Response Variable Of Interest Research Articles

Effects of Feeding Systems on the Growth Performance, Carcass Characteristics, and Meat Quality in Sheep: A Meta-Analysis.

Meat quality is important in the meat-production chain. Conflicting reports of the effects of feeding systems on sheep growth performance and meat quality exist. By way of meta-analysis, we reviewed the literature on the growth and slaughter performance, and meat quality of lambs that grazed solely on pasture, those that grazed on pasture but received a dietary supplement, and those were exclusively fed indoors. The relevant literature comprised 28 papers, from which response variables of interest were obtained. Compared with stall-fed sheep, pasture-grazing led to significantly (p < 0.05) lower average daily gain, slaughter live weight, hot carcass weight, cold carcass weight, and similar dressing percentage, but pasture-grazed sheep fed a supplement had similar (p > 0.05) values for each of these attributes to stall-fed sheep. The quality of the longissimus muscle from lambs that grazed either exclusively on pasture or pasture with a supplement had significantly (p < 0.05) lower lightness and intramuscular fat content, and significantly (p < 0.05) higher yellowness, Warner-Bratzler shear force, and protein content than meat from stall-fed sheep. We conclude that sheep that have fed exclusively on pasture have lower carcass yield and meat edibility, but improved meat quality, and that pasture-fed sheep that received a supplement had comparable carcass attributes, but greater meat color and health quality than stall-fed sheep.

ACE surrogate Model–Based uncertainty and sensitivity analysis methods for severe accident codes

This paper explores the alternating conditional expectation (ACE) algorithm-based surrogate model to advance the state-of-practice in uncertainty and sensitivity analysis methodologies for severe accident codes. For engineering purposes, the ACE algorithm has been used as an alternative means to find the optimal functional forms of the multiple input variables and response variables of interest. Analysis results here demonstrate that compared with the reference cases the proposed surrogate model provides much higher performance in terms of the coefficient of determination (R2) and normalized root mean square error (NRMSE), thus giving more robust insights into the relationship and correlation between the input parameters and figures of merit (FOMs) of interest. Relevant results and insights are summarized in terms of points of interest.

Capsaicin as a Dietary Additive for Dairy Cows: A Meta-Analysis on Performance, Milk Composition, Digestibility, Rumen Fermentation, and Serum Metabolites.

This study aimed to evaluate the effects of dietary supplementation with capsaicin (CAP) on productive performance, milk composition, nutrient digestibility, ruminal fermentation, and serum metabolites of dairy cows using a meta-analytical approach. The database included 13 studies, from which the response variables of interest were obtained. Data were analyzed using a random effects model, and results were expressed as weighted mean differences between treatments supplemented with and without CAP. Dietary supplementation with CAP increased (p < 0.05) dry matter intake, milk yield, feed efficiency, milk fat yield, and milk fat content. However, CAP supplementation did not affect (p > 0.05) milk protein and lactose yield, milk urea nitrogen, or milk somatic cell count. Greater (p < 0.05) apparent digestibility of dry matter and crude protein was observed in response to the dietary inclusion of CAP. Likewise, supplementation with CAP increased (p < 0.05) the rumen concentration of total volatile fatty acids. In contrast, CAP supplementation did not affect (p > 0.05) ruminal pH or the ruminal concentration of ammonia nitrogen, acetate, propionate, and butyrate. In blood serum, CAP supplementation increased (p < 0.05) the glucose concentration and decreased (p < 0.05) the concentration of non-esterified fatty acids. However, CAP supplementation did not affect (p > 0.05) the serum concentration of urea and beta-hydroxybutyrate. In conclusion, capsaicin can be used as a dietary additive to improve the productive performance, milk composition, and nutrient digestibility in dairy cows and, at the same time, improve the ruminal concentration of total volatile fatty acids and serum levels of glucose and non-esterified fatty acids.

