Purpose: General linear modeling (GLM) is usually applied to investigate factors associated with the domains of Quality of Life (QOL). A summation score in a specific sub-domain is regressed by a statistical model inc...Purpose: General linear modeling (GLM) is usually applied to investigate factors associated with the domains of Quality of Life (QOL). A summation score in a specific sub-domain is regressed by a statistical model including factors that are associated with the sub-domain. However, using the summation score ignores the influence of individual questions. Structural equation modeling (SEM) can account for the influence of each question’s score by compositing a latent variable from each question of a sub-domain. The objective of this study is to determine whether a conventional approach such as GLM, with its use of the summation score, is valid from the standpoint of the SEM approach. Method: We used the Japanese version of the Maugeri Foundation Respiratory Failure Questionnaire, a QOL measure, on 94 patients with heart failure. The daily activity sub-domain of the questionnaire was selected together with its four accompanying factors, namely, living together, occupation, gender, and the New York Heart Association’s cardiac function scale (NYHA). The association level between individual factors and the daily activity sub-domain was estimated using SEM?and GLM, respectively. The standard partial regression coefficients of GLM and standardized path coefficients of SEM were compared. If?these coefficients were similar (absolute value of the difference -0.06 and -0.07 for the GLM and SEM. Likewise, the estimates of occupation, gender, and NYHA were -0.18 and -0.20, -0.08 and -0.08, 0.51 and 0.54, respectively. The absolute values of the difference for each factor were 0.01, 0.02, 0.00, and 0.03, respectively. All differences were less than 0.05. This means that these two approaches lead to similar conclusions. Conclusion: GLM is a valid method for exploring association factors with a domain in QOL.展开更多
To find the quantitative trait loci associated with wood density in teak(Tectona grandis L.f.), 21 co-dominant markers including 13 site specific recombinase and 8 EST-based co-dominant markers designed from lignin bi...To find the quantitative trait loci associated with wood density in teak(Tectona grandis L.f.), 21 co-dominant markers including 13 site specific recombinase and 8 EST-based co-dominant markers designed from lignin biosynthesis genes were applied to 174 teak plus tree clones at the National Germplasm Bank, Chandrapur,India. The germplasm bank exhibited 10.6% coefficient of variation for wood densities with 84.5 ± 31.3 genetic polymorphism(%). The highly panmictic set of genotypes(FST= 0.035 ± 0.004) harbored 96.47 ± 0.40 genetic variability(%). The average allelic frequency of the 21 codominant markers was 0.65 ± 0.11 with 12.9% pairs of loci in significant LD(p\0.05, R^2 values [ 0.1), confirming their suitability for a strong marker-trait association study. The marker CCoAMT-1 was significantly(p\0.01) associated with wood density showing stability by both GLM and MLM models and explained 4.3% of the phenotypic effect. The marker from the EST representing CCoAMT can be further developed for gene-assisted selection of elite genotypes of teak with greater wood density. Therefore, we believe that the report will help accelerate the genetic improvement and advance the breeding program of the species.展开更多
An investigation using recall questionnaires was conducted in winter and autumn 2006 to evaluate the time-activity patterns in Chongqing,China.The average time spent in seven microenvironments(MEs)including outdoors,t...An investigation using recall questionnaires was conducted in winter and autumn 2006 to evaluate the time-activity patterns in Chongqing,China.The average time spent in seven microenvironments(MEs)including outdoors,transit,living room,bedroom,kitchen,classroom/office,and other indoors were found to be about 3.5,1.1,2.5,9.7,1.4,4.2,and 1.7 h per day,respectively.According to the results of a nonparametric test,the sampling period and day of week were significant for the variation of the time spent in all MEs except for transit and outdoors.The time budget was analyzed using a general linear model(GLM),which exhibited significant variability by demographic factors such as gender,age,residence,education,and household income.展开更多
文摘Purpose: General linear modeling (GLM) is usually applied to investigate factors associated with the domains of Quality of Life (QOL). A summation score in a specific sub-domain is regressed by a statistical model including factors that are associated with the sub-domain. However, using the summation score ignores the influence of individual questions. Structural equation modeling (SEM) can account for the influence of each question’s score by compositing a latent variable from each question of a sub-domain. The objective of this study is to determine whether a conventional approach such as GLM, with its use of the summation score, is valid from the standpoint of the SEM approach. Method: We used the Japanese version of the Maugeri Foundation Respiratory Failure Questionnaire, a QOL measure, on 94 patients with heart failure. The daily activity sub-domain of the questionnaire was selected together with its four accompanying factors, namely, living together, occupation, gender, and the New York Heart Association’s cardiac function scale (NYHA). The association level between individual factors and the daily activity sub-domain was estimated using SEM?and GLM, respectively. The standard partial regression coefficients of GLM and standardized path coefficients of SEM were compared. If?these coefficients were similar (absolute value of the difference -0.06 and -0.07 for the GLM and SEM. Likewise, the estimates of occupation, gender, and NYHA were -0.18 and -0.20, -0.08 and -0.08, 0.51 and 0.54, respectively. The absolute values of the difference for each factor were 0.01, 0.02, 0.00, and 0.03, respectively. All differences were less than 0.05. This means that these two approaches lead to similar conclusions. Conclusion: GLM is a valid method for exploring association factors with a domain in QOL.
基金partially funded in the form of Senior Research Fellowship(vide No.09/1164(0001)/2016-EMR-I)awarded to the first author(Vivek Vaishnav)by Government of India Council of Scientific and Industrial Research,New Delhi,which is gratefully acknowledged
文摘To find the quantitative trait loci associated with wood density in teak(Tectona grandis L.f.), 21 co-dominant markers including 13 site specific recombinase and 8 EST-based co-dominant markers designed from lignin biosynthesis genes were applied to 174 teak plus tree clones at the National Germplasm Bank, Chandrapur,India. The germplasm bank exhibited 10.6% coefficient of variation for wood densities with 84.5 ± 31.3 genetic polymorphism(%). The highly panmictic set of genotypes(FST= 0.035 ± 0.004) harbored 96.47 ± 0.40 genetic variability(%). The average allelic frequency of the 21 codominant markers was 0.65 ± 0.11 with 12.9% pairs of loci in significant LD(p\0.05, R^2 values [ 0.1), confirming their suitability for a strong marker-trait association study. The marker CCoAMT-1 was significantly(p\0.01) associated with wood density showing stability by both GLM and MLM models and explained 4.3% of the phenotypic effect. The marker from the EST representing CCoAMT can be further developed for gene-assisted selection of elite genotypes of teak with greater wood density. Therefore, we believe that the report will help accelerate the genetic improvement and advance the breeding program of the species.
基金This work was supported by the Environmental Decision Making in China(DEMAND)funded by the Norwegian Agency for Development Cooperation(No.CHN-2087).
文摘An investigation using recall questionnaires was conducted in winter and autumn 2006 to evaluate the time-activity patterns in Chongqing,China.The average time spent in seven microenvironments(MEs)including outdoors,transit,living room,bedroom,kitchen,classroom/office,and other indoors were found to be about 3.5,1.1,2.5,9.7,1.4,4.2,and 1.7 h per day,respectively.According to the results of a nonparametric test,the sampling period and day of week were significant for the variation of the time spent in all MEs except for transit and outdoors.The time budget was analyzed using a general linear model(GLM),which exhibited significant variability by demographic factors such as gender,age,residence,education,and household income.
