scholarly journals Properties of the full random effect modelling approach with missing covariates

2019 ◽  
Author(s):  
Joakim Nyberg ◽  
E. Niclas Jonsson ◽  
Mats O. Karlsson ◽  
Jonas Häggström ◽  
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Simulated Data ◽  
Reference Method ◽  
Parameter Estimates ◽  
Full Model ◽  
Covariate Data ◽  
Model Method ◽  
Covariate Information

SummaryTwo full model approaches was compared with respect to their ability to handle missing covariate information. The reference data analysis approach was the full model method in which the covariate effects are estimated conventionally using fixed effects, and missing covariate data is imputed with the median of the non-missing covariate information. This approach was compared to a novel full model method which treats the covariate data as observed data and estimates the covariates as random effects. A consequence of this way of handling the covariates is that no covariate imputation is required and that any missingness in the covariates is handled implicitly. The comparison between the two analysis methods was based on simulated data from a model of height for age z-scores as a function of age. Data was simulated with increasing degrees of randomly missing covariate information (0-90%) and analyzed using each of the two analysis approaches. Not surprisingly, the precision in the parameter estimates from both methods decreased with increasing degrees of missing covariate information. However, while the bias in the parameter estimates increased in a similar fashion for the reference method, the full random effects approach provided unbiased estimates for all degrees of covariate missingness.

scholarly journals Including random effects in statistical models in ecology: fewer than five levels?

2021 ◽  
Author(s):  
Dylan G.E. Gomes
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Linear Models ◽  
Random Effect ◽  
Mixed Effects ◽  
Parameter Estimates ◽  
Type I ◽  
Type I Errors ◽  
Ecological Sites ◽  
Generalized Linear Mixed Effects

AbstractAs generalized linear mixed-effects models (GLMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common guideline is that one needs at least five levels of a random effect. Having such few levels makes the estimation of the variance of random effects terms (such as ecological sites, individuals, or populations) difficult, but it need not muddy one’s ability to estimate fixed effects terms – which are often of primary interest in ecology. Here, I simulate ecological datasets and fit simple models and show that having too few random effects terms does not influence the parameter estimates or uncertainty around those estimates for fixed effects terms. Thus, it should be acceptable to use fewer levels of random effects if one is not interested in making inference about the random effects terms (i.e. they are ‘nuisance’ parameters used to group non-independent data). I also use simulations to assess the potential for pseudoreplication in (generalized) linear models (LMs), when random effects are explicitly ignored and find that LMs do not show increased type-I errors compared to their mixed-effects model counterparts. Instead, LM uncertainty (and p values) appears to be more conservative in an analysis with a real ecological dataset presented here. These results challenge the view that it is never appropriate to model random effects terms with fewer than five levels – specifically when inference is not being made for the random effects, but suggest that in simple cases LMs might be robust to ignored random effects terms. Given the widespread accessibility of GLMMs in ecology and evolution, future simulation studies and further assessments of these statistical methods are necessary to understand the consequences of both violating and blindly following simple guidelines.

scholarly journals A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality

2012 ◽  
Vol 69 (11) ◽  
pp. 1881-1893 ◽  
Author(s):  
Verena M. Trenkel ◽  
Mark V. Bravington ◽  
Pascal Lorance
Keyword(s):  
Population Dynamics ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Total Mortality ◽  
Estimation Bias ◽  
Effective Sample Size ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Study Parameter

Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

scholarly journals The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 48-76
Author(s):  
Freddy Hernández ◽  
Viviana Giampaoli
Keyword(s):  
Weibull Distribution ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Mixed Model ◽  
Random Effect ◽  
Estimation Procedure ◽  
Weibull Regression ◽  
Two Parameters ◽  
The Impact

Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

PSIII-8 Genetic Parameter Estimates for Bull Prolificacy and Its Relationship with Scrotal Circumference in a Commercial Beef Cattle Population

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 158-159
Author(s):  
Chad A Russell ◽  
E J Pollak ◽  
Matthew L Spangler
Keyword(s):  
Beef Cattle ◽  
Breeding Season ◽  
Random Effects ◽  
Fixed Effects ◽  
Genetic Parameter ◽  
Additive Genetic Effect ◽  
Parameter Estimates ◽  
Contemporary Group ◽  
Scrotal Circumference ◽  
Heritability Estimates

