scholarly journals A Small Area Estimation Method for Investigating the Relationship between Public Perception of Drug Problems with Neighborhood Prognostics: Trends in London between 2012 and 2019

2021 ◽  
Vol 18 (17) ◽  
pp. 9016
Author(s):  
Arun Sondhi ◽  
Alessandro Leidi ◽  
Emily Gilbert
Keyword(s):  
Public Perception ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Statistical Significance ◽  
Local Population ◽  
Random Effect ◽  
Gaussian Quadrature ◽  
Estimation Method ◽  
Likelihood Method ◽  
Drug Problems

The correlation of the public’s perception of drug problems with neighborhood characteristics has rarely been studied. The aim of this study was to investigate factors that correlate with public perceptions in London boroughs using the Mayor’s Office for Policing and Crime (MOPAC) Public Attitude Survey between 2012 and 2019. A subject-specific random effect deploying a Generalized Linear Mixed Model (GLMM) using an Adaptive Gaussian Quadrature method with 10 integration points was applied. To obtain time trends across inner and outer London areas, the GLMM was fitted using a Restricted Marginal Pseudo Likelihood method. The perception of drug problems increased with statistical significance in 17 out of 32 London boroughs between 2012 and 2019. These boroughs were geographically clustered across the north of London. Levels of deprivation, as measured by the English Index of Multiple Deprivation, as well as the percentage of local population who were non-UK-born and recorded vehicle crime rates were shown to be positively associated with the public’s perception of drug problems. Conversely, recorded burglary rate was negatively associated with the public’s perception of drug problems in their area. The public are influenced in their perception of drug problems by neighborhood factors including deprivation and visible manifestations of antisocial behavior.

scholarly journals The generalizability crisis

2020 ◽  
pp. 1-37
Author(s):  
Tal Yarkoni
Keyword(s):  
Quantitative Analysis ◽  
Statistical Inference ◽  
Random Effects ◽  
False Positive ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Procedures ◽  
Subject Variability ◽  
Replication Crisis

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same set of hypothetical observations. Here I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology—the linear mixed model—I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that whereas the "random effect" formalism is used pervasively in psychology to model inter-subject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

scholarly journals Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

2018 ◽  
Vol 147 ◽  
Author(s):  
A. Aswi ◽  
S. M. Cramb ◽  
P. Moraga ◽  
K. Mengersen
Keyword(s):  
Dengue Fever ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Random Effect ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Spatio Temporal

AbstractDengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

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.

scholarly journals PSVII-9 Effects of a multi-species direct-fed microbial on growth performance and carcass characteristics of grow-finish pigs

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 216-216
Author(s):  
Mariana Boscato Menegat ◽  
Joel M DeRouchey ◽  
Jason C Woodworth ◽  
Mike D Tokach ◽  
Steve S Dritz ◽  
...  
Keyword(s):  
Growth Performance ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Block Design ◽  
Control Diet ◽  
Random Effect ◽  
Carcass Characteristics ◽  
Experimental Unit ◽  
Feeding Duration ◽  
Corn Distillers

Abstract This study was conducted to determine the effects of a multi-species direct-fed microbial (DFM) product based on lactic acid bacteria and Bacillus subtilis on growth performance and carcass characteristics of grow-finish pigs. A total of 1,188 pigs (PIC 359 × 1050; initially 25.8 kg BW) were used in a 121-d growth trial with 27 pigs/pen and 22 pens/treatment. Pigs were allotted to treatments based on initial BW in a randomized complete block design. Treatments included a control diet and the control diet with added DFM (BiOWiSH Technologies Inc., Cincinnati, OH) included at 0.055% of the diet at the expense of corn. Diets were based on corn, distillers dried grains with solubles, and soybean meal and fed in four dietary phases. Data were analyzed using a linear mixed model (PROC GLIMMIX, SAS®) with treatment as fixed effect, block as random effect, and pen as experimental unit. Overall (d 0 to 121), pigs fed the control diet had greater ADG (P < 0.05) and final BW (P < 0.001) compared to pigs fed the DFM diet (Table 1). There was no evidence for differences (P > 0.05) in ADFI or G:F between treatments. The difference in final BW resulted in heavier (P < 0.05) HCW in control pigs compared to DFM pigs, but no evidence for differences (P > 0.05) was observed in carcass yield, backfat, loin depth, and percentage lean between treatments. In conclusion, the inclusion of this multi-species DFM in growing-finishing diets reduced ADG in this commercial study. This response could be related to inclusion rate, feeding duration, or other factors not identified in this study, warranting further research to characterize the effects on pig performance.

scholarly journals Novel Fiber Formulation Does Not Alter Cardiometabolic Profiles in Overweight Participants Consuming Low-Fiber Diets