A meta-analysis of essential oils as a dietary additive for weaned piglets: Growth performance, antioxidant status, immune response, and intestinal morphology

This study aimed to evaluate the effects of dietary supplementation with EOS on growth performance, blood serum antioxidant status, immune response, and intestinal morphology of weaned piglets using a meta-analytical approach. The database included 31 studies from which the response variables of interest were obtained. All data were analyzed using a random effects model, and results were expressed as weighted mean differences between treatments supplemented with and without EOS. EOS supplementation increased (P < 0.001) average daily feed intake, average daily gain, and final body weight and decreased (P < 0.001) feed conversion ratio and diarrhea incidence. Lower (P = 0.001) serum malondialdehyde content and higher (P < 0.05) serum concentrations of superoxide dismutase, catalase, glutathione peroxidase, and total antioxidant capacity were observed in response to the dietary inclusion of EOS. EOS supplementation increased (P < 0.001) the serum concentration of immunoglobulins A, G, and M and decreased (P < 0.05) the serum concentration of tumor necrosis factor-α, interleukin-1β, and interleukin-6. Greater (P ≤ 0.001) villus height (VH) was observed in the jejunum and ileum in response to the dietary inclusion of EOS. However, EOS supplementation did not affect (P > 0.05) crypt depth (CD) and decreased (P < 0.001) the VH/CD ratio in the duodenum, jejunum, and ileum. In conclusion, essential oils can be used as a dietary additive to improve growth performance and reduce the incidence of diarrhea in weaned piglets and, at the same time, improve the antioxidant status in blood serum, immune response, and intestinal morphology.

Viscosity, mixing regime and power consumption analysis during enzymatic hydrolysis of fish (Sardina pilchardus) processing by-products, through development of power number and Reynolds number correlations

The influence of processing conditions on fluid rheology and mixing regime is of considerable importance during enzymatic hydrolysis of fish processing by-products. Solids concentration ranging from 30 to 70 % (wet basis) and mixing speed from 100 to 400 rpm were investigated, with apparent viscosity, power consumption, power number (Np) and Reynolds number (Re) as response variables of interest. The fish processing by-products at different solids concentration showed power-law pseudoplastic behaviour. Apparent viscosity ranged from 4.35 to 8.51 mPa·s, and power consumption ranged from 53.0 to 492 W/m3. Np―Re curves showed the typical relationship where Np decreased from 20.4 to 1.99 when Re increased from 425 to 3 317. Poor mixing was observed at high solids concentration and low mixing speed, with cavern formation in the early stages of mixing. Liquefaction of solids led to the disappearance of caverns, resulting in good degree of mixing during the hydrolysis process. Contrary to expectations, our study indicated that liquefaction of solids and reduction in molecular weight of proteins had a significant effect on power consumption more than increase in soluble protein concentration, gel formation and/or emulsion. Liquefaction and molecular weight reduction caused a decrease in fluid viscosity while increase in protein concentration, gel formation and/or emulsion contributed to an increase in viscosity. The net result was a decrease in power consumption from 0 to 60 min of enzymatic hydrolysis. The practical implications of the results presented in this study are that one Np―Re curve can be used for any solids concentration between 30 and 50 %. Beyond 50 %, low degree of hydrolysis, protein recovery and significantly high power consumption are experienced.

Inference on the best policies with many covariates

Understanding the impact of the most effective policies or treatments on a response variable of interest is desirable in many empirical works in economics, statistics and other disciplines. Due to the widespread winner’s curse phenomenon, conventional statistical inference assuming that the top policies are chosen independent of the random sample may lead to overly optimistic evaluations of the best policies. In recent years, given the increased availability of large datasets, such an issue can be further complicated when researchers include many covariates to estimate the policy or treatment effects in an attempt to control for potential confounders. In this manuscript, to simultaneously address the above-mentioned issues, we propose a resampling-based procedure that not only lifts the winner’s curse in evaluating the best policies observed in a random sample, but also is robust to the presence of many covariates. The proposed inference procedure yields accurate point estimates and valid frequentist confidence intervals that achieve the exact nominal level as the sample size goes to infinity for multiple best policy effect sizes. We illustrate the finite-sample performance of our approach through Monte Carlo experiments and two empirical studies, evaluating the most effective policies in charitable giving and the most beneficial group of workers in the National Supported Work program.

Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm

Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. However, it is not easy to determine the optimal model beforehand. Therefore, in this paper, we look at how to choose a hyper-parameter in a kernel semi-parametric regression model. A quasi-oppositional learning pelican optimization algorithm strategy is used to select the smoothness parameter. In comparison to other competitor approaches, simulation results revealed that the suggested method, the quasi-oppositional learning pelican optimization algorithm, is superior in terms of MSE. The experimental findings and statistical analysis show that when compared to the CV and GCV, our proposed quasi-oppositional learning pelican optimization algorithm provides greater performance in terms of computational time.

Prediction of Specific Antibody- and Cell-Mediated Responses Using Baseline Immune Status Parameters of Individuals Received Measles-Mumps-Rubella Vaccine.

