Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

A mixed autoregressive probit model for ordinal longitudinal data

Abstract: SUMMARY Longitudinal data with binary and ordinal outcomes routinely appear in medical applications. Existing methods are typically designed to deal with short measurement series. In contrast, modern longitudinal data can result in large numbers of subject-specific serial observations. In this framework, we consider multivariate probit models with random effects to capture heterogeneity and autoregressive terms for describing the serial dependence. Since likelihood inference for the proposed class of models is computationally burdensome because of high-dimensional intractable integrals, a pseudolikelihood approach is followed. The methodology is motivated by the analysis of a large longitudinal study on the determinants of migraine severity.

Influence of methodologic factors in a pooled analysis of 13 case-control studies of colorectal cancer and dietary fiber.

Abstract: We examined the study design features and data collection methods from 13 case-control studies of colorectal cancer and diet, which had been previously combined and analyzed, to determine whether they influenced the results obtained from a pooled analysis. We assessed the methods used in each study, estimated a quality score, and used random effects models to re-estimate the pooled odds ratio for the association between dietary fiber and colorectal cancer for these data. Key features of the methods used in each study and the quality score were examined in random effects models to determine whether the heterogeneity found between study-specific risk estimates could be explained by these variables. The odds ratio for dietary fiber and colorectal cancer was 0.46 (95% confidence interval = 0.34-0.64) for the 13 case-control studies as estimated with a random effects model. Two factors, whether the diet questionnaire had been validated before use in the case-control study and whether qualitative data on dietary habits and cooking methods had been incorporated into the nutrient estimation, explained some of the heterogeneity found between studies. Risk estimates for dietary fiber and colorectal cancer were closer to the null for the studies that had these two characteristics. Quality score did not explain any between-study heterogeneity. Random effects models, which included fixed effects covariates, explained some between-study heterogeneity in these data and would be useful for future pooled analyses.

Meta‐analyses in systematic reviews of randomized controlled trials in perinatal medicine: comparison of fixed and random effects models

Abstract: There is a need for empirical work comparing the random effects model with the fixed effects model in the calculation of a pooled relative risk in the meta-analysis in systematic reviews of randomized controlled trials. Such comparisons are particularly important when trial results are heterogeneous. We considered 84 independent meta-analyses in which each trial included a set of different women/newborns. These meta-analyses were included in systematic reviews published in the Cochrane Library's pregnancy and childbirth module. Twenty-one of these 84 meta-analyses demonstrated statistical heterogeneity at p<0.10. The random effects model estimates showed wider confidence intervals, particularly in those meta-analyses showing heterogeneity in the trial results. The summary relative risk for the random effects model tended to show a larger protective treatment effect than the fixed effects model in the heterogeneous meta-analyses. In this set of meta-analyses, statistical evaluation of publication bias cannot be shown to account for heterogeneity. Our empirical conclusion is that there may be opposing effects if the random effects model is used in the meta-analysis of clinical trials showing heterogeneity in the results: stronger treatment effects reflected in the summary relative risk, but wider confidence intervals about this summary measure.

A spatial dynamic panel model with random effects applied to commuting times

Abstract: A space–time filter is set forth for spatial panel data situations that include random effects. We propose a general spatial dynamic specification that encompasses several spatiotemporal models previously used in the panel data literature. We apply the model to the case of highway induced travel demand. The theory of induced travel demand asserts that increased highway capacity will induce growth in traffic for a number of reasons. Our model allows us to quantify the spatial spillover impacts of increased highway capacity at one location in the network on travel times in neighboring locations and in future time periods.

The Sensitivity of Labor-Supply Parameter Estimates to Unobserved Individual Effects: Fixed- and Random-Effects Estimates in a Nonlinear Model Using Panel Data

Abstract: Life-cycle models of labor supply predict the presence of an unobserved individual effect in the labor-supply equation that is correlated with observed explanatory variables, leading to an omitted variables bias in the cross section. I examine the sensitivity of parameter estimates to the presence of these effects, using fixed- and random-effect Tobit models. The estimated effects of children are too large in the cross section. The estimated intertemporal substitution elasticity ranges from 1.1 to 1.7. The results are similar for fixed- and random-effects models and for models using different specifications of the dependent variable.