Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion

2018 ◽  
pp. 99-116 ◽  
Author(s):  
Danielle Barth ◽  
Vsevolod Kapatsinski
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linguistic Data ◽  
Corpus Linguistic ◽  
Lexical Diffusion

The most under-used statistical method in corpus linguistics: multi-level (and mixed-effects) models

Corpora ◽  
2015 ◽  
Vol 10 (1) ◽  
pp. 95-125 ◽  
Author(s):  
Stefan Th. Gries
Keyword(s):  
Statistical Analysis ◽  
Statistical Method ◽  
Gold Standard ◽  
Corpus Linguistics ◽  
Regression Models ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Regression Modelling ◽  
Corpus Linguistic ◽  
Multi Level

Much statistical analysis of psycholinguistic data is now being done with so-called mixed-effects regression models. This development was spearheaded by a few highly influential introductory articles that (i) showed how these regression models are superior to what was the previous gold standard and, perhaps even more importantly, (ii) showed how these models are used practically. Corpus linguistics can benefit from mixed-effects/multi-level models for the same reason that psycholinguistics can – because, for example, speaker-specific and lexically specific idiosyncrasies can be accounted for elegantly; but, in fact, corpus linguistics needs them even more because (i) corpus-linguistic data are observational and, thus, usually unbalanced and messy/noisy, and (ii) most widely used corpora come with a hierarchical structure that corpus linguists routinely fail to consider. Unlike nearly all overviews of mixed-effects/multi-level modelling, this paper is specifically written for corpus linguists to get more of them to start using these techniques more. After a short methodological history, I provide a non-technical introduction to mixed-effects models and then discuss in detail one example – particle placement in English – to show how mixed-effects/multi-level modelling results can be obtained and how they are far superior to those of traditional regression modelling.

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

2018 ◽  
Vol 586 ◽  
pp. 217-232 ◽  
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

scholarly journals 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

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)

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 ALLY in fighting COVID-19: magnitude of albumin decline and lymphopenia (ALLY) predict progression to critical disease

2021 ◽  
pp. jim-2020-001525
Author(s):  
Johanna S van Zyl ◽  
Amit Alam ◽  
Joost Felius ◽  
Ronnie M Youssef ◽  
Dipesh Bhakta ◽  
...  
Keyword(s):  
Medical Records ◽  
Resource Planning ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Disease Course ◽  
Risk Models ◽  
Simple Tool ◽  
Laboratory Results ◽  
Risk Of Disease ◽  
Critical Patients

The global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic leading to coronavirus disease 2019 (COVID-19) is straining hospitals. Judicious resource allocation is paramount but difficult due to the unpredictable disease course. Once hospitalized, discerning which patients may progress to critical disease would be valuable for resource planning. Medical records were reviewed for consecutive hospitalized patients with COVID-19 in a large healthcare system in Texas. The main outcome was progression to critical disease within 10 days from admission. Albumin trends from admission to 7 days were analyzed using mixed-effects models, and progression to critical disease was modeled by multivariable logistic regression of laboratory results. Risk models were evaluated in an independent group. Of 153 non-critical patients, 28 (18%) progressed to critical disease. The rate of decrease in mean baseline-corrected (Δ) albumin was −0.08 g/dL/day (95% CI −0.11 to −0.04; p<0.001) or four times faster, in those who progressed compared with those who did not progress. A model of Δ albumin combined with lymphocyte percentage predicting progression to critical disease was validated in 60 separate patients (sensitivity, 0.70; specificity, 0.74). ALLY (delta albumin and lymphocyte percentage) is a simple tool to identify patients with COVID-19 at higher risk of disease progression when: (1) a 0.9 g/dL or greater albumin drop from baseline within 5 days of admission or (2) baseline lymphocyte of ≤10% is observed. The ALLY tool identified >70% of hospitalized cases that progressed to critical COVID-19 disease. We recommend prospectively tracking albumin. This is a globally applicable tool for all healthcare systems.

Detecting trends in diameter growth of Norway spruce on long-term forest research plots using linear mixed-effects models

2014 ◽  
Vol 133 (5) ◽  
pp. 783-792 ◽  
Author(s):  
Chaofang Yue ◽  
Hans-Peter Kahle ◽  
Ulrich Kohnle ◽  
Qing Zhang ◽  
Xingang Kang
Keyword(s):  
Norway Spruce ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Diameter Growth ◽  
Forest Research ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects
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