scholarly journals Causal Graphical Views of Fixed Effects and Random Effects Models

2019 ◽  
Author(s):  
Yongnam Kim ◽  
Peter Steiner
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Controlled Trial ◽  
Unmeasured Confounding ◽  
Random Effects Models ◽  
Deviation Score ◽  
Confounding Bias ◽  
Causal Graphs ◽  
Time Invariant ◽  
Linear Setting

Despite the long-standing discussion on fixed effects (FE) and random effects (RE) models, how and under which conditions both methods can eliminate unmeasured confounding bias have not yet been widely understood in practice. Using a simple pretest-posttest design in a linear setting, this article translates the conventional algebraic formalization of FE and RE models into causal graphs and provides intuitively accessible graphical explanations about their data-generating and bias-removing processes. The proposed causal graphs highlight that FE and RE models consider different data-generating models. RE models presume a data-generating model that is identical to a randomized controlled trial while FE models allow for unobserved time-invariant treatment-outcome confounding. Augmenting regular causal graphs that describe data-generating processes by adding the computational structures of FE and RE estimators, the article visualizes how FE estimators (gain score and deviation score estimators) and RE estimators (quasi-deviation score estimator) offset unmeasured confounding bias. In contrast to standard regression or matching estimators that reduce confounding bias by blocking non-causal paths via conditioning, FE and RE estimators offset confounding bias by deliberately creating new non-causal paths and associations of opposite sign. Though FE and RE estimators are similar in their bias-offsetting mechanisms, the augmented graphs reveal their subtle differences that can result in different biases in observational studies.

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

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.

Abstract WP65: Low-Versus Standard-Dose Alteplase for AcuteIschemic Strokes Within 4.5 Hours: An updated Meta Analysis

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Huaguang Zheng ◽  
Xu Tong ◽  
Liping Liu ◽  
Zixiao Li ◽  
Xiaoling Liao ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Random Effects ◽  
Standard Dose ◽  
Controlled Trial ◽  
Meta Analysis ◽  
Tissue Type ◽  
Random Effects Models ◽  
Dose Treatment ◽  
Randomized Controlled ◽  
Type Plasminogen Activator

Background and Purpose: We performed a meta-analysis to compare the outcomes between lower dose and standard dose intravenous tissue-type plasminogen activator (tPA) for acute ischemic stroke in randomized and non- randomized controlled trials. Methods: We searched PubMed for relevant studies and calculated pooled odds ratios (ORs) using random effects models.The primary endpoint was good functional outcome[modified Rankin Scale (mRS) of 0-1] at 3 month after stroke onset. Other major end points were all-cause mortality and symptomatic intracerebral haemorrhage (sICH). Results: From 2010 to 2016, 7 Cohort studies and 1 randomized controlled trial (ENCHANTED trial) were pooled in meta-analysis. The lower tPA strategy was likely to be less effective than the standard dose treatment (OR=0.87; 95% confidence interval [CI], 0.73-1.04, P=0.136; I 2 =47.9%, P=0.044 in random effects models and OR=0.88; 95% CI 0.88-0.98,P=0.016 ;I 2 =0.0%, P=0.693 in non- random effects models after 2 cohort studies were excluded due to heterogeneity). No difference was found for mortality at 90 days (OR=0.87; 95% CI 0.74-1.03, P=0.102 ;I 2 =0.0%, P=0.635 in non-random effects models)and sICH (OR=1.12; 95% CI 0.68-1.83,P=0.659; I 2 =57.6%, P=0.016 in random effects models and OR=1.23; 95% CI 0.92-1.65, P=0.168; I 2 =0.0%, P=0.547 in non-random effects models after 2 cohort studies were excluded due to heterogeneity ) between lower tPA group and standard dose . Conclusions: The low-dose alteplase strategy was less effective comparable to the standard-dose treatment .The safety was similar between the two strategies.

scholarly journals Metaanalysis of the relationship between violent video game play and physical aggression over time

2018 ◽  
Vol 115 (40) ◽  
pp. 9882-9888 ◽  
Author(s):  
Anna T. Prescott ◽  
James D. Sargent ◽  
Jay G. Hull
Keyword(s):  
Random Effects ◽  
Video Game ◽  
Physical Aggression ◽  
Fixed Effects ◽  
Time Lags ◽  
Random Effects Models ◽  
Video Game Play ◽  
Game Violence ◽  
The Relationship ◽  
Significant Moderator

To clarify and quantify the influence of video game violence (VGV) on aggressive behavior, we conducted a metaanalysis of all prospective studies to date that assessed the relation between exposure to VGV and subsequent overt physical aggression. The search strategy identified 24 studies with over 17,000 participants and time lags ranging from 3 months to 4 years. The samples comprised various nationalities and ethnicities with mean ages from 9 to 19 years. For each study we obtained the standardized regression coefficient for the prospective effect of VGV on subsequent aggression, controlling for baseline aggression. VGV was related to aggression using both fixed [β = 0.113, 95% CI = (0.098, 0.128)] and random effects models [β = 0.106 (0.078, 0.134)]. When all available covariates were included, the size of the effect remained significant for both models [β = 0.080 (0.065, 0.094) and β = 0.078 (0.053, 0.102), respectively]. No evidence of publication bias was found. Ethnicity was a statistically significant moderator for the fixed-effects models (P≤ 0.011) but not for the random-effects models. Stratified analyses indicated the effect was largest among Whites, intermediate among Asians, and nonsignificant among Hispanics. Discussion focuses on the implications of such findings for current debates regarding the effects of violent video games on physical aggression.

