Mixed-Effects Regression Models for Ordinal Outcomes

2006 ◽  
pp. 187-217
Keyword(s):  
Regression Models ◽  
Mixed Effects

scholarly journals Estimating functional linear mixed-effects regression models

2017 ◽  
Vol 106 ◽  
pp. 153-164 ◽  
Author(s):  
Baisen Liu ◽  
Liangliang Wang ◽  
Jiguo Cao
Keyword(s):  
Regression Models ◽  
Mixed Effects ◽  
Linear Mixed Effects

Physician Perceptions on Quality of Life and Resuscitation Preferences for Extremely Early Newborns

2021 ◽  
Author(s):  
Danielle LoRe ◽  
Christopher Mattson ◽  
Dalia M. Feltman ◽  
Jessica T. Fry ◽  
Kathleen G. Brennan ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Mixed Effects ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Comfort Care ◽  
Logistic Regression Models ◽  
Extremely Preterm

Objective The study aimed to explore physician views on whether extremely early newborns will have an acceptable quality of life (QOL), and if these views are associated with physician resuscitation preferences. Study Design We performed a cross-sectional survey of neonatologists and maternal fetal medicine (MFM) attendings, fellows, and residents at four U.S. medical centers exploring physician views on future QOL of extremely early newborns and physician resuscitation preferences. Mixed-effects logistic regression models examined association of perceived QOL and resuscitation preferences when adjusting for specialty, level of training, gender, and experience with ex-premature infants. Results A total of 254 of 544 (47%) physicians were responded. A minority of physicians had interacted with surviving extremely early newborns when they were ≥3 years old (23% of physicians in pediatrics/neonatology and 6% in obstetrics/MFM). The majority of physicians did not believe an extremely early newborn would have an acceptable QOL at the earliest gestational ages (11% at 22 and 23% at 23 weeks). The majority of physicians (73%) believed that having an extremely preterm infant would have negative effects on the family's QOL. Mixed-effects logistic regression models (odds ratio [OR], 95% confidence interval [CI]) revealed that physicians who believed infants would have an acceptable QOL were less likely to offer comfort care only at 22 (OR: 0.19, 95% CI: 0.05–0.65, p < 0.01) and 23 weeks (OR: 0.24, 95% CI: 0.07–0.78, p < 0.02). They were also more likely to offer active treatment only at 24 weeks (OR: 9.66, 95% CI: 2.56–38.87, p < 0.01) and 25 weeks (OR: 19.51, 95% CI: 3.33–126.72, p < 0.01). Conclusion Physician views of extremely early newborns' future QOL correlated with self-reported resuscitation preferences. Residents and obstetric physicians reported more pessimistic views on QOL. Key Points

Alternatives for Mixed-Effects Meta-Regression Models in the Reliability Generalization Approach

2013 ◽  
Vol 38 (5) ◽  
pp. 443-469 ◽  
Author(s):  
José Antonio López-López ◽  
Juan Botella ◽  
Julio Sánchez-Meca ◽  
Fulgencio Marín-Martínez
Keyword(s):  
Regression Models ◽  
Mixed Effects ◽  
Reliability Generalization ◽  
Meta Regression

scholarly journals Infection Dynamics of Mycoplasma bovis and Other Respiratory Mycoplasmas in Newly Imported Bulls on Italian Fattening Farms

Pathogens ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 537 ◽  
Author(s):  
Salvatore Catania ◽  
Michele Gastaldelli ◽  
Eliana Schiavon ◽  
Andrea Matucci ◽  
Annalucia Tondo ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Gel Electrophoresis ◽  
Regression Models ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Mixed Effects ◽  
Mycoplasma Bovis ◽  
Logistic Regression Models ◽  
Infection Dynamics ◽  
16S Rdna Pcr ◽  
Gradient Gel Electrophoresis

