Robust Estimation of Self-Exciting Generalized Linear Models With Application to Neuronal Modeling

Objective: To evaluate color and roughness of bovine enamel exposed to dentifrices, dental bleaching with 35% hydrogen peroxide (HP), and erosion/staining by red wine. Methods: Bovine enamel blocks were exposed to: artificial saliva (control), Oral-B Pro-Health (stannous fluoride with sodium fluoride, SF), Sensodyne Repair & Protect (bioactive glass, BG), Colgate Pro-Relief (arginine and calcium carbonate, AR), or Chitodent (chitosan, CHI). After toothpaste exposure, half (n=12) of the samples were bleached (35% HP), and the other half were not (n=12). The color (CIE L*a* b*, ΔE), surface roughness (Ra), and scanning electron microscopy were evaluated. Color and roughness were assessed at baseline, post-dentifrice and/or -dental bleaching, and after red wine. The data were subjected to analysis of variance (ANOVA) (ΔE) for repeated measures (Ra), followed by Tukey ́s test. The L*, a*, and b* values were analyzed by generalized linear models (a=0.05). Results: The HP promoted an increase in Ra values; however, the SF, BG, and AR did not enable this alteration. After red wine, all groups apart from SF (unbleached) showed increases in Ra values; SF and AR promoted decreases in L* values; AR demonstrated higher ΔE values, differing from the control; and CHI decreased the L* variation in the unbleached group. Conclusion: Dentifrices did not interfere with bleaching efficacy of 35% HP. However, dentifrices acted as a preventive agent against surface alteration from dental bleaching (BG, SF, and AR) or red wine (SF). Dentifrices can decrease (CHI) or increase (AR and SF) staining by red wine.

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Economic burden of readmission due to postoperative cerebrospinal fluid leak in Chinese patients

Journal of Comparative Effectiveness Research ◽

10.2217/cer-2020-0067 ◽

2020 ◽

Vol 9 (16) ◽

pp. 1105-1115

Author(s):

Shuqing Wu ◽

Xin Cui ◽

Shaoyu Zhang ◽

Wenqi Tian ◽

Jiazhen Liu ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Generalized Linear Models ◽

Economic Burden ◽

Linear Models ◽

Chinese Patients ◽

Cerebrospinal Fluid Leakage ◽

Index Hospitalization ◽

Real World Data ◽

Factors Associated ◽

Hospitalization Cost

Aim: This real-world data study investigated the economic burden and associated factors of readmissions for cerebrospinal fluid leakage (CSFL) post-cranial, transsphenoidal, or spinal index surgeries. Methods: Costs of CSFL readmissions and index hospitalizations during 2014–2018 were collected. Readmission cost was measured as absolute cost and as percentage of index hospitalization cost. Factors associated with readmission cost were explored using generalized linear models. Results: Readmission cost averaged US$2407–6106, 35–94% of index hospitalization cost. Pharmacy costs were the leading contributor. Generalized linear models showed transsphenoidal index surgery and surgical treatment for CSFL were associated with higher readmission costs. Conclusion: CSFL readmissions are a significant economic burden in China. Factors associated with higher readmission cost should be monitored.

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Modelling passenger train arrival delays with Generalized Linear Models and its perspective for scheduling at main stations

8th International Conference on Railway Engineering (ICRE 2018) ◽

10.1049/cp.2018.0049 ◽

2018 ◽

Author(s):

M.M. de Faverges ◽

G. Russolillo ◽

C. Picouleau ◽

B. Merabet ◽

B. Houzel

Keyword(s):

Generalized Linear Models ◽

Linear Models ◽

Passenger Train

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Determination of Enzyme or Binding Constants Using Generalized Linear Models, with Particular Reference to Michaelis–Menten Models

Journal of Pharmaceutical Sciences ◽

10.1002/jps.2600780514 ◽

1989 ◽

Vol 78 (5) ◽

pp. 413-416

Author(s):

Gerald Van Belle ◽

Sue Leurgans ◽

Pat Friel ◽

Sunwei Guo ◽

Mark Yerby

Keyword(s):

Generalized Linear Models ◽

Linear Models ◽

Binding Constants

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Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates

Statistical Methods in Medical Research ◽

10.1177/09622802211008206 ◽

2021 ◽

pp. 096228022110082

Author(s):

Yang Li ◽

Wei Ma ◽

Yichen Qin ◽

Feifang Hu

Keyword(s):

Generalized Linear Models ◽

Treatment Effect ◽

Linear Models ◽

Type I Error ◽

Type I ◽

Test Statistic ◽

Asymptotic Results ◽

Adaptive Randomization ◽

Adjustment Method ◽

Inflated Type

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

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