Abstract The commercial beef cattle industry relies heavily on the use of natural service sires. Either due to the size of breeding herds or to safe-guard against injury during the breeding season, multiple-sire breeding pastures are utilized. Although each bull might be given an equal opportunity to produce offspring, evidence suggest that there is substantial variation in the number of calves sired by each bull in a breeding pasture. DNA-based paternity assignment enables correct assignment of calves to their respective sires in multi-sire pastures and presents an opportunity to investigate the degree to which this trait complex is under genetic control. Field data from a large commercial ranch were used to estimate genetic parameters for calf count (CC; n=623) and yearling scrotal circumference (SC; n=1962) using univariate and bivariate animal models. Average CC and SC were 12.1±11.1 calves and 35.4±2.30 cm, respectively. Average number breeding seasons per bull and bulls per contemporary group were 1.40 and 24.9, respectively. The model for CC included fixed effects of age during the breeding season (in years) and contemporary group (concatenation of breeding pasture and year). Random effects included additive genetic and permanent environmental effects, and a residual. The model for SC included fixed effects of age (in days) and contemporary group (concatenation of month and year of measurement). Random effects included an additive genetic effect and a residual. Univariate model heritability estimates for CC and SC were 0.237±0.156 and 0.456±0.072, respectively. Similarly, the bivariate model resulted in heritability estimates for CC and SC of 0.240±0.155 and 0.461±0.072, respectively. Repeatability estimates for CC from univariate and bivariate models were 0.517±0.054 and 0.518±0.053, respectively. The estimate of genetic correlation between CC and SC was 0.270±0.220. Parameter estimates suggest that both CC and SC would respond favorably to selection and that CC is moderately repeatable.

scholarly journals A two-stage biomass random effects model for stock assessment without catches: What can be estimated using only biomass survey indices?

2008 ◽  
Vol 65 (6) ◽  
pp. 1024-1035 ◽  
Author(s):  
Verena M. Trenkel
Keyword(s):  
Random Effects ◽  
Stock Assessment ◽  
Random Effect ◽  
Simulated Data ◽  
Fish Stock ◽  
Model Parameters ◽  
Biomass Growth ◽  
Two Stage ◽  
Commercial Catch ◽  
Nested Models

A simple two-stage biomass random effects population dynamics model is presented for carrying out fish stock assessments based on survey indices using no commercial catch information. Recruitment and biomass growth are modelled as random effects, reducing the number of model parameters while maintaining model flexibility. No assumptions regarding natural mortality rates are required. The performance of the method was evaluated using simulated data with emphasis on identifying parameter redundancy, which showed that the variance of the biomass growth random effect might only be estimable if large (>0.2). The full and two nested models were fitted to European anchovy ( Engraulis encrasicolus ) in the Bay of Biscay using two survey series. The best-fitting model had fixed biomass growth and random recruitment following a lognormal distribution.

scholarly journals Impact of Full IFRS, Accounting Standards for SMES and Company Demographics on Firms’ Return on Asset and Return on Equity using Panel Data Regression

2019 ◽  
Vol 8 (6S) ◽  
pp. 589-601 ◽  
Keyword(s):  
Least Squares ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Ordinary Least Squares ◽  
Lagrangian Multiplier ◽  
Random Effects Model ◽  
Fixed Effects Model ◽  
Real Property ◽  
The Impact