A successful vaccination implies the induction of effective specific immune responses. We intend to find biomarkers among various immune cell subpopulations, cytokines and antibodies that could be used to predict the levels of specific antibody- and cell-mediated responses after measles-mumps-rubella vaccination. We measured 59 baseline immune status parameters (frequencies of 42 immune cell subsets, levels of 13 cytokines, immunoglobulins) before vaccination and 13 response variables (specific IgA and IgG, antigen-induced IFN-γ production, CD107a expression on CD8+ T lymphocytes, and cellular proliferation levels by CFSE dilution) 6 weeks after vaccination for 19 individuals. Statistically significant Spearman correlations between some baseline parameters and response variables were found for each response variable (p < 0.05). Because of the low number of observations relative to the number of baseline parameters and missing data for some observations, we used three feature selection strategies to select potential predictors of the post-vaccination responses among baseline variables: (a) screening of the variables based on correlation analysis; (b) supervised screening based on the information of changes of baseline variables at day 7; and (c) implicit feature selection using regularization-based sparse regression. We identified optimal multivariate linear regression models for predicting the effectiveness of vaccination against measles-mumps-rubella using the baseline immune status parameters. It turned out that the sufficient number of predictor variables ranges from one to five, depending on the response variable of interest.

Pain and physical activity for one individual

Aims: there is increasing awareness that for effective patient care we need more than only randomized controlled trials with groups of participants and that carefully collected single case (N = 1) data have several important advantages over traditional group-level studies. With the advance of technology, collecting relevant data from a single case is becoming easier by the day, and this offers tremendous opportunities for understanding how behaviors displayed by an individual can be influenced by one or several key variables. For example, how pain experienced influences the amount of time spent on physical exercise. Method: using publicly available observational single case data, five models are compared: a classical ordinary least squares (OLS) linear regression model; a dynamic regression model (DRM); a two-level random-intercepts model (2LRI); a continuous covariate first-order autoregressive correlation model (CAR1); and an ordinary least squares model with time trend (OLST). These models are compared in terms of overall model fit statistics, estimates of the relation between physical activity (response variable of interest) and pain (covariate of interest), and residual statistics. Results: 2LRI outperforms all other models on both overall model fit and residual statistics, and provides covariate estimates that are in between the relative extremes provided by other models. CAR1 and OLST demonstrate an almost identical performance and one that is substantially better than OLS – which performs worst – and DRM. Conclusion: for observational single case data, DRM, CAR1, OLST, and 2LRI account for the serial correlation that is typically present in single case data in somewhat different ways under somewhat different assumptions, and all perform better than OLS. Implications of these findings for observational, quasi-experimental, and experimental single case studies are discussed.

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases

We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.

Weighting, Informativeness and Causal Inference, with an Application to Rainfall Enhancement

Abstract Sampling is informative when probabilities of sample inclusion depend on unknown variables that are correlated with a response variable of interest. When sample inclusion probabilities are available, inverse probability weighting can be used to account for informative sampling in such a situation, although usually at the cost of less precise inference. This paper reviews two important research contributions by Chris Skinner that modify these weights to reduce their variability while at the same time retaining consistency of the weighted estimators. In some cases, however, sample inclusion probabilities are not known, and are estimated as propensity scores. This is often the situation in causal analysis, and double robust methods that protect against the resulting misspecification of the sampling process have been the focus of much recent research. In this paper we propose two model-assisted modifications to the popular inverse propensity score weighted estimator of an average treatment effect, and then illustrate their use in a causal analysis of a rainfall enhancement experiment that was carried out in Oman between 2013 and 2018.

Handling multicollinearity in quantile regression through the use of principal component regression

In many fields of applications, linear regression is the most widely used statistical method to analyze the effect of a set of explanatory variables on a response variable of interest. Classical least squares regression focuses on the conditional mean of the response, while quantile regression extends the view to conditional quantiles. Quantile regression is very convenient, whereas classical parametric assumptions do not hold and/or when relevant information lies in the tails and therefore the interest is in modeling the conditional distribution of the response at locations different from the mean. A situation common to most regression applications is the presence of strong correlations between predictors. This leads to the well-known problem of collinearity. While the effects of collinearity on least squares estimates are well investigated, this is not the case for quantile regression estimates. This paper aims to explore the collinearity problem in quantile regression. First, a simulation study analyses the problem concerning different degrees of collinearity and various response distributions. Then the paper proposes using regression on latent components as a possible solution to collinearity in quantile regression. Finally, a case study shows the assessment of the quality of service in the presence of highly correlated predictors.

