Application of the disposition model to breast cancer data

In this paper, we have presented the second level nesting of Bonney's disposition model (Bonney, 1998) and examined the implications of higher level nesting of the disposition model in relation to the dimension of the parameter space. We have also compared the performance of the disposition model with Cox's regression model (Cox, 1972). It has been observed that the disposition model has a very large number of unknown parameters, and is therefore limited by the method of estimation used. In the case of the maximum likelihood method, reasonable estimates are obtained if the number of parameters in the model is at most nine. This corresponds to about four to seven covariates. Since each covariate in Cox's model provides a parameter, it is possible to include more covariates in the regression analysis. On the other hand, as opposed to Cox's model, the disposition model is fitted with parameters to capture aggregation in families, if there should be any. The choice of a particular model should therefore depend on the available data set and the purpose of the statistical analysis. --Second level nesting,Proportional hazards model,Quadratic exponential form,Partial likelihood,Familial aggregation,Second-order methods,Marginal models,Conditional models

Application of the Disposition Model to Breast Cancer Data

In this paper, we have presented the second level nesting of Bonney’s disposition model (Bonney, 1998) and examined the implications of higher level nesting of the disposition model in relation to the dimension of the parameter space. We have also compared the performance of the disposition model with Cox’s regression model (Cox, 1972). It has been observed that the disposition model has a very large number of unknown parameters, and is therefore limited by the method of estimation used. In the case of the maximum likelihood method, reasonable estimates are obtained if the number of parameters in the model is at most nine. This corresponds to about four to seven covariates. Since each covariate in Cox’s model provides a parameter, it is possible to include more covariates in the regression analysis. On the other hand, as opposed to Cox’s model, the disposition model is fitted with parameters to capture aggregation in families, if there should be any. The choice of a particular model should therefore depend on the available data set and the purpose of the statistical analysis

Correlated Weibull regression model for multivariate binary data

The correlated Weibull regression model for the analysis of correlated binary data is presented. This regression model is based on Bonney’s disposition model for the regression analysis of correlated binary outcomes. Parameter estimation was done through the maximum likelihood method. The correlated Weibull regression model was contrasted with the correlated logistic regression model. The results showed that both regression models were useful in explaining the familial aggregation of oesophageal cancer. The correlated logistic regression model fitted the oesophageal cancer data better than the correlated Weibull regression model for both the non-nested and nested cases. Furthermore, the correlated logistic regression model was computationally more attractive than the correlated Weibull regression model

Correlated Weibull Regression Model for Multivariate Binary Data

The correlated Weibull regression model for the analysis of correlated binary data is presented. This regression model is based on Bonney’s disposition model for the regression analysis of correlated binary outcomes. Parameter estimation was done through the maximum likelihood method. The correlated Weibull regression model was contrasted with the correlated logistic regression model. The results showed that both regression models were useful in explaining the familial aggregation of oesophageal cancer. The correlated logistic regression model fitted the oesophageal cancer data better than the correlated Weibull regression model for both the non-nested and nested cases. Furthermore, the correlated logistic regression model was computationally more attractive than the correlated Weibull regression model

Polymorphisms of genes coding for ghrelin and its receptor in relation to colorectal cancer risk: a two-step gene-wide case-control study

<p>Abstract</p> <p>Background</p> <p>Ghrelin, an endogenous ligand for the growth hormone secretagogue receptor (GHSR), has two major functions: the stimulation of the growth hormone production and the stimulation of food intake. Accumulating evidence also indicates a role of ghrelin in cancer development.</p> <p>Methods</p> <p>We conducted a case-control study to examine the association of common genetic variants in the genes coding for ghrelin (GHRL) and its receptor (GHSR) with colorectal cancer risk. Pairwise tagging was used to select the 11 polymorphisms included in the study. The selected polymorphisms were genotyped in 680 cases and 593 controls from the Czech Republic.</p> <p>Results</p> <p>We found two SNPs associated with lower risk of colorectal cancer, namely SNPs rs27647 and rs35683. We replicated the two hits, in additional 569 cases and 726 controls from Germany.</p> <p>Conclusion</p> <p>A joint analysis of the two populations indicated that the T allele of rs27647 SNP exerted a protective borderline effect (P_trend= 0.004).</p

Alpha-Glucan, Water Dikinase 1 Affects Starch Metabolism and Storage Root Growth in Cassava (Manihot esculenta Crantz)

Abstarct Regulation of storage root development by source strength remains largely unknown. The cassava storage root delay (srd) T-DNA mutant postpones storage root development but manifests normal foliage growth as wild-type plants. The SRD gene was identified as an orthologue of α-glucan, water dikinase 1 (GWD1), whose expression is regulated under conditions of light/dark cycles in leaves and is associated with storage root development. The GWD1-RNAi cassava plants showed both retarded plant and storage root growth, as a result of starch excess phenotypes with reduced photosynthetic capacity and decreased levels of soluble saccharides in their leaves. These leaves contained starch granules having greatly increased amylose content and type C semi-crystalline structures with increased short chains that suggested storage starch. In storage roots of GWD1-RNAi lines, maltose content was dramatically decreased and starches with much lower phosphorylation levels showed a drastically reduced β-amylolytic rate. These results suggested that GWD1 regulates transient starch morphogenesis and storage root growth by decreasing photo-assimilation partitioning from the source to the sink and by starch mobilization in root crops