Generalized Estimating Equations

Ziegler, Andreas

doi:10.1007/978-1-4614-0499-6

Cited by 110 publications

(45 citation statements)

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“…To test whether this response rate varied between noise and quiet treatments, and in relation to time without food, we used generalized estimating equations [14]. We first analysed responses to the parental stimuli, using a model that included stimulus (parent-with-call, parent-without-call), treatment (noise, quiet), deprivation period (shorter, longer) and their interaction as main effects, and trial (i.e.…”

Section: Methodsmentioning

confidence: 99%

Ambient noise increases missed detections in nestling birds

Leonard

Horn

2012

Biol. Lett.

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Ambient noise can mask acoustic cues, making their detection and discrimination difficult for receivers. This can result in two types of error: missed detections, when receivers fail to respond to the appropriate cues, and false alarms, when they respond to inappropriate cues. Nestling birds are error-prone, sometimes failing to beg when parents arrive with food (committing missed detections) or begging in response to stimuli other than a parent's arrival (committing false alarms). Here, we ask whether the frequency of these errors by nestling tree swallows ( Tachycineta bicolor ) increases in the presence of noise. We found that nestlings exposed to noise had more missed detections than their unexposed counterparts. We also found that false alarms remained low overall and did not differ significantly between noise and quiet treatments. Our results suggest that nestlings living in noisy environments may be less responsive to their parents than nestlings in quieter environments.

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Section: Methodsmentioning

confidence: 99%

Ambient noise increases missed detections in nestling birds

Leonard

Horn

2012

Biol. Lett.

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“…The probabilities calculated are the proportions of 1s from each participant for each swallowing task by each physiological component. We estimated the probability that each swallowing task yielded the OI score using generalized estimating equations with a logit link function and the binary indicator variable as the outcome variable (Ziegler & Vens, 2010). We also calculated the range (maximum minus minimum) of probabilities across swallowing tasks for each physiological component to discern whether one swallowing task had sufficiently higher probability of yielding the worst performance.…”

Section: Discussionmentioning

confidence: 99%

Identification of Swallowing Tasks From a Modified Barium Swallow Study That Optimize the Detection of Physiological Impairment

Hazelwood

Armeson

Hill

et al. 2017

J Speech Lang Hear Res

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Purpose The purpose of this study was to identify which swallowing task(s) yielded the worst performance during a standardized modified barium swallow study (MBSS) in order to optimize the detection of swallowing impairment. Method This secondary data analysis of adult MBSSs estimated the probability of each swallowing task yielding the derived Modified Barium Swallow Impairment Profile (MBSImP™©; Martin-Harris et al., 2008) Overall Impression (OI; worst) scores using generalized estimating equations. The range of probabilities across swallowing tasks was calculated to discern which swallowing task(s) yielded the worst performance. Results Large-volume, thin-liquid swallowing tasks had the highest probabilities of yielding the OI scores for oral containment and airway protection. The cookie swallowing task was most likely to yield OI scores for oral clearance. Several swallowing tasks had nearly equal probabilities (≤ .20) of yielding the OI score. Conclusions The MBSS must represent impairment while requiring boluses that challenge the swallowing system. No single swallowing task had a sufficiently high probability to yield the identification of the worst score for each physiological component. Omission of swallowing tasks will likely fail to capture the most severe impairment for physiological components critical for safe and efficient swallowing. Results provide further support for standardized, well-tested protocols during MBSS.

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“…Surprisingly, marginal models are quite rarely employed within the behavioural sciences, despite the fact that Generalized estimating equations, i.e. the marginal modelling approach (Lee & Nelder, 2004;Ziegler, 2011), can handle correlated non-normally distributed (and heteroscedastic) data, which are, in fact, very common in the behavioural sciences. GEE is relatively easy to use.…”

Section: Analysis Of Non-normal Correlated Datamentioning

confidence: 99%

Generalized estimating equations: A pragmatic and flexible approach to the marginalGLMmodelling of correlated data in the behavioural sciences

2017

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Within behavioural research, non-normally distributed data with a complicated structure are common. For instance, data can represent repeated observations of quantities on the same individual. The regression analysis of such data is complicated both by the interdependency of the observations (response variables) and by their non-normal distribution. Over the last decade, such data have been more and more frequently analysed using generalized mixed-effect models. Some researchers invoke the heavy machinery of mixed-effect modelling to obtain the desired population-level (marginal) inference, which can be achieved by using simpler tools-namely by marginal models. This paper highlights marginal modelling (using generalized estimating

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Generalized Estimating Equations

Cited by 110 publications

References 0 publications

Ambient noise increases missed detections in nestling birds

Ambient noise increases missed detections in nestling birds

Identification of Swallowing Tasks From a Modified Barium Swallow Study That Optimize the Detection of Physiological Impairment

Generalized estimating equations: A pragmatic and flexible approach to the marginalGLMmodelling of correlated data in the behavioural sciences

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