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Fixed-effect Versus Random-effects Models for Meta-analyses: Random-effects Models
European Urology Focus, 2023Random-effects models can account for variability both within and between studies. This makes them suitable for meta-analyses in surgery, where there is often significant heterogeneity between studies or heterogeneity owing to intrinsic differences attributable to patient or population factors.
Alex L.E. Halme +2 more
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Fixed- and Random-Effects Models
2021Deciding whether to use a fixed-effect model or a random-effects model is a primary decision an analyst must make when combining the results from multiple studies through meta-analysis. Both modeling approaches estimate a single effect size of interest.
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Random-Effects Models for Longitudinal Data
Biometrics, 1982Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily.
Laird, Nan M., Ware, James H.
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2017
This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike the case of fixed effects, the number of parameters to be estimated does not increase with the sample size. First, optimal (F)GLS estimators are presented for the textbook-style complete data case, paying special attention to asymptotics.
Balazsi, Laszlo +3 more
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This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike the case of fixed effects, the number of parameters to be estimated does not increase with the sample size. First, optimal (F)GLS estimators are presented for the textbook-style complete data case, paying special attention to asymptotics.
Balazsi, Laszlo +3 more
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1994
This chapter is concerned with random effects models for analyzing nonnormal data that are assumed to be clustered or correlated. The clustering may be due to repeated measurements over time, as in longitudinal studies, or to subsampling the primary sampling units, as in cross-sectional studies.
Ludwig Fahrmeir, Gerhard Tutz
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This chapter is concerned with random effects models for analyzing nonnormal data that are assumed to be clustered or correlated. The clustering may be due to repeated measurements over time, as in longitudinal studies, or to subsampling the primary sampling units, as in cross-sectional studies.
Ludwig Fahrmeir, Gerhard Tutz
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2002
Abstract Chapter 8 has dealt with marginal models whose regression parameters have population average interpretations. In this chapter we consider random effects models in which the regression coeficients measure the more direct infiuence of explanatory variables on the responses for heterogeneous individuals.
Peter J Diggle +3 more
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Abstract Chapter 8 has dealt with marginal models whose regression parameters have population average interpretations. In this chapter we consider random effects models in which the regression coeficients measure the more direct infiuence of explanatory variables on the responses for heterogeneous individuals.
Peter J Diggle +3 more
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Random effects Cox models: A Poisson modelling approach [PDF]
Biometrika, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Renjun Ma +2 more
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2005
Abstract In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a GLM to fit may be due to several causes. The distribution of Ymay not be the specified exponential family member, or the regression model fitted may be mis-specified.
Murray Aitkin, Brain Francis, John Hinde
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Abstract In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a GLM to fit may be due to several causes. The distribution of Ymay not be the specified exponential family member, or the regression model fitted may be mis-specified.
Murray Aitkin, Brain Francis, John Hinde
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2009
Abstract In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a generalized linear model to fit may be due to several causes. The distribution of Y may not be the specified exponential family member, or the regression model fitted may be mis-specified.
Murray Aitkin +3 more
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Abstract In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a generalized linear model to fit may be due to several causes. The distribution of Y may not be the specified exponential family member, or the regression model fitted may be mis-specified.
Murray Aitkin +3 more
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Random effects models in clinical research
Int. Journal of Clinical Pharmacology and Therapeutics, 2008In clinical trials a fixed effects research model assumes that the patients selected for a specific treatment have the same true quantitative effect and that the differences observed are residual error. If, however, we have reasons to believe that certain patients respond differently from others, then the spread in the data is caused not only by the ...
Cleophas, T. J., Zwinderman, A. H.
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