Bayesian information criterion accounting for the number of covariance parameters in mixed effects models

AbstractThis paper concerns methods of choosing appropriate models for longitudinal studies. Attention is paid to three criteria: the marginal Akaike Information Criterion (mAIC), the conditional Akaike Information Criterion (cAIC), and the corrected conditional Akaike Information Criterion (ccAIC). We consider these criteria based on an example concerning the effect of storage time and addition of flaxseed (Linum usitatissimum L.) preparations (i.e. ground flaxseeds, defatted flaxseed meal and flaxseed ethanolic extract) on changes in lipid oxidation and fatty acid composition during the storage of liver pâté with partial substitution of fat with flax oil.

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Harmonic Syntax of the Twelve-Bar Blues Form

Music Perception An Interdisciplinary Journal ◽

10.1525/mp.2017.35.2.165 ◽

2017 ◽

Vol 35 (2) ◽

pp. 165-192 ◽

Cited By ~ 1

Author(s):

Jonah Katz

Keyword(s):

Bayesian Information Criterion ◽

Regression Models ◽

Information Criterion ◽

Mixed Effects ◽

Context Free Grammar ◽

Harmonic Syntax ◽

Novel Method ◽

The Difference ◽

Transitional Probabilities ◽

Context Free

This paper describes the construction and analysis of a corpus of harmonic progressions from 12-bar blues forms included in the jazz repertoire collection The Real Book. A novel method of coding and analyzing such corpus data is developed, with a notion of “possible harmonic change” derived from the corpus and logit mixed-effects regression models that describe the difference between actually occurring harmonic events and possible but non-occurring ones in terms of various sets of theoretical constructs. Models using different sets of constructs are compared using the Bayesian Information Criterion, which assesses the accuracy and efficiency of each model. The principal results are that: (1) transitional probabilities are better modeled using root-motion and chord-frequency information than they are using pairs of individual chords; (2) transitional probabilities are better described using a mixture model intermediate in complexity between a bigram and full trigram model; and (3) the difference between occurring and non-occurring chords is more efficiently modeled with a hierarchical, recursive context-free grammar than it is as a Markov chain. The results have implications for theories of harmony, composition, and cognition more generally.

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Estimating the distribution and relative density of satellite-tagged loggerhead sea turtles using geostatistical mixed effects models

Marine Ecology Progress Series ◽

10.3354/meps12396 ◽

2018 ◽

Vol 586 ◽

pp. 217-232 ◽

Cited By ~ 11

Author(s):

MV Winton ◽

G Fay ◽

HL Haas ◽

M Arendt ◽

S Barco ◽

...

Keyword(s):

Relative Density ◽

Sea Turtles ◽

Mixed Effects ◽

Mixed Effects Models ◽

Loggerhead Sea Turtles

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Semiparametric Inference in Generalized Mixed Effects Models

SSRN Electronic Journal ◽

10.2139/ssrn.1010928 ◽

2007 ◽

Author(s):

Maria Jose Lombardia ◽

Stefan Sperlich

Keyword(s):

Mixed Effects ◽

Mixed Effects Models ◽

Semiparametric Inference

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Effect of tofogliflozin on arterial stiffness in patients with type 2 diabetes: prespecified sub-analysis of the prospective, randomized, open-label, parallel-group comparative UTOPIA trial

Cardiovascular Diabetology ◽

10.1186/s12933-020-01206-1 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Naoto Katakami ◽

◽

Tomoya Mita ◽

Hidenori Yoshii ◽

Toshihiko Shiraiwa ◽

...

Keyword(s):

Risk Factors ◽

Blood Pressure ◽

Type 2 Diabetes ◽

Conventional Treatment ◽

Mixed Effects ◽

Mixed Effects Models ◽

Open Label ◽

Parallel Group ◽

History Of

Abstract Background Tofogliflozin, an SGLT2 inhibitor, is associated with favorable metabolic effects, including improved glycemic control and serum lipid profile and decreased body weight, visceral adipose tissue, and blood pressure (BP). This study evaluated the effects of tofogliflozin on the brachial-ankle pulse wave velocity (baPWV) in patients with type 2 diabetes (T2DM) without a history of apparent cardiovascular disease. Methods The using tofogliflozin for possible better intervention against atherosclerosis for type 2 diabetes patients (UTOPIA) trial is a prospective, randomized, open-label, multicenter, parallel-group, comparative study. As one of the prespecified secondary outcomes, changes in baPWV over 104 weeks were evaluated in 154 individuals (80 in the tofogliflozin group and 74 in the conventional treatment group) who completed baPWV measurement at baseline. Results In a mixed-effects model, the progression in the right, left, and mean baPWV over 104 weeks was significantly attenuated with tofogliflozin compared to that with conventional treatment (– 109.3 [– 184.3, – 34.3] (mean change [95% CI] cm/s, p = 0.005; – 98.3 [– 172.6, – 24.1] cm/s, p = 0.010; – 104.7 [– 177.0, – 32.4] cm/s, p = 0.005, respectively). Similar findings were obtained even after adjusting the mixed-effects models for traditional cardiovascular risk factors, including body mass index (BMI), glycated hemoglobin (HbA1c), total cholesterol, high-density lipoprotein (HDL)-cholesterol, triglyceride, systolic blood pressure (SBP), hypertension, smoking, and/or administration of drugs, including hypoglycemic agents, antihypertensive agents, statins, and anti-platelets, at baseline. The findings of the analysis of covariance (ANCOVA) models, which included the treatment group, baseline baPWV, and traditional cardiovascular risk factors, resembled those generated by the mixed-effects models. Conclusions Tofogliflozin significantly inhibited the increased baPWV in patients with T2DM without a history of apparent cardiovascular disease, suggesting that tofogliflozin suppressed the progression of arterial stiffness. Trial Registration UMIN000017607. Registered 18 May 2015. (https://www.umin.ac.jp/icdr/index.html)

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

Educational and Psychological Measurement ◽

10.1177/0013164421994893 ◽

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.

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