Random Effects and Fixed Effects Fallacy

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.

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Understanding, Choosing, and Unifying Multilevel and Fixed Effect Approaches

Political Analysis ◽

10.1017/pan.2020.41 ◽

2020 ◽

pp. 1-20

Author(s):

Chad Hazlett ◽

Leonard Wainstein

Keyword(s):

Random Effects ◽

Fixed Effects ◽

Multilevel Models ◽

Standard Errors ◽

Point Estimate ◽

Fe Model ◽

Grouped Data ◽

Coefficient Estimates ◽

Political Science Education ◽

Robust Standard Errors

Abstract When working with grouped data, investigators may choose between “fixed effects” models (FE) with specialized (e.g., cluster-robust) standard errors, or “multilevel models” (MLMs) employing “random effects.” We review the claims given in published works regarding this choice, then clarify how these approaches work and compare by showing that: (i) random effects employed in MLMs are simply “regularized” fixed effects; (ii) unmodified MLMs are consequently susceptible to bias—but there is a longstanding remedy; and (iii) the “default” MLM standard errors rely on narrow assumptions that can lead to undercoverage in many settings. Our review of over 100 papers using MLM in political science, education, and sociology show that these “known” concerns have been widely ignored in practice. We describe how to debias MLM’s coefficient estimates, and provide an option to more flexibly estimate their standard errors. Most illuminating, once MLMs are adjusted in these two ways the point estimate and standard error for the target coefficient are exactly equal to those of the analogous FE model with cluster-robust standard errors. For investigators working with observational data and who are interested only in inference on the target coefficient, either approach is equally appropriate and preferable to uncorrected MLM.

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Association of bisphenol A with puberty timing: a meta-analysis

Reviews on Environmental Health ◽

10.1515/reveh-2020-0091 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Hui Meng ◽

Yunping Zhou ◽

Yunxia Jiang

Keyword(s):

Bisphenol A ◽

Confidence Intervals ◽

Random Effects ◽

Strong Correlation ◽

Fixed Effects ◽

Meta Analysis ◽

Random Effects Models ◽

Study Heterogeneity ◽

The Relationship ◽

Prevalence Ratios

AbstractObjectivesThe results of existing studies on bisphenol A (BPA) and puberty timing did not reach a consensus. Thereby we performed this meta-analytic study to explore the association between BPA exposure in urine and puberty timing.MethodsMeta-analysis of the pooled odds ratios (OR), prevalence ratios (PR) or hazards ratios (HR) with 95% confidence intervals (CI) were calculated and estimated using fixed-effects or random-effects models based on between-study heterogeneity.ResultsA total of 10 studies involving 5621 subjects were finally included. The meta-analysis showed that BPA exposure was weakly associated with thelarche (PR: 0.96, 95% CI: 0.93–0.99), while no association was found between BPA exposure and menarche (HR: 0.99, 95% CI: 0.89–1.12; OR: 1.02, 95% CI: 0.73–1.43), and pubarche (OR: 1.00, 95% CI: 0.79–1.26; PR: 1.00, 95% CI: 0.95–1.05).ConclusionsThere was no strong correlation between BPA exposure and puberty timing. Further studies with large sample sizes are needed to verify the relationship between BPA and puberty timing.

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1 Effect of Conditioning, Rest, and Post-rest Transport Duration on Welfare Indicators of Beef Cattle Transported by Road

Journal of Animal Science ◽

10.1093/jas/skab235.003 ◽

2021 ◽

Vol 99 (Supplement_3) ◽

pp. 2-3

Author(s):

Daniela M Melendez ◽

Sonia Marti ◽

Timothy D Schwinghamer ◽

Derek B Haley ◽

Karen S Schwartzkopf-Genswein

Keyword(s):

Beef Cattle ◽

Factorial Design ◽

Random Effects ◽

Fixed Effects ◽

Rest Period ◽

Flight Speed ◽

Beef Calves ◽

Welfare Indicators ◽

Preliminary Results ◽

And Performance

Abstract The aim of this study was to assess the effects of conditioning, rest, and post-rest transport duration on welfare indicators of 6–7 mo old beef calves. Three hundred and twenty-eight weaned calves (237 ± 29.7 kg BW) were randomly assigned to a 2 × 2 × 2 nested factorial design: conditioning, conditioned (C) or non-conditioned (N); rest, 0 (R0) or 8 (R8) h, and post-rest transport, 4 (T4) or 15 (T15) h. Calves were sampled prior to the first loading (L1), after 20h of transport, prior to and after the additional 4 or 15-h transport, and at 1, 2, 3, 5, 14, and 28 d after transport ended. Data were analyzed using the GLIMMIX procedure of SAS. Fixed effects included conditioning, transport and time nested within rest period, while random effects included animal and pen. Greater shrink (P < 0.01) was observed in C than N calves after the initial 20-h transport. The N calves had greater (P < 0.01) ADG than C calves between L1 and d 5, while C had greater (P < 0.01) ADG than N calves between 14 and 28 d. L-lactate concentrations and flight speed were greater (P ≤ 0.05) in C than N calves between L1 and d 5. The R8-T4 calves had greater (P < 0.01) ADG than R8-T15 calves between L1 and d 5. The R0-T4 calves had greater (P = 0.02) L-lactate concentrations than R0-T15 and R8-T4 calves on d 1. The R0 calves had greater (P < 0.01) ADG than R8 calves between 14 and 28 d. Preliminary results show physiological, behavioral, and performance differences across treatments, however, additional indicators are required to accurately assess the effect of conditioning, rest, and post-rest transport durations on calf welfare.