Due to globalization, markets are becoming more interconnected as the companies are engaged in doing cross-border offerings. Currently, competitions are intensified because Domestic organizations discover themselves competing with each nearby opposite numbers and worldwide companies. But one component that hinders SMEs is the need for reliable and similar monetary data. According to Abarca (2014), adoption of a high-quality and consistent set of accounting requirements is critical so as for the businesses to remain competitive in ASEAN member states. This paper ambitions to answer the query, what modified into the extent of the impact of compliance with full IFRS and IFRS for SMEs on profitability of agencies belong to real property enterprise? This paper moreover sought to decide whether there may be a sizeable distinction among the groups’ compliance with the overall PFRS and the PFRS for SMEs and to determine whether or now not there is a massive distinction among the companies’ financial normal overall performance earlier than and after the adoption of the PFRS for SMEs.Paired T-test have become employed in case you need to determine whether there is a big distinction between the agencies’ compliance with the entire PFRS and the PFRS for SMEs and to decide whether or not there may be a big difference some of the groups’ monetary performance earlier than and after the adoption of the PFRS for SMEs. Using STATA, the great appropriate version for every economic ratio on the subject of degree of compliance emerge as determined on. First, take a look at parm command became used to find out which most of the Least Squares Dummy Variable Regression Modes (LSDV1, LSDV2, LSDV3) underneath the Fixed Effects Model is the ideal version. Afterwards, Hausman Fixed Random Test changed into used to pick out out which is more suitable amongst Fixed Effects Model and Random Effects Model. If Fixed Effects Model modified into the more appropriate one, the Wald’s test turn out to be used to determine the best version among Fixed Effects Model and Ordinary Least Squares Model. On the alternative hand, if Random Effects Model became the more suitable one, the Breusch and Pagan Lagrangian Multiplier Test for Random Effect have become used to decide the satisfactory version amongst Random Effects Model and Ordinary Least Squares. Moreover, if Ordinary Least Squares became the splendid model, it is going to be in addition tested to check for heteroscedasticity and multicollinearity. White’s test became used to check for heterescedasticity and Variance Inflation Factor have become used to test if multicollinearity is gift. The results display that the adoption of PFRS for SMEs stepped forward the compliance of Philippine real property SMEs. However, no vast alternate became said inside the financial average performance of those companies (as measured with the resource of cross back on assets and go back on equity). This was further supported by the results of the panel regression. This means that despite having a relatively

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.

scholarly journals Estimated Risk for Insulin Dose Error Among Hospital Patients Due to Glucose Meter Hematocrit Bias in 2020

2020 ◽  
Vol 144 (10) ◽  
pp. 1204-1208
Author(s):  
Mark Inman ◽  
Andrew W. Lyon ◽  
Oliver A. S. Lyon ◽  
Martha E. Lyon
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Insulin Dose ◽  
Reference Method ◽  
Hospital Inpatient ◽  
Dose Error ◽  
Glucose Testing ◽  
Dosing Error ◽  
Fixed And Random Effects ◽  
Glucose Meter

Context.— Glycemic control requires accurate blood glucose testing. The extent of hematocrit interference is difficult to assess to assure quality patient care. Objective.— To predict the effect of patient hematocrit on the performance of a glucose meter and its corresponding impact on insulin-dosing error. Design.— Multilevel mixed regression was conducted to assess the extent that patient hematocrit influences Roche Accu-Chek Inform II glucose meters, using the Radiometer ABL 837 as a reference method collected during validation of 35 new meters. Regression coefficients of fixed effects for reference glucose, hematocrit, an interaction term, and random error were applied to 4 months of patient reference method results extracted from the laboratory information system. A hospital inpatient insulin dose algorithm was used to determine the frequency of insulin dose error between reference glucose and meter glucose results. Results.— Fixed effects regression for method and hematocrit predicted biases to glucose meter results that met the “95% within ±12%” for the US Food and Drug Administration goal, but combinations of fixed and random effects exceeded that target in emergency and hospital inpatient units. Insulin dose errors were predicted from the meter results. Twenty-eight percent of intensive care unit, 20.8% of hospital inpatient, and 17.7% of emergency department results were predicted to trigger a ±1 insulin dose error by fixed and random effects. Conclusions.— The current extent of hematocrit interference on glucose meter performance is anticipated to cause insulin error by 1-dose category, which is likely associated with low patient risk.

scholarly journals A review and meta-analysis of red and processed meat consumption and breast cancer

2010 ◽  
Vol 23 (2) ◽  
pp. 349-365 ◽  
Author(s):  
Dominik D. Alexander ◽  
Libby M. Morimoto ◽  
Pamela J. Mink ◽  
Colleen A. Cushing
Keyword(s):  
Breast Cancer ◽  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Random Effect ◽  
Red Meat ◽  
Processed Meat ◽  
Random Effects Model ◽  
Fixed Effects Model ◽  
Meat Intake

