Simultaneous Factor Selection and Collapsing Levels in ANOVA

Bondell, Howard D.; Reich, Brian J.

doi:10.1111/j.1541-0420.2008.01061.x

Cited by 82 publications

(107 citation statements)

References 22 publications

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“…This raises a number of issues, including how to form sensible subgroup combinations and, from a technical standpoint, how to weight each of the effect estimates to produce a combined estimate (see e.g., [42,43]). It is beyond the scope of this article to examine this area in detail, but in some additional analyses in which estimates arising from several subgroups in our case-study were combined based on clinical similarity, we found some evidence of an above average treatment effect in the combined subpopulation.…”

Section: Discussionmentioning

confidence: 99%

Bayesian models for subgroup analysis in clinical trials

et al. 2011

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Section: Discussionmentioning

confidence: 99%

Bayesian models for subgroup analysis in clinical trials

et al. 2011

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“…These tests allow for the comparison of all haplotypes to determine the effect structure. Following the idea of the penalized approach to ANOVA9, we estimate the effects ( γ 0 , γ , β ) by ( γ̂ 0 , γ̂ , β̂ ), which is the minimizer of…”

Section: Methodsmentioning

confidence: 99%

A comprehensive approach to haplotype-specific analysis by penalized likelihood

Tzeng

Bondell

2009

Eur J Hum Genet

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Haplotypes can hold key information to understand the role of candidate genes in disease etiology. However, standard haplotype analysis has yet been able to fully reveal the information retained by haplotypes. In most analysis, haplotype inference focuses on relative effects compared to an arbitrarily-chosen baseline haplotype. It does not depict the effect structure unless an additional inference procedure is used in a secondary post-hoc analysis, and such analysis tends to be lack of power. In this work, we propose a penalized regression approach to systematically evaluate the pattern and structure of the haplotype effects. By specifying an L1 penalty on the pairwise difference of the haplotype effects, we present a model-based haplotype analysis to detect and to characterize the haplotypic association signals. The proposed method avoids the need to choose a baseline haplotype; it simultaneously carries out the effect estimation and effect comparison of all haplotypes, and outputs the haplotype group structure based on their effect size. Finally, our penalty weights are theoretically designed to balance the likelihood and the penalty term in an appropriate manner. The proposed method can be used as a tool to comprehend candidate regions identified from a genome or chromosomal scan. Simulation studies reveal the better abilities of the proposed method to identify the haplotype effect structure compared to the traditional haplotype association methods, demonstrating the informativeness and powerfulness of the proposed method.

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“…Including the nostril factor in the model allowed for control of the variability of the response variable area among different cadavers. These characteristics of the repeated measures model allowed the comparisons among the means of the response variable under the different treatments to be highly accurate [14][15][16][17] .…”

Section: Nostril Cross-section Area Evaluation and Statistical Analysismentioning

confidence: 99%

Modified technique to increase nostril cross-sectional area after using rib and septal cartilage graft over alar nasal cartilages

Sá¹,

Alonso²

2012

Acta Cir. Bras.

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PURPOSE: Describe a modified technique to increase nostril cross-sectional area using rib and septal cartilage graft over alar nasal cartilages. METHODS: A modified surgical technique was used to obtain, carve and insert cartilage grafts over alar nasal cartilages. This study used standardized pictures and measured 90 cadaveric nostril cross-sectional area using Autocad®; 30 were taken before any procedure and 60 were taken after grafts over lateral crura (30 using costal cartilage and 30 using septal cartilage). Statistical analysis were assessed using a model for repeated measures and ANOVA (Analysis of Variance) for the variable "area". RESULTS: There's statistical evidence that rib cartilage graft is more effective than septal cartilage graft. The mean area after the insertion of septal cartilage graft is smaller than the mean area under rib graft treatment (no confidence interval for mean difference contains the zero value and all P-values are below the significance level of 5%). CONCLUSIONS: The technique presented is applicable to increase nostril cross section area in cadavers. This modified technique revealed to enhance more nostril cross section area with costal cartilage graft over lateral crura rather than by septal graft.

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Simultaneous Factor Selection and Collapsing Levels in ANOVA

Cited by 82 publications

References 22 publications

Bayesian models for subgroup analysis in clinical trials

Bayesian models for subgroup analysis in clinical trials

A comprehensive approach to haplotype-specific analysis by penalized likelihood

Modified technique to increase nostril cross-sectional area after using rib and septal cartilage graft over alar nasal cartilages

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