Investigating the correlation structure of quadrivariate udder infection times through hierarchical Archimedean copulas

The correlation structure imposed on multivariate time to event data is often of a simple nature, such as in the shared frailty model where pairwise correlations between event times in a cluster are all the same. In modeling the infection times of the four udder quarters clustered within the cow, more complex correlation structures are possibly required, and if so, such more complex correlation structures give more insight in the infection process. In this article, we will choose a marginal approach to study more complex correlation structures, therefore leaving themodeling ofmarginal distributions unaffected by the association parameters. The dependency of failure times will be induced through copula functions. The methods are shown for (mixtures of) the Clayton copula, but can be generalized to mixtures of Archimedean copulas for which the nesting conditions are met (McNeil in J Stat Comput Simul 6:567-581, 2008; Hofert in Comput Stat Data Anal 55:57-70, 2011)

Extending the Archimedean copula methodology to model multivariate survival data grouped in clusters of variable size

For the analysis of clustered survival data, two different types of model that take the association into account are commonly used: frailty models and copula models. Frailty models assume that, conditionally on a frailty term for each cluster, the hazard functions of individuals within that cluster are independent. These unknown frailty terms with their imposed distribution are used to express the association between the different individuals in a cluster. Copula models in contrast assume that the joint survival function of the individuals within a cluster is given by a copula function, evaluated in the marginal survival function of each individual. It is the copula function which describes the association between the lifetimes within a cluster. A major disadvantage of the present copula models over the frailty models is that the size of the different clusters must be small and equal to set up manageable estimation procedures for the different model parameters. We describe a copula model for clustered survival data where the clusters are allowed to be moderate to large and varying in size by considering the class of Archimedean copulas with completely monotone generator. We develop both one- and two-stage estimators for the copula parameters. Furthermore we show the consistency and asymptotic normality of these estimators. Finally, we perform a simulation study to investigate the finite sample properties of the estimators. We illustrate the method on a data set containing the time to first insemination in cows, with cows clustered in herds

Relevance of Geriatric Assessment in Older Patients With Colorectal Cancer

This study aims to evaluate the relevance of geriatric assessment (GA) in older patients with colorectal cancer (CRC) and to study functional status (FS) and chemotherapy-related toxicity during treatment.status: publishe

Marker genes for the non-immune clusters.

List of marker genes that were identified using the FindAllMarkers function for all non-immune clusters as shown in Fig 5. Table contains the gene name (rowname and gene), the p-value (p_val), the average log2 fold change compared to all other clusters (avg_log2FC), percent of cells expressing the gene in cluster of interest (pct.1), percent of cells expressing the genes in all other clusters (pct. 2), adjusted p-value (p_val_adj) and the cluster to which the gene belongs (cluster). (XLSX)</p

Output of the GSEA analysis for arterial blood endothelial cells.

Overview of the hallmark pathways, their p-value (pval), adjusted p-value (padj), enrichment score (ES), normalised enrichment score (NES), number of times a random gene set had a more extreme enrichment score value (nMoreExtreme), size of the pathway after removing genes not present in list of differentially expressed genes (size) and a vector with indexes of leading edge genes that drive the enrichment (leadingEdge) when comparing the different conditions (CONvsd8, CONvsd12 and d8vsd12) for all arterial blood endothelial cells as shown in Fig 5H. (XLSX)</p

Marker genes for the main clusters.

List of marker genes that were identified using the FindAllMarkers function for all main clusters as shown in Fig 2. Table contains the gene name (rowname and gene), the p-value (p_val), the average log2 fold change compared to all other clusters (avg_log2FC), percent of cells expressing the gene in cluster of interest (pct.1), percent of cells expressing the genes in all other clusters (pct. 2), adjusted p-value (p_val_adj) and the cluster to which the gene belongs (cluster). (XLSX)</p

Expression levels of <i>Vegfa</i> and <i>Mki67</i> in pulmonary nonimmune cell populations.

Non-immune cell clusters (Fig 5) from control (CON), PbNK65-infected (d8) and ART+CQ-treated (d12) mice were checked for the expression of different genes. Expression levels of vascular endothelial growth factor (Vegfa; A) and Marker of proliferation Ki-67 (Mki67; B) in the different nonimmune cell populations in all three conditions are shown. (TIF)</p

No increase in CD45^- CD31^- cells was observed upon <i>Pb</i>NK65 infection.

PbNK65-infected C57BL/6 mice were treated daily from 8 until 12 dpi with 10 mg/kg artesunate + 30 mg/kg chloroquine (ART+CQ). Mice were dissected at 8 dpi for the untreated (UT), PbNK65-infected mice and at 12 or 15 dpi for the ART+CQ-treated, PbNK65-infected C57BL/6 mice. Uninfected mice were used as controls (CON). Cells were isolated from the lungs and flow cytometry was performed. (A) Representative FACS plots showing CD45 and CD31 expression on all live single cells, with ECs (CD45- CD31+) and leukocytes (CD45+ CD31-) gated in black and CD45- CD31- population in orange. (B) The absolute number of CD45- CD31+ in the lungs was calculated. Data from two to five experiments. Each symbol represents an individual mouse. Horizontal black lines indicate the median. n = 16–18 for CON, n = 10 for UT d8, n = 20 for ART+CQ d12, n = 14 for ART+CQ d15. (TIF)</p