Semiparametric Methods for Clustered Recurrent Event Data

In biomedical studies, the event of interest is often recurrent and within-subject events cannot usually be assumed independent. In addition, individuals within a cluster might not be independent; for example, in multi-center or familial studies, subjects from the same center or family might be correlated. We propose methods of estimating parameters in two semi-parametric proportional rates/means models for clustered recurrent event data. The first model contains a baseline rate function which is common across clusters, while the second model features cluster-specific baseline rates. Dependence structures for patients-within-cluster and events-within-patient are both unspecified. Estimating equations are derived for the regression parameters. For the common baseline model, an estimator of the baseline mean function is proposed. The asymptotic distributions of the model parameters are derived, while finite-sample properties are assessed through a simulation study. Using data from a national organ failure registry, the proposed methods are applied to the analysis of technique failures among Canadian dialysis patients.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46817/1/10985_2005_Article_2970.pd

Simulation model of renal replacement therapy: Predicting future demand in England

Background. The demand for renal replacement therapy (RRT) in England has risen steadily, although from a lower base than many other developed countries. Predicting the future demand for RRT and the impact of factors such as the acceptance rate, transplant supply and patient survival, is required in order to inform the planning of such services. Methods. A discrete event simulation model estimates the future demand for RRT