present study investigated estimate biases in cross-classified random effect modeling (CCREM) and hierarchical linear modeling (HLM) when ignoring a crossed factor in CCREM considering the impact of the feeder and the magnitude of coefficients. There were six simulation factors: the magnitude of coefficient, the correlation between the level 2 residuals, the number of groups, the average number of individuals sampled from each group, the intra-unit correlation coefficient, and the number of feeders. The targeted interests of the coefficients were four fixed effects and two random effects. The results showed that ignoring a crossed factor in cross-classified data causes a parameter bias for the random effects of level 2 predictors and a standard error bias for the fixed effects of intercepts, level 1 predictors, and level 2 predictors. Bayesian information criteria generally outperformed Akaike information criteria in detecting the correct model.

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