Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

High NRBP1 expression in prostate cancer is linked with poor clinical outcomes and increased cancer cell growth

We recently established the rationale that NRBP1 (nuclear receptor binding protein 1) has a potential growth-promoting role in cell biology. NRBP1 interacts directly with TSC-22, a potential tumor suppressor gene that is differently expressed in prostate cancer. Consequently, we analyzed the role of NRBP1 expression in prostate cancer cell lines and its expression on prostate cancer tissue microarrays (TMA).

Development and simultaneous application of multiple care protocols in critical care: a multicenter feasibility study

OBJECTIVE: To test the feasibility of and interactions among three software-driven critical care protocols. DESIGN: Prospective cohort study. SETTING: Intensive care units in six European and American university hospitals. PATIENTS: 174 cardiac surgery and 41 septic patients. INTERVENTIONS: Application of software-driven protocols for cardiovascular management, sedation, and weaning during the first 7 days of intensive care. MEASUREMENTS AND RESULTS: All protocols were used simultaneously in 85% of the cardiac surgery and 44% of the septic patients, and any one of the protocols was used for 73 and 44% of study duration, respectively. Protocol use was discontinued in 12% of patients by the treating clinician and in 6% for technical/administrative reasons. The number of protocol steps per unit of time was similar in the two diagnostic groups (n.s. for all protocols). Initial hemodynamic stability (a protocol target) was achieved in 26+/-18 min (mean+/-SD) in cardiac surgery and in 24+/-18 min in septic patients. Sedation targets were reached in 2.4+/-0.2h in cardiac surgery and in 3.6 +/-0.2h in septic patients. Weaning protocol was started in 164 (94%; 154 extubated) cardiac surgery and in 25 (60%; 9 extubated) septic patients. The median (interquartile range) time from starting weaning to extubation (a protocol target) was 89 min (range 44-154 min) for the cardiac surgery patients and 96 min (range 56-205 min) for the septic patients. CONCLUSIONS: Multiple software-driven treatment protocols can be simultaneously applied with high acceptance and rapid achievement of primary treatment goals. Time to reach these primary goals may provide a performance indicator.