Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

International Collection of JGOFS (Joint Global Ocean Flux Study), Volume 2: Integrated Data Sets (1989-2003)

The JGOFS International Collection Volume 2: Integrated Data Sets CD is a coherent, organised compilation of existing data sets produced by member countries which participated in JGOFS. In most cases, the data were gathered from the JGOFS International Collection, Volume 1: Discrete Datasets DVD. To produce Vol. 1 data were taken from the original sources and copied "as is" on the DVD. For Vol. 2 data and metadata have been harmonized using the conversion software PanTool and the import routine of PANGAEA checking for completeness of metadata and defining the relations between data and metadata. Prior to the import, data had performed a technical quality control, i.e. format and readability of the file, availability and combination of parameters and units, range of values.