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 757-757
Author(s):  
Jea Woo Kang ◽  
Chenghao Zhu ◽  
Christopher Rhodes ◽  
Hannah Houts ◽  
Jingyuan Zheng ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Statistical Significance ◽  
Rice Flour ◽  
Lipid Profiles ◽  
Funding Sources ◽  
Fiber Diet ◽  
High Interindividual Variability ◽  
Dietary Records ◽  
Fiber Consumption

Abstract Objectives The objective of this study was to determine whether a novel fiber formulation improves glucose, insulin, and lipid profiles in overweight men and women consuming a low fiber diet. Methods Twenty individuals were enrolled in this randomized order, placebo-controlled, cross-over study. Participants were young, healthy, overweight (BMI 23.0–32.0) and consumed &lt;15 g/day of fiber. All participants consumed the fiber and placebo supplement for a period of 4 weeks each, with a 4-week washout between intervention arms. Participants recorded their diet for 3 days using dietary records twice during each 4-week segment. They consumed either fiber and/or placebo packet containing a total of 12 g/serving per day. The Fiber and/or Placebo was given out as powder form which include mostly dietary fiber (resistant starch, fructooligosaccharide, sugarcane fiber, and inulin), rice flour, xanthan gum, and fruit powders that was mixed with water for consumption. Questionnaires, anthropometric measurements, blood draws, and stool samples were collected at each study visit. Changes in glucose, insulin, and lipid profile (total cholesterol (TC), triacylglycerols (TG), HDL-C and calculated LDL-C) were assessed using a linear mixed model. Results The mean change in fasted glucose, insulin, and lipid profiles showed a tendency to decrease in response to fiber consumption compared with the placebo but did not meet statistical significance (P = 0.29, 0.42, and 0.61) due to high interindividual variability. This clinical trial was registered at clinicaltrials.gov as NCT03785860. Conclusions Cardiometabolic profiles did not change in response to the fiber supplement. Funding Sources I would like to acknowledge Usana Health Sciences, Inc. for the support in this research.

scholarly journals Categorical Visual Score Traits of a Nellore Beef Cattle Population

2017 ◽  
Vol 9 (8) ◽  
pp. 63
Author(s):  
Jairo Azevedo Junior ◽  
Juliana Petrini ◽  
Gerson Barreto Mourão ◽  
José Bento Sterman Ferraz
Keyword(s):  
Variance Components ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Correlations ◽  
Estimation Method ◽  
Analysis Data ◽  
Suitable Model ◽  
Linear Distribution ◽  
Tropical Regions ◽  
Trait Analysis

Variance components and genetic parameters of economically relevant traits in livestock, whether continuous or categorical, can be estimated by methods computationally available providing support for the selection and mating of animals in breeding programs. The objectives of this paper were to obtain and compare the variance components estimates for visual traits under continuous or categorical distribution in single-trait analysis and their correlations with continuous productive traits in two-trait analysis. Data of conformation (CONF), precocity of fat deposition (PREC) and muscling (MUSC) visual scores evaluated at 18 months of age as well as the weight at 18 months of age (YW) were collected from animals born from 2000 to 2012, in Nellore cattle herds raised in Southeastern and Central Western tropical regions of Brazil. Methods III of Henderson, Restricted Maximum Likelihood (REML), Bayesian Inference and generalized linear mixed model (GLMM) were tested. Variance components obtained from single-trait analysis were similar to those obtained from two-trait analysis. The estimates of heritability (h2) for the visual scores ranged from 0.1081 to 0.2190. Heritability estimates for traits evaluated by visual scores have moderate to high magnitude justifying the inclusion of visual scores as selection criteria in animal breeding and the selection of animals with higher scores for mating. High genetic correlations between yearling weight and morphological traits were verified. For visual scores of conformation, precocity and muscling, the most suitable model based on one-trait or two-trait analyses considered an animal model, a linear distribution of the data and the estimation method of the components of (co)variance based on Bayesian methodology.

A threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on longitudinal outcomes and a continuous covariate

2020 ◽  
Vol 29 (10) ◽  
pp. 2919-2931
Author(s):  
Xinyi Ge ◽  
Yingwei Peng ◽  
Dongsheng Tu
Keyword(s):  
Clinical Trial ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Likelihood Function ◽  
Specific Treatment ◽  
Likelihood Method ◽  
Simulation Studies ◽  
Longitudinal Outcomes ◽  
Continuous Covariate

Identification of a subset of patients who may be sensitive to a specific treatment is an important problem in clinical trials. In this paper, we consider the case where the treatment effect is measured by longitudinal outcomes, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. A threshold linear mixed model is introduced, and a smoothing maximum likelihood method is proposed to obtain the estimation of the parameters in the model. Broyden-Fletcher-Goldfarb-Shanno algorithm is employed to maximize the proposed smoothing likelihood function. The proposed procedure is evaluated through simulation studies and application to the analysis of data from a randomized clinical trial on patients with advanced colorectal cancer.

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.

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