scholarly journals Longitudinal assessment of utilities in patients with migraine: an analysis of erenumab randomized controlled trials

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Gian Luca Di Tanna ◽  
Joshua K. Porter ◽  
Richard B. Lipton ◽  
Anthony J. Hatswell ◽  
Sandhya Sapra ◽  
...  
Keyword(s):  
Regression Model ◽  
Linear Mixed Effect Model ◽  
Beta Regression ◽  
Response Model ◽  
Mixed Effect ◽  
Utility Values ◽  
Effect Model ◽  
Fractional Response ◽  
Probit Link ◽  
Beta Regression Model

Abstract Background Cost-effectiveness analyses in patients with migraine require estimates of patients’ utility values and how these relate to monthly migraine days (MMDs). This analysis examined four different modelling approaches to assess utility values as a function of MMDs. Methods Disease-specific patient-reported outcomes from three erenumab clinical studies (two in episodic migraine [NCT02456740 and NCT02483585] and one in chronic migraine [NCT02066415]) were mapped to the 5-dimension EuroQol questionnaire (EQ-5D) as a function of the Migraine-Specific Quality of Life Questionnaire (MSQ) and the Headache Impact Test (HIT-6™) using published algorithms. The mapped utility values were used to estimate generic, preference-based utility values suitable for use in economic models. Four models were assessed to explain utility values as a function of MMDs: a linear mixed effects model with restricted maximum likelihood (REML), a fractional response model with logit link, a fractional response model with probit link and a beta regression model. Results All models tested showed very similar fittings. Root mean squared errors were similar in the four models assessed (0.115, 0.114, 0.114 and 0.114, for the linear mixed effect model with REML, fractional response model with logit link, fractional response model with probit link and beta regression model respectively), when mapped from MSQ. Mean absolute errors for the four models tested were also similar when mapped from MSQ (0.085, 0.086, 0.085 and 0.085) and HIT-6 and (0.087, 0.088, 0.088 and 0.089) for the linear mixed effect model with REML, fractional response model with logit link, fractional response model with probit link and beta regression model, respectively. Conclusions This analysis describes the assessment of longitudinal approaches in modelling utility values and the four models proposed fitted the observed data well. Mapped utility values for patients treated with erenumab were generally higher than those for individuals treated with placebo with equivalent number of MMDs. Linking patient utility values to MMDs allows utility estimates for different levels of MMD to be predicted, for use in economic evaluations of preventive therapies. Trial registration ClinicalTrials.gov numbers of the trials used in this study: STRIVE, NCT02456740 (registered May 14, 2015), ARISE, NCT02483585 (registered June 12, 2015) and NCT02066415 (registered Feb 17, 2014).

scholarly journals MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel E. Runcie ◽  
Jiayi Qu ◽  
Hao Cheng ◽  
Lorin Crawford
Keyword(s):  
Genomic Prediction ◽  
Large Scale ◽  
Mixed Model ◽  
Human Genetics ◽  
Linear Mixed Effect Model ◽  
Mixed Effect ◽  
Statistical Framework ◽  
Effect Model ◽  
Plant Data ◽  
Genetic Value

AbstractLarge-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present , a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

scholarly journals Limited not lazy: a quasi-experimental secondary analysis of evidence quality evaluations by those who hold implausible beliefs

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Kristy A. Martire ◽  
Bethany Growns ◽  
Agnes S. Bali ◽  
Bronte Montgomery-Farrer ◽  
Stephanie Summersby ◽  
...  
Keyword(s):  
Secondary Analysis ◽  
Past Research ◽  
Linear Mixed Effect Model ◽  
High Quality ◽  
Mixed Effect ◽  
High Quality Evidence ◽  
Effect Model ◽  
Evidence Quality ◽  
Quasi Experimental ◽  
Quality Evidence

AbstractPast research suggests that an uncritical or ‘lazy’ style of evaluating evidence may play a role in the development and maintenance of implausible beliefs. We examine this possibility by using a quasi-experimental design to compare how low- and high-quality evidence is evaluated by those who do and do not endorse implausible claims. Seven studies conducted during 2019–2020 provided the data for this analysis (N = 746). Each of the seven primary studies presented participants with high- and/or low-quality evidence and measured implausible claim endorsement and evaluations of evidence persuasiveness (via credibility, value, and/or weight). A linear mixed-effect model was used to predict persuasiveness from the interaction between implausible claim endorsement and evidence quality. Our results showed that endorsers were significantly more persuaded by the evidence than non-endorsers, but both groups were significantly more persuaded by high-quality than low-quality evidence. The interaction between endorsement and evidence quality was not significant. These results suggest that the formation and maintenance of implausible beliefs by endorsers may result from less critical evidence evaluations rather than a failure to analyse. This is consistent with a limited rather than a lazy approach and suggests that interventions to develop analytical skill may be useful for minimising the effects of implausible claims.