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The relationship between migraine and infant colic: A systematic review and meta-analysis

Cephalalgia ◽

10.1177/0333102414534326 ◽

2014 ◽

Vol 35 (1) ◽

pp. 63-72 ◽

Cited By ~ 18

Author(s):

Amy A Gelfand ◽

Peter J Goadsby ◽

I Elaine Allen

Keyword(s):

Systematic Review ◽

Random Effects ◽

Odds Ratio ◽

Fixed Effects ◽

Meta Analysis ◽

Random Effects Model ◽

Fixed Effects Model ◽

Primary Analysis ◽

Infant Colic ◽

The Relationship

Context Infant colic is a common and distressing disorder of early infancy. Its etiology is unknown, making treatment challenging. Several articles have suggested a link to migraine. Objective The objective of this article was to perform a systematic review and, if appropriate, a meta-analysis of the studies on the relationship between infant colic and migraine. Data sources Studies were identified by searching PubMed and ScienceDirect and by hand-searching references and conference proceedings. Study selection For the primary analysis, studies specifically designed to measure the association between colic and migraine were included. For the secondary analysis, studies that collected data on colic and migraine but were designed for another primary research question were also included. Data extraction Data were abstracted from the original studies, through communication with study authors, or both. Two authors independently abstracted data. Main outcomes and measures The main outcome measure was the association between infant colic and migraine using both a fixed-effects model and a more conservative random-effects model. Results Three studies were included in the primary analysis; the odds ratio for the association between migraine and infant colic was 6.5 (4.6–8.9, p < 0.001) for the fixed-effects model and 5.6 (3.3–9.5, p = 0.004) for the random-effects model. In a sensitivity analysis wherein the study with the largest effect size was removed, the odds ratio was 3.6 (95% CI 1.7–7.6, p = 0.001) for both the fixed-effects model and random-effects model. Conclusions In this meta-analysis, infant colic was associated with increased odds of migraine. If infant colic is a migrainous disorder, this would have important implications for treatment. The main limitation of this meta-analysis was the relatively small number of studies included.

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Selection of a suitable data set and model for the genetic evaluation of the linear description of conformation and type description in Old Kladruber horses

Archives Animal Breeding ◽

10.5194/aab-55-105-2012 ◽

2012 ◽

Vol 55 (2) ◽

pp. 105-112

Author(s):

L. Vostrý ◽

K. Mach ◽

J. Přibyl

Keyword(s):

Random Effects ◽

Fixed Effects ◽

Genetic Parameters ◽

Suitable Model ◽

Residual Variance ◽

Linear Type ◽

Colour Variant ◽

Repeated Evaluation ◽

Estimation Of Genetic Parameters ◽

Selection Of

Abstract. The objective of this paper was to select a suitable data subset and statistical model for the estimation of genetic parameters for 36 traits of the linear type in 977 Old Kladruber horses. Two subsets were tested to identify a suitable subset for analysis. One subset included repeated evaluation of certain individuals, whereas the other did not. The most suitable subset included repeated evaluation (n=1 390). The selection of a suitable model was made from 4 candidate models. These models comprised a number of random effects (direct individual effect and animal permanent environmental effect of the animal) and a number of fixed effects (colour variant, stud, colour variant × stud interaction, sex, age at description, year of birth, year of description). The model was selected based on the Akaike information criterion (AIC, Akaike 1974), residual variance and heritability coefficient. The model that included colour variant, stud, colour variant × stud interaction, sex, age at description, and year of description as fixed effects and direct individual and animal permanent environment as random effects was the most suitable model for the estimation of genetic parameters and for the subsequent estimation of breeding values.

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P075 Travel or cheers? Examining the drivers and mechanisms of home court advantage in the 2020/2021 NBA regular season

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.119 ◽

2021 ◽

Vol 2 (Supplement_1) ◽

pp. A45-A45

Author(s):

J Leota ◽

D Hoffman ◽

L Mascaro ◽

M Czeisler ◽

K Nash ◽

...

Keyword(s):

Random Effects ◽

Mixed Models ◽

Fixed Effects ◽

Natural Experiment ◽

National Basketball Association ◽

Home Team ◽

The Road ◽

On The Road ◽

The Impact ◽

The Relationship

Abstract Introduction Home court advantage (HCA) in the National Basketball Association (NBA) is well-documented, yet the co-occurring drivers responsible for this advantage have proven difficult to examine in isolation. The Coronavirus disease (COVID-19) pandemic resulted in the elimination of crowds in ~50% of games during the 2020/2021 NBA season, whereas travel remained unchanged. Using this ‘natural experiment’, we investigated the impact of crowds and travel-related sleep and circadian disruption on NBA HCA. Methods 1080 games from the 2020/2021 NBA regular season were analyzed using mixed models (fixed effects: crowds, travel; random effects: team, opponent). Results In games with crowds, home teams won 58.65% of the time and outrebounded (M=2.28) and outscored (M=2.18) their opponents. In games without crowds, home teams won significantly less (50.60%, p = .01) and were outrebounded (M=-0.41, p < .001) and outscored (M=-0.13, p < .05) by their opponents. Further, the increase in home rebound margin fully mediated the relationship between crowds and home points margin (p < .001). No significant sleep or circadian effects were observed. Discussion Taken together, these results suggest that HCA in the 2020/2021 NBA season was predominately driven by the presence of crowds and their influence on the effort exerted by the home team to rebound the ball. Moreover, we speculate that the strict NBA COVID-19 policies may have mitigated the travel-related sleep and circadian effects on the road team. These findings are of considerable significance to a domain wherein marginal gains can have immense competitive, financial, and even historical consequences.

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

Stats ◽

10.3390/stats1010005 ◽

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.

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