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Mixed-effects models in psychophysiology
Psychophysiology, 2000The current methodological policy in Psychophysiology stipulates that repeated-measures designs be analyzed using either multivariate analysis of variance (ANOVA) or repeated-measures ANOVA with the Greenhouse-Geisser or Huynh-Feldt correction. Both techniques lead to appropriate type I error probabilities under general assumptions about the variance ...
E, Bagiella, R P, Sloan, D F, Heitjan
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Functional Mixed Effects Models
Biometrics, 2002Summary.In this article, a new class of functional models in which smoothing splines are used to model fixed effects as well as random effects is introduced. The linear mixed effects models are extended to non‐parametric mixed effects models by introducing functional random effects, which are modeled as realizations of zero‐mean stochastic processes ...
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To Mix or Not To Mix: The Effects of Not Mixing Sera on HIV Serologic Results
Laboratory Medicine, 1990Previously frozen serum samples, known to be positive for antibodies to HIV, produced divergent ELISA results when tested before and after mixing. Eleven of IS samples produced lower titers before mixing, and all samples had lower optical density readings when compared with the same samples after mixing.
Niel T. Constantine +2 more
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2007
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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2021
Abstract Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset
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Abstract Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset
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Nursing Management (Springhouse), 1997
Although many facilities believe turning over some patient care to unlicensed caregivers will cut costs, results show otherwise. The greater the rate of change--patient acuity--the greater the need for an all-RN staff. Skill mix, length of stay and salary costs among five teaching hospitals measure the cost effects.
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Although many facilities believe turning over some patient care to unlicensed caregivers will cut costs, results show otherwise. The greater the rate of change--patient acuity--the greater the need for an all-RN staff. Skill mix, length of stay and salary costs among five teaching hospitals measure the cost effects.
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The demographic effect of mixed marriages
European Journal of Population, 1988This paper gives a formal expression for the demographic effect of mixed marriages, i.e., the effect on the number of children, and thereafter illustrates the long-term effects of these marriages with a simple simulation model.
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2010
Mixed effects models, or simply mixed models, are widely used in practice. These models are characterized by the involvement of the so-called random effects. To understand the basic elements of a mixed model, let us first recall a linear regression model, which can be expressed as y = Xβ + e, where y is a vector of observations, X is a matrix of known ...
Donghui Zhang, Chunpeng Fan, A Gould
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Mixed effects models, or simply mixed models, are widely used in practice. These models are characterized by the involvement of the so-called random effects. To understand the basic elements of a mixed model, let us first recall a linear regression model, which can be expressed as y = Xβ + e, where y is a vector of observations, X is a matrix of known ...
Donghui Zhang, Chunpeng Fan, A Gould
+4 more sources