Abstract 240: Serum Cholesterol and Angiotensin-Induced Abdominal Aortic Aneurysm in Hypercholesterolemic Mice

2013 ◽  
Vol 33 (suppl_1) ◽  
Author(s):  
Petra A Prins ◽  
Michael Hill ◽  
David Airey ◽  
Sam Nwosu ◽  
Prudhvidhar R Perati ◽  
...  
Keyword(s):  
Aortic Aneurysms ◽  
Cholesterol Concentration ◽  
Linear Mixed Effect Model ◽  
Hemodynamic Parameters ◽  
Mixed Effect ◽  
Regression Methods ◽  
Effect Model ◽  
Abdominal Aortic ◽  
And Control ◽  
The Relationship

Background Although hyperlipidemia is known to augment the incidence of abdominal aortic aneurysms (AAA) in the AngII-induced model of apolipoprotein E -/- mice, its relationship to AAA size is unknown. Therefore, we evaluated the relationship between total cholesterol concentration (TC) and change (delta) in aortic diameter. Methods TC was measured in 36 male mice that underwent a 4-week infusion period with saline (n=9) or AngII (1500 ng/kg/min; n=27), along with serial measurements of pulse rate (PR), and pulse (PP), mean arterial (MAP), systolic (SBP) and diastolic (DBP) pressure. A linear mixed effect model was used to assess the relationship between all hemodynamic parameters and delta. Nonparametric and linear regression methods were used to evaluate TC in relation to delta. Results TC did not differ between AngII and control mice (Figure, bottom left) (p=0.18). The burden of atherosclerosis was greater among AngII-exposed mice versus control, but did not differ by presence or size of AAA (Figure, bottom right). None of the hemodynamic parameters were predictive of delta (SBP, p = 0.66; DBP, p = 0.66; MAP, p = 0.55; PP, p = 0.66; and PR, p = 0.39). Mean TC was higher among mice with large versus small AAA (552.6 vs. 393.5 mg/ ml, p<0.05; Figure, top right). The nonparametric smoothing line (Figure, top left) suggests a first order relationship between delta and TC (p for trend < 0.001). AngII (ß = 0.48, p < 0.001) and TC (ß = 0.0015, p = 0.003) were independent predictors in the linear model for delta. Conclusions Our findings suggest that TC is incrementally associated with AAA size. These findings may have potential clinical relevance for risk assessment in AAA patients. Figure

Multilevel Zero-inflated Censored Beta Regression Modeling for Proportions and Rate Data with Extra-zeros

2019 ◽  
Author(s):  
Leili Tapak ◽  
Omid Hamidi ◽  
Majid Sadeghifar ◽  
Hassan Doosti ◽  
Ghobad Moradi
Keyword(s):  
Regression Model ◽  
Simulation Study ◽  
Real Data ◽  
P Value ◽  
Parameter Estimates ◽  
Beta Regression ◽  
Rate Data ◽  
Data Set ◽  
Proposed Model ◽  
Beta Regression Model

Abstract Objectives Zero-inflated proportion or rate data nested in clusters due to the sampling structure can be found in many disciplines. Sometimes, the rate response may not be observed for some study units because of some limitations (false negative) like failure in recording data and the zeros are observed instead of the actual value of the rate/proportions (low incidence). In this study, we proposed a multilevel zero-inflated censored Beta regression model that can address zero-inflation rate data with low incidence.Methods We assumed that the random effects are independent and normally distributed. The performance of the proposed approach was evaluated by application on a three level real data set and a simulation study. We applied the proposed model to analyze brucellosis diagnosis rate data and investigate the effects of climatic and geographical position. For comparison, we also applied the standard zero-inflated censored Beta regression model that does not account for correlation.Results Results showed the proposed model performed better than zero-inflated censored Beta based on AIC criterion. Height (p-value <0.0001), temperature (p-value <0.0001) and precipitation (p-value = 0.0006) significantly affected brucellosis rates. While, precipitation in ZICBETA model was not statistically significant (p-value =0.385). Simulation study also showed that the estimations obtained by maximum likelihood approach had reasonable in terms of mean square error.Conclusions The results showed that the proposed method can capture the correlations in the real data set and yields accurate parameter estimates.

