The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level

2021 ◽  
pp. 001316442199489
Author(s):  
Luyao Peng ◽  
Sandip Sinharay
Keyword(s):  
Type I Error ◽  
Real Data ◽  
Mixed Effects ◽  
Error Rates ◽  
Mixed Effects Models ◽  
Type I ◽  
Aggregate Level ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Best Linear Unbiased

Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of Wollack and Eckerly (2017) and Sinharay (2018) and suggests a new aggregate-level EDI by incorporating the empirical best linear unbiased predictor from the literature of linear mixed-effects models (e.g., McCulloch et al., 2008). A simulation study shows that the new EDI has larger power than the indices of Wollack and Eckerly (2017) and Sinharay (2018). In addition, the new index has satisfactory Type I error rates. A real data example is also included.

scholarly journals Consequences of power transforms as a statistical solution in linear mixed-effects models of chronometric data

2018 ◽  
Author(s):  
Van Rynald T Liceralde ◽  
Peter C. Gordon
Keyword(s):  
Type I Error ◽  
Response Times ◽  
Random Effect ◽  
Mixed Effects ◽  
Error Rates ◽  
Mixed Effects Models ◽  
Type I ◽  
Statistical Solution ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects

Power transforms have been increasingly used in linear mixed-effects models (LMMs) of chronometric data (e.g., response times [RTs]) as a statistical solution to preempt violating the assumption of residual normality. However, differences in results between LMMs fit to raw RTs and transformed RTs have reignited discussions on issues concerning the transformation of RTs. Here, we analyzed three word-recognition megastudies and performed Monte Carlo simulations to better understand the consequences of transforming RTs in LMMs. Within each megastudy, transforming RTs produced different fixed- and random-effect patterns; across the megastudies, RTs were optimally normalized by different power transforms, and results were more consistent among LMMs fit to raw RTs. Moreover, the simulations showed that LMMs fit to optimally normalized RTs had greater power for main effects in smaller samples, but that LMMs fit to raw RTs had greater power for interaction effects as sample sizes increased, with negligible differences in Type I error rates between the two models. Based on these results, LMMs should be fit to raw RTs when there is no compelling reason beyond nonnormality to transform RTs and when the interpretive framework mapping the predictors and RTs treats RT as an interval scale.

Efficient baseline utilization for incomplete block crossover clinical trials

2017 ◽  
Vol 28 (3) ◽  
pp. 801-821
Author(s):  
Thomas O Jemielita ◽  
Mary E Putt ◽  
Devan V Mehrotra
Keyword(s):  
Fixed Effects ◽  
Type I Error ◽  
Real Data ◽  
Mixed Effects ◽  
Error Rates ◽  
Small Sample ◽  
Analysis Of Covariance ◽  
Type I ◽  
Incomplete Block ◽  
Clinical Drug Development

Incomplete block crossover trials with period-specific baseline and post-baseline (outcome) measures for each subject are often used in clinical drug development; without loss of generality, we focus on the three-treatment two-period ([Formula: see text]) crossover. Data from such trials are commonly analyzed using a mixed effects model with indicator terms for treatment and period, and an unstructured covariance matrix for the vector of intra-subject measurements. It is well-known that treatment effect estimates from this analysis are complex functions of both within-subject and between-subject treatment contrasts. We caution that the associated type I error rate and power for hypothesis testing can be non-trivially influenced by how the baselines are utilized. Specifically, the mixed effects analysis which uses change from baseline as the dependent variable is shown to consistently underperform corresponding analyses in which the outcome is the dependent variable and linear combinations of the baselines are used as period-specific and/or period-invariant covariates. A simpler fixed effects analysis of covariance involving only within-subject contrasts is also described for small sample situations in which the mixed effects analyses can suffer from increased type I error rates. Theoretical insights, simulation results and an illustrative example with real data are used to develop the main points.

scholarly journals Association of body condition with lameness in dairy cattle: a single-farm longitudinal study

2021 ◽  
pp. 1-4
Author(s):  
Michaela Kranepuhl ◽  
Detlef May ◽  
Edna Hillmann ◽  
Lorenz Gygax
Keyword(s):  
Body Condition ◽  
Body Condition Score ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Confounding Variables ◽  
Condition Score ◽  
The Impact ◽  
The Relationship

Abstract This research communication describes the relationship between the occurrence of lameness and body condition score (BCS) in a sample of 288 cows from a single farm that were repeatedly scored in the course of 9 months while controlling for confounding variables. The relationship between BCS and lameness was evaluated using generalised linear mixed-effects models. It was found that the proportion of lame cows was higher with decreasing but also with increasing BCS, increased with lactation number and decreased with time since the last claw trimming. This is likely to reflect the importance of sufficient body condition in the prevention of lameness but also raises the question of the impact of overcondition on lameness and the influence of claw trimming events on the assessment of lameness. A stronger focus on BCS might allow improved management of lameness that is still one of the major problems in housed cows.

D-optimal cohort designs for linear mixed-effects models

2007 ◽  
Vol 27 (14) ◽  
pp. 2586-2600 ◽  
Author(s):  
Fetene B. Tekle ◽  
Frans E. S. Tan ◽  
Martijn P. F. Berger
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects
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