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Multi-omics Analysis
Published in Altuna Akalin, Computational Genomics with R, 2020
Multiple factor analysis is a natural starting point for a discussion about matrix factorization methods for integrating multiple data types. It is a straightforward extension of PCA into the domain of multiple data types 1.
T
Published in Anton Sebastian, A Dictionary of the History of Medicine, 2018
Thurston, Louis Leon (1887–1955) Professor of psychology at Chicago University (1927–1952). His main interest was intelligence testing and he devised several tests. He wrote Vectors of Mind (1935) and Multiple-factor Analysis in 1947.
Factors predicting rehabilitation outcomes after severe acquired brain injury in trauma, stroke and anoxia populations: A cohort study
Published in Neuropsychological Rehabilitation, 2022
The Motor subscale comprises the summed scores of items covering Eating, Grooming, Bathing, Dressing, Toileting, Bladder/Bowel Management, Transfers, Locomotion and Managing Stairs. The Motor subscale was not subdivided for analysis, to avoid drastically increasing model complexity and reducing power. The subscale also has generally good cohesiveness demonstrated in multiple factor analysis studies, which supports its use as a single outcome measure (Gunn et al., 2018; Hawley et al., 1999; Nayar et al., 2016; Turner-Stokes & Siegert, 2013). Further, it has excellent internal consistency (α = .96–.97) (Hobart et al., 2001; Turner-Stokes & Siegert, 2013), good item kappa values for interrater reliability (most items ≥.75) (McPherson et al., 1996) and excellent overall subscale interrater reliability (.98) (Hobart et al., 2001).
The effect of the detection of minimal residual disease for the prognosis and the choice of post-remission therapy of intermediate-risk acute myeloid leukemia without FLT3-ITD, NPM1 and biallelic CEBPA mutations
Published in Hematology, 2021
Wen-shuai Zheng, Ya-lei Hu, Li-xun Guan, Bo Peng, Shen-yu Wang
SPSS 20.0 was used for statistical analysis. Firstly, the normality test was conducted for all continuous variables, and mean ± standard deviation (SD) was used to describe the variables that followed normal distribution. Variables that don’t conform to normal distribution were described by median and quartile. Comparisons of patient characteristics between two groups were performed by independent-samples T test, Mann–Whitney U test, Chi-square or Fisher’s exact test. CRI was calculated by Kaplan-Meier method. Survival analysis was carried out by Kaplan-Meier method, and the difference between groups was compared by log-rank method. The factors affecting relapse was analyzed by Logistic analysis. The factors with P < 0.05 were entered into Cox regression model for multiple-factor analysis. Hazard ratios were presented with 95% confidence intervals (95% CI). A P value of <0.05 was considered to be statistically significant.
A statistical procedure for representing state fragility and transition paths
Published in Journal of Applied Statistics, 2019
Marcella Corduas, Giancarlo Ragozini
The statistical procedure that we propose can be summarized as follows: multiple factor analysis (MFA) is carried out to combine the data tables into a common representation of the observations, namely the compromise, that depicts the global degree of fragility of countries [6,1];the low-dimensional factorial map is rotated in order to find a standpoint, rooted in the most fragile country, from which other countries can be seen, and clusters and outliers are identified;using MFA partial scores, the trajectories of countries are graphically displayed in the compromise space;finally, the trajectories are extrapolated exploiting the regression models that relate the first factorial axes to a limited number of explanatory variables. The conditional predictions of country coordinates are obtained according to a scenario elaborated from the documents and texts concerning the outlooks of main international organizations.