The relationship between meat consumption and breast cancer has been the focus of several epidemiological investigations, yet there has been no clear scientific consensus as to whether red or processed meat intake increases the risk of breast cancer. We conducted a comprehensive meta-analysis incorporating data from several recently published prospective studies of red or processed meat intake and breast cancer. In the meta-analysis utilising data from the Pooling Project publication (includes data from eight cohorts) combined with data from nine studies published between 2004 and 2009 and one study published in 1996, the fixed-effect summary relative risk estimate (SRRE) for red meat intake (high v. low) and breast cancer was 1·02 (95 % CI 0·98, 1·07; P value for heterogeneity = 0·001) and the random-effects SRRE was 1·07 (95 % CI 0·98, 1·17). The SRRE for each 100 g increment of red meat was 1·04 (95 % CI 1·00, 1·07), based on a fixed-effects model, and 1·12 (95 % CI 1·03, 1·23) based on a random-effects model. No association was observed for each 100 g increment of red meat among premenopausal women (SRRE 1·01; 95 % CI 0·92, 1·11) but a statistically significant SRRE of 1·22 (95 % CI 1·04, 1·44) was observed among postmenopausal women using a random-effects model. However, the association for postmenopausal women was attenuated and non-significant when using a fixed-effects model (SRRE 1·03; 95 % CI 0·98, 1·08). The fixed- and random-effect SRRE for high (v. low) processed meat intake and breast cancer were 1·00 (95 % CI 0·98, 1·01; P value for heterogeneity = 0·005) and 1·08 (95 % CI 1·01, 1·16), respectively. The fixed- and random-effect SRRE for each 30 g increment of processed meat were 1·03 (95 % CI 1·00, 1·06) and 1·06 (95 % CI 0·99, 1·14), respectively. Overall, weak positive summary associations were observed across all meta-analysis models, with the majority being non-statistically significant. Heterogeneity was evident in most analyses, summary associations were sensitive to the choice of analytical model (fixed v. random effects), and publication bias appeared to have produced slightly elevated summary associations. On the basis of this quantitative assessment, red meat and processed meat intake does not appear to be independently associated with increasing the risk of breast cancer, although further investigations of potential effect modifiers, such as analyses by hormone receptor status, may provide valuable insight to potential patterns of associations.

scholarly journals Impact of Environmental Airborne Manganese Exposure on Cognitive and Motor Functions in Adults: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 18 (8) ◽  
pp. 4075
Author(s):  
Laura Ruiz-Azcona ◽  
Ignacio Fernández-Olmo ◽  
Andrea Expósito ◽  
Bohdana Markiv ◽  
María Paz-Zulueta ◽  
...  
Keyword(s):  
Systematic Review ◽  
Motor Function ◽  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Random Effect ◽  
Random Effect Model ◽  
Motor Functions ◽  
Effect Model

Background/Objective: Whether environmental exposure to Manganese (Mn) in adults is associated with poorer results in cognitive and motor function is unclear. We aimed to determine these associations through a meta-analysis of published studies. Methods: A systematic review was conducted to identify epidemiological studies on a population ≥18 years old exposed to environmental airborne Mn, and in which results on specific tests to evaluate cognitive or motor functions were reported. We consulted Medline through PubMed, Web of Science and SCOPUS databases. We also performed a manual search within the list of bibliographic references of the retrieved studies and systematic reviews. To weight Mn effects, a random effects versus fixed effect model was chosen after studying the heterogeneity of each outcome. Results. Eighteen studies met the inclusion criteria. Among them, eleven studies reported data susceptible for meta-analysis through a pooled correlation or a standardized means difference (SMD) approach between exposed and non-exposed groups. Regarding cognitive function, the results of the studies showed heterogeneity among them (I2 = 76.49%, p < 0.001). The overall effect was a statistically significant negative correlation in the random effects model (pooled r = −0.165; 95%CI: −0.214 to −0.116; p < 0.001). For SMD, the results showed a lower heterogeneity with a negative SMD that did not reach statistical significance under the fixed effects model (SMD = −0.052; 95%CI −0.108 to 0.004; p = 0.068). Regarding motor function, heterogeneity (I2 = 75%) was also observed in the correlation approach with a pooled r (random effect model) = −0.150; 95%CI: −0.219 to −0.079; p < 0.001. Moderate heterogeneity was observed according to the SMD approach (I2 = 52.28%), with a pooled SMD = −0.136; 95%CI: −0.188 to−0.084; p < 0.001, indicating worse motor function in those exposed. Conclusions: Correlation approach results support a negative effect on cognitive and motor functions (the higher the Mn levels, the poorer the scores). Regarding the SMD approach, results also support a worse cognitive and motor functions in those exposed, although only for motor function statistical significance was obtained.

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