Italian beef production is mainly based on a feedlot system where calves are housed with mixed aged cattle often in conditions favourable to bovine respiratory disease (BRD). In Veneto, an indoor system is also used for imported bulls around 300–350 kg. Mycoplasmas, in particular Mycoplasma bovis and Mycoplasma dispar, contribute to BRD in young calves, but their role in the disease in older cattle has not been investigated. In this study, ten heads of cattle were selected from each of the 24 groups kept in 13 different farms. Bulls were sampled by nasal swabbing at 0, 15, and 60 days after arrival for Mycoplasma isolation. Identification was carried out by 16S-rDNA PCR followed by denaturing gradient gel electrophoresis. M. bovis, M. dispar, and M. bovirhinis were identified, and prevalence was analysed by mixed-effects logistic regression models. This showed that most bulls arrived free of M. bovis, but within two weeks, approximately 40% became infected, decreasing to 13% by the last sampling. In contrast, the prevalence of M. dispar was not dependent on time or seasonality, while M. bovirhinis only showed a seasonality-dependent trend. The Italian fattening system creates an ideal environment for infection with M. bovis, probably originating from previously stabled animals.

Extending the interpretation and utility of mixed effects logistic regression models

1995 ◽  
Vol 24 (3) ◽  
pp. 187-201 ◽  
Author(s):  
Edward R. Atwill ◽  
Hussni O. Mohammed ◽  
Janet M. Scarlett ◽  
Charles E. McCulloch
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Mixed Effects ◽  
Logistic Regression Models

Mixed-effects regression models for studying the natural history of prostate disease

1994 ◽  
Vol 13 (5-7) ◽  
pp. 587-601 ◽  
Author(s):  
Jay D. Pearson ◽  
Christopher H. Morrell ◽  
Patricia K. Landis ◽  
H. Ballentine Carter ◽  
Larry J. Brant
Keyword(s):  
Natural History ◽  
Regression Models ◽  
Mixed Effects ◽  
Prostate Disease ◽  
History Of

scholarly journals Improving the odds? How to pick the winner of the English Derby

2014 ◽  
Vol 10 (1) ◽  
pp. 57-62
Author(s):  
D.J. Marlin ◽  
J.M. Williams ◽  
T. Parkin
Keyword(s):  
Logistic Regression ◽  
Confidence Interval ◽  
Regression Models ◽  
Mixed Effects ◽  
Point Estimate ◽  
Prize Money ◽  
Logistic Regression Models ◽  
Predictive Variables ◽  
Time Period ◽  
Predictive Probability

Many consider the English Derby on Epsom Downs to be ‘The Blue Riband of the Turf’. The Epsom Derby has been run annually since 1780 and the colt Diomed was the first winner. Today the Epsom Derby, run over 1.5 miles, is one of five classic races and is the second leg of the English Triple Crown, preceded by the 2,000 Guineas and followed by the St Leger. The prize money for 2010 has been in excess of £1.25 million. To the best of our knowledge, whilst epidemiological techniques have previously been applied in an attempt to identify risk factors for injury, the purpose of the present study, which we believe is unique, was to use an epidemiological approach to analyse factors that may be predictive of success (or failure) in a single race over the course of a number of consecutive years: The Epsom Derby. Information on the horses competing in the last 22 runnings of the Epsom Derby between 1988 and 2009. Univariate and multivariable single-level and mixed effects logistic regression models were developed using winning the Epsom Derby as the dependent variable. Between 1988 and 2009 in 22 runnings of the Derby, a total of 344 horses started the Epsom Derby. The number of runners in the race has varied between 12 and 25 over the same time period. On average the probability of winning the Derby between 1988 and 2009 was approximately 6% (22/344), without accounting for any potentially predictive variables. Variables that were related to an increased chance of success were being the favourite (odds ratio (OR) 4.75; 95 % confidence interval (CI) 1.58-14.3; P=0.006), the number of 2-year old wins (OR 1.45; CI 1.03-2.04; P=0.03), being foaled in Ireland (OR 2.80; CI 1.12-7.04; P=0.041) and having the same jockey in all races throughout the horses career up to and including the Derby (OR 2.53; CI 1.0-6.41; P=0.05). The highest predictive probability was for horses that started the race as a favourite, were Irish bred, had been ridden by a single jockey and had won twice as a 2-year old. Although the point estimate for this probability was 52% the degree of uncertainty around this estimate was wide, i.e. the 95% CI was 17.5 to 86.5%. Nevertheless even at the lower confidence interval this still represents a significant improvement on the approximately 6% chance of picking a winner at random. In conclusion, using mixed effects logistic regression models would allow one to improve the odds of picking the winner of the Epsom Derby over the past 22 runnings.

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