To see or not to see: The roles of item properties and language knowledge in Chinese missing logographeme effect

2020 ◽  
Vol 41 (5) ◽  
pp. 1113-1139
Author(s):  
Xiuli Tong ◽  
Qinli Deng ◽  
Hélène Deacon ◽  
Jean Saint-Aubin ◽  
Suiping Wang
Keyword(s):  
Visual Complexity ◽  
Semantic Relatedness ◽  
Linear Mixed Effect Model ◽  
Mixed Effect ◽  
Effect Model ◽  
Bottom Position ◽  
Language Knowledge ◽  
Item Properties ◽  
The Right ◽  
Omission Rate

AbstractThis study examined how language knowledge and item properties (i.e., semantic relatedness and position) influenced Chinese missing logographeme effects. Eighty-four Chinese readers and 53 English readers were asked to search for the Chinese logographeme 口 while reading a Chinese prose passage. The target 口 appeared in five different positions (i.e., left, right, top, bottom, or inside), varying its degree of semantic relatedness to its embedded characters. The generalized linear mixed-effect model revealed a significant interaction between semantic relatedness and position in Chinese, but not in English, readers when visual complexity and frequency were controlled. For Chinese readers, a higher omission rate occurred when 口 appeared in the top and inside positions and exhibited low semantic relatedness with its embedded characters, whereas 口 was omitted more when it was positioned on the right and exhibited high semantic relatedness to its embedded characters. English readers exhibited a different omission pattern: 口 was omitted more when it appeared in the left or right position irrespective of semantic relatedness. In addition, 口 was omitted more in the inside, rather than the bottom, position. These findings suggest that the omission rate of the logographeme is determined by item properties at the sublexical level and the reader’s language knowledge.

Flexible quasi-beta regression models for continuous bounded data

2018 ◽  
Vol 19 (6) ◽  
pp. 617-633 ◽  
Author(s):  
Wagner H Bonat ◽  
Ricardo R Petterle ◽  
John Hinde ◽  
Clarice GB Demétrio
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Beta Regression ◽  
Estimating Function ◽  
Dispersion Parameters ◽  
Linear Predictor ◽  
Mean And Variance ◽  
The Mean ◽  
Bounded Data ◽  
Beta Regression Model

We propose a flexible class of regression models for continuous bounded data based on second-moment assumptions. The mean structure is modelled by means of a link function and a linear predictor, while the mean and variance relationship has the form [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text] are the mean, dispersion and power parameters respectively. The models are fitted by using an estimating function approach where the quasi-score and Pearson estimating functions are employed for the estimation of the regression and dispersion parameters respectively. The flexible quasi-beta regression model can automatically adapt to the underlying bounded data distribution by the estimation of the power parameter. Furthermore, the model can easily handle data with exact zeroes and ones in a unified way and has the Bernoulli mean and variance relationship as a limiting case. The computational implementation of the proposed model is fast, relying on a simple Newton scoring algorithm. Simulation studies, using datasets generated from simplex and beta regression models show that the estimating function estimators are unbiased and consistent for the regression coefficients. We illustrate the flexibility of the quasi-beta regression model to deal with bounded data with two examples. We provide an R implementation and the datasets as supplementary materials.

scholarly journals Differential Thermotolerance Adaptation between Species of Coccidioides

2020 ◽  
Vol 6 (4) ◽  
pp. 366
Author(s):  
Heather L. Mead ◽  
Paris S. Hamm ◽  
Isaac N. Shaffer ◽  
Marcus de Melo Teixeira ◽  
Christopher S. Wendel ◽  
...  
Keyword(s):  
Growth Rate ◽  
Linear Mixed Effect Model ◽  
Phenotypic Difference ◽  
Dimorphic Fungi ◽  
Mixed Effect ◽  
Valley Fever ◽  
Solid Media ◽  
Effect Model ◽  
Molecular Phylogenetic ◽  
Similar Growth

Coccidioidomycosis, or Valley fever, is caused by two species of dimorphic fungi. Based on molecular phylogenetic evidence, the genus Coccidioides contains two reciprocally monophyletic species: C. immitis and C. posadasii. However, phenotypic variation between species has not been deeply investigated. We therefore explored differences in growth rate under various conditions. A collection of 39 C. posadasii and 46 C. immitis isolates, representing the full geographical range of the two species, was screened for mycelial growth rate at 37 °C and 28 °C on solid media. The radial growth rate was measured for 16 days on yeast extract agar. A linear mixed effect model was used to compare the growth rate of C. posadasii and C. immitis at 37 °C and 28 °C, respectively. C. posadasii grew significantly faster at 37 °C, when compared to C. immitis; whereas both species had similar growth rates at 28 °C. These results indicate thermotolerance differs between these two species. As the ecological niche has not been well-described for Coccidioides spp., and disease variability between species has not been shown, the evolutionary pressure underlying the adaptation is unclear. However, this research reveals the first significant phenotypic difference between the two species that directly applies to ecological research.

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