scholarly journals Prognostic Role of Matrix Metalloproteinases in Cervical Cancer: A Meta-Analysis

2021 ◽  
Vol 28 ◽  
pp. 107327482110337
Author(s):  
Weiwei Chen ◽  
Shenjiao Huang ◽  
Kun Shi ◽  
Lisha Yi ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Matrix Metalloproteinases ◽  
Random Effects ◽  
Fixed Effects ◽  
Prognostic Value ◽  
Meta Analysis ◽  
Free Survival ◽  
Random Effects Models ◽  
Registration Number ◽  
Meta Analyses

Objective Studies have published the association between the expression of matrix metalloproteinases (MMPs) and the outcome of cervical cancer. However, the prognostic value in cervical cancer remains controversial. This meta-analysis was conducted to evaluate the prognostic functions of MMP expression in cervical cancer. Methods A comprehensive search of PubMed, Embase, and Web of Science databases was conducted to identify the eligible studies according to defined selection and excluding criteria and analyzed according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Fixed and random effects models were evaluated through the hazard ratios (HRs) and 95% confidence intervals (CIs) to estimate the overall survival (OS), recurrence-free survival (RFS), and progress-free survival (PFS). Results A total of 18 eligible studies including 1967 patients were analyzed for prognostic value. Totally 16 selected studies including 21 tests were relevant to the cervical cancer OS, 4 studies focused on RFS, and 1 study on PFS. The combined pooled HRs and 95% CIs of OS were calculated with random-effects models (HR = 1.64, 95% CI = 1.01–2.65, P = .000). In the subgroup analysis for OS, there was no heterogeneity in MMP-2 (I2 = .0%, P = .880), MMP-1 (I2 = .0%, P = .587), and MMP-14 (I2 = 28.3%, P = .248). In MMP-7 and MMP-9, the heterogeneities were obvious (I2 = 99.2% ( P = .000) and I2 = 77.9% ( P = .000), respectively). The pooled HRs and 95% CIs of RFS were calculated with fixed-effects models (HR = 2.22, 95% CI = 1.38–3.58, P = .001) and PFS (HR = 2.29, 95% CI = 1.14–4.58, P = .035). Conclusions The results indicated that MMP overexpression was associated with shorter OS and RFS in cervical cancer patients. It suggested that MMP overexpression might be a poor prognostic marker in cervical cancer. Research Registry Registration Number: reviewregistry 1159.

scholarly journals Comparison of Competing Approaches to Analyzing Cross-Classified Data: Random Effects Models, Ordinary Least Squares, or Fixed Effects with Cluster Robust Standard Errors

2021 ◽  
Author(s):  
Young Ri Lee ◽  
James E Pustejovsky
Keyword(s):  
Least Squares ◽  
Random Effects ◽  
Fixed Effects ◽  
Good Alternative ◽  
Ordinary Least Squares ◽  
Alternative Methods ◽  
Least Squares Regression ◽  
Monte Carlo Simulation Study ◽  
Random Effects Models ◽  
Fixed Effects Regression

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in education. However, when the focus of a study is on the regression coefficients at level one rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods may be advantageous because they rely on weaker assumptions than what is required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models with crossed random effects, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated. We found that CCREM performed the best when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. FE-CRVE showed the best performance when the exogeneity assumption is violated. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt.

To What Extent Do Age, Period, and Cohort Patterns Account for Time Trends and Social Inequalities in Smoking?

2010 ◽  
Vol 56 (5) ◽  
pp. 361-371 ◽  
Author(s):  
Daniela Piontek ◽  
Ludwig Kraus ◽  
Stefanie Müller ◽  
Alexander Pabst
Keyword(s):  
Random Effects ◽  
Time Trends ◽  
Fixed Effects ◽  
Social Inequalities ◽  
Smoking Behavior ◽  
Epidemiological Survey ◽  
Control Measures ◽  
Cohort Effects ◽  
Social Disparities ◽  
Random Effects Models

Aims: This paper evaluates nonconfounded and independent age, period, and cohort effects on time trends and social disparities in smoking behavior. Methods: Data from nine waves of the German Epidemiological Survey of Substance Abuse (ESA) conducted between 1980 and 2009 were used. A total of N = 73,782 individuals aged 18 and older were included in the analyses. Using cross-classified random-effects models, fixed effects of age (level 1) as well as random effects of periods and cohorts (level 2) on 30-day smoking prevalence and average number of cigarettes per day were estimated. Analyses were stratified by socioeconomic status (SES). Results: Independent of SES, positive and curvilinear age effects emerged. Prevalence and amount of smoking declined sharply over time and revealed an inversion of the social gradient in 2003. This was due to a stronger decrease of smoking in the highest SES group compared to the other groups. Cohort effects were nonlinear and inconsistent. Conclusion: The emergence of increasing social disparities in smoking implies the need to specifically address the situation of low-SES groups when developing tobacco control measures.

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