A finite mixture mixed proportion regression model for classification problems in longitudinal voting data

Continuous clustered proportion data often arise in various areas of the social and political sciences where the response variable of interest is a proportion (or percentage). An example is the behavior of the proportion of voters favorable to a political party in municipalities (or cities) of a country over time. This behavior can be different depending on the region of the country, giving rise to groups (or clusters) with similar profiles. For this kind of data, we propose a finite mixture of a random effects regression model based on the L-Logistic distribution. A Markov chain Monte Carlo algorithm is tailored to obtain posterior distributions of the unknown quantities of interest through a Bayesian approach. To illustrate the proposed method, with emphasis on analysis of clusters, we analyze the proportion of votes for a political party in presidential elections in different municipalities observed over time, and then identify groups according to electoral behavior at different levels of favorable votes.

New Metrics for Assessing the State Performance in Combating the COVID-19 Pandemic.

Previous research has noted that many factors greatly influence the spread of COVID‐19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unobserved heterogeneity. Most contemporary COVID‐19 related research has focused on modeling the relationship between explicitly measurable factors and the response variable of interest (such as the infection rate or the death rate). The infection rate is a commonly used metric for evaluating disease progression and a state's mitigation efforts. Because unobservable sources of heterogeneity cannot be measured directly, it is hard to incorporate them into the quantitative assessment and decision‐making process. In this study, we propose new metrics to study a state's performance by adjusting the measurable county‐level covariates and unobservable state‐level heterogeneity through random effects. A hierarchical linear model (HLM) is postulated, and we calculate two model‐based metrics—the standardized infection ratio (SDIR) and the adjusted infection rate (AIR). This analysis highlights certain time periods when the infection rate for a state was high while their SDIR was low and vice versa. We show that trends in these metrics can give insight into certain aspects of a state's performance. As each state continues to develop their individualized COVID‐19 mitigation strategy and ultimately works to improve their performance, the SDIR and AIR may help supplement the crude infection rate metric to provide a more thorough understanding of a state's performance.

A focused simulation-based optimization of print time and material usage with respect to orientation, layer height and support settings for multi-pathological anatomical models in inverted vat photopolymerization 3D printing

Background3D printing of anatomical models requires multi-factorial decision making for optimal model manufacturing. Due to the complex nature of the printing process, there are frequently multiple potentialities based on the desired end goal. The task of identifying the most optimal combination of print control variables is inherently subjective and rests on sound operator intuition. This study investigates the effect of orientation, layer and support settings on print time and material usage. This study also presents a quantitative optimization framework to jointly optimize print time and material usage as a function of those settings for multi-pathological anatomical models.MethodsSeven anatomical models representing different anatomical regions (cardiovascular, abdominal, neurological and maxillofacial) were selected for this study. A reference cube was also included in the simulations. Using PreForm print preparation software the print time and material usage was simulated for each model across 4 orientations, 2 layer heights, 2 support densities and 2 support tip sizes. A 90–10 weighted optimization was performed to identify the 5 most optimal treatment combinations that resulted in the lowest print time (90% weight) and material usage (10% weight) for each model.ResultsThe 0.1 mm layer height was uniformly the most optimal setting across all models. Layer height had the largest effect on print time. Orientation had a complex effect on both print time and material usage in certain models. The support density and the support tip size settings were found to have a relatively minor effect on both print time and material usage. Hollow models had a larger support volume fraction compared to solid models.ConclusionsThe quantitative optimization framework identified the 5 most optimal treatment combinations for each model using a 90–10 weighting for print time and material usage. The presented optimization framework could be adapted based on the individual circumstance of each 3D printing lab and/or to potentially incorporate additional response variables of interest.

Tree-aggregated predictive modeling of microbiome data

Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest.

Improved Tolerance Limits by Combining Analytical and Experimental Data: An Information Integration Methodology

We propose a coherent methodology for integrating different sources of information on a response variable of interest, in order to accurately predict percentiles of its distribution. Under the assumption that one of the sources is more reliable than the other(s), the approach combines factors formed from the data into an additive linear regression model. Quantile regression, designed for quantifying the goodness of fit precisely at a desired quantile, is used as the optimality criterion in model-fitting. Asymptotic confidence interval construction methods for the percentiles are adopted to compute statistical tolerance limits for the response. The approach is demonstrated on a materials science case study that pools together information on failure load from physical tests and computer model predictions. A small simulation study assesses the precision of the inferences. The methodology gives plausible percentile estimates. Resulting tolerance limits are close to nominal coverage probability levels.

Spatially clustered regression

Spatial regression and geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a region. In contrast, relationships between response and auxiliary variables are expected to exhibit complex spatial patterns in many applications. This paper proposes a new approach for spatial regression, called spatially clustered regression, to estimate possibly clustered spatial patterns of the relationships. We combine K-means-based clustering formulation and penalty function motivated from a spatial process known as Potts model for encouraging similar clustering in neighboring locations. We provide a simple iterative algorithm to fit the proposed method, scalable for large spatial datasets. Through simulation studies, the proposed method demonstrates its superior performance to existing methods even under the true structure does not admit spatial clustering. Finally, the proposed method is applied to crime event data in Tokyo and produces interpretable results for spatial patterns. The R code is available at https://github.com/sshonosuke/SCR.

Exploring for Municipality-Level Socioeconomic Variables Related to Zika Virus Incidence in Colombia.

Colombia experienced an outbreak of Zika virus infection during September 2015 until July 2016. This study aimed to identify the socioeconomic factors that at the municipality level correlate with this outbreak and therefore could have influenced its incidence. An analysis of publicly available, municipality-aggregated data related to eight potential explanatory socioeconomic variables was conducted. These variables are school dropout, low energy strata, social security system, savings capacity, tax, resources, investment, and debt. The response variable of interest in this study is the number of reported cases of Zika virus infection per people (projected) per square kilometer. Binomial regression models were performed. Results show that the best predictor variables of Zika virus occurrence, assuming an expected inverse relationship with socioeconomic status, are “school”, “energy”, and “savings”. Contrary to expectations, proxies of socioeconomic status such as “investment”, “tax”, and “resources” were associated with an increase in the occurrence of Zika virus infection, while no association was detected for “social security” and “debt”. Energy stratification, school dropout rate, and the percentage of the municipality’s income that is saved conformed to the hypothesized inverse relationship between socioeconomic standing and Zika occurrence. As such, this study suggests these factors should be considered in Zika risk modeling.

Abstract C020: Regularized multiple mediation analysis for big data set—With an application to explore racial disparity in breast cancer survival

Abstract Mediation effect refers to the effect conveyed by a third variable, the mediator, to an observed relationship between an exposure and a response variable of interest. Mediation analysis is used widely in fields such as social science, prevention study, behavior research, and epidemiology. For example, it has been well established that low socioeconomic status is associated with poor health status. To reduce this health disparity, investigators would need to quantify the effects, called mediation effects, from different risk factors that intervene in the relationship between the socioeconomic status and health outcomes, so that efficient interventions can be carried out. The general multiple mediation analysis method, proposed by Yu et al., improved traditional methods (e.g., estimation of natural and controlled direct effects) to enable consideration of multiple mediators simultaneously and the use of linear and nonlinear predictive models for estimating mediation effects. A big data set includes a large number of variables. Within big data mediation analysis, the number of potential mediators is big. For example, in explaining the racial disparity in breast cancer survival, there are a lot of risk factors that need to be considered such as individual behaviors, environmental factors, social-economic status, treatment effects, diagnosis characters, and even gene expression variables. In this study, we propose to use a regularized mediation analysis method that identifies significant mediators and estimates their indirect effects simultaneously. Breast cancer is the most common cancer and the second leading cause of cancer death among women of all races. Despite the improvement of survival rates of breast cancer in the U.S., a significant difference between white and black women remains. Due to limitations of current analytic methods and the lack of comprehensive data sets, researchers have not been able to differentiate the relative effect that each factor contributes to the overall racial disparity. We use the CDC-funded Patterns of Care study and Enhancing Cancer Registry Data for Comparative Effectiveness Research study to examine the determinants of racial disparities in breast cancer survival using the propose regularized multiple mediation analysis. Using the proposed method, we identified important factors that explain the racial disparity in breast cancer survival, which includes the cancer stage-related variables (stage, tumor size, lymph nodes involvement, extension, and tumor size), molecular subtype, treatment methods such as surgery and hormonal therapy, and tumor grade. Age also contributed to the racial differences, but in an opposite direction. Black patients are more likely to be diagnosed at a younger age, which is related with better survival outcomes. Overall, we found that all racial disparity in survival among Louisiana breast cancer patients was explained by factors included in the study. Citation Format: Qingzhao Yu, Lu Zhang, Meichin Hsieh, Xiaocheng Wu, Richard A Scribner, Bin Li. Regularized multiple mediation analysis for big data set—With an application to explore racial disparity in breast cancer survival [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr C020.