Analysis of Multivariate Interval Censoring by Diabetic Retinopathy Study

[[abstract]]Multivariate failure time data are commonly encountered in biomedical research since each study subject may experience multiple events or because there exists clustering of subjects such that failure times within the same cluster are correlated. There are numerous statistical methods reported for the analysis of right-censored multivariate failure time data and among these. In this paper we use the frailty approach to catch the related survival variables and assume each event is a discrete analogue as an interval of clinical examinations periodically. For estimation, an Expectation Maximization (EM) algorithm is developed and is applied to the diabetic retinopathy study (DRS).[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]US

Statistical analysis of multivariate interval-censored failure time data

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on March 6, 2009)Thesis (Ph.D.) University of Missouri-Columbia 2007.A voluminous literature on right-censored failure time data has been developed in the past 30 years. Due to advances in biomedical research, interval censoring has become increasingly common in medical follow-up studies. In these cases, each study subject is examined or observed periodically, thus the observed failure time falls into a certain interval. Additional problems arise in the analysis of multivariate interval-censored failure time data. These include the estimating the correlation among failure times. The first part of this dissertation considers regression analysis of multivariate interval-censored failure time data using the proportional odds model. One situation in which the proportional odds model is preferred is when the covariate effects diminish over time. In contrast, if the proportional hazards model is applied for the situation, one may have to deal with time-dependent covariates. We present an inference approach for fitting the model to multivariate interval-censored failure time data. Simulation studies are conducted and an AIDS clinical trial is analyzed by using this methodology. The second part of this dissertation is devoted to the additive hazards model for multivariate interval-censored failure time data. In many applications, the proportional hazards model may not be appropriate and the additive hazards model provides an important and useful alternative. The presented estimates of regression parameters are consistent and asymptotically normal and a robust estimate of their covariance matrix is given that takes into account the correlation of the survival variables. Simulation studies are conducted for practical situations. The third part of this dissertation discusses regression analysis of multivariate interval censored failure time data using the frailty model approach. Based on the most commonly used regression model, the proportional hazards model, the frailty model approach considers the random effect directly models the correlation between multivariate failure times. For the analysis, we will focus on current status or case I interval-censored data and the maximum likelihood approach is developed for inference. The simulation studies are conducted to asses and compare the finite-sample behaviors of the estimators and we apply the proposed method to an animal tumorigenicity experiment.Includes bibliographical reference

(μ-Ethane-1,2-diamine-κ2 N:N′)bis­[bis­(ethane-1,2-diamine-κ2 N,N′)zinc(II)] tetra­kis­(perchlorate)

In the title salt, [Zn2(C2H8N2)5](ClO4)4, an ethyl­enediamine mol­ecule bridges two bis­(ethyl­enediamine)­zinc units; the five-coordinate Zn atoms show a trigonal–bipyramidal coordination geometry that is distorted towards square-pyramidal (that of one Zn atom is distorted by 12% and that of the other by 34%). The perchlorate ions are all disordered over two positions in a 1:1 ratio. The cation inter­acts weakly with the anion by N—H⋯O hydrogen bonds, generating a three-dimensional network

5,6-Dimethyl-1,2,4-triazin-3-amine

In the crystal structure of the title compound, C5H8N4, adjacent mol­ecules are connected through N—H⋯N hydrogen bonds, resulting in a zigzag chain along [100]. The amino groups and heterocyclic N atoms are involved in further N—H⋯N hydrogen bonds, forming R 2 2(8) motifs

catena-Poly[[(isoquinoline-κN)(triphenylphospane-κP)copper(I)]-μ-thio­cyanato-κ2 N:S]

In the title coordination compound, [Cu(NCS)(C9H7N)(C18H15P)]n, the CuI atom is tetra­hedrally coordinated by one N atom from an isoquinoline ligand, one P atom from a triphenyl­phospane ligand, and one N and one S atom from two thio­cyanate anions. The thio­cyanide anions bridge the CuI atoms into a chain along [100]. π–π inter­actions between the pyridine and benzene rings of the isoquinoline ligands connect the chains [centroid-to-centroid distance = 3.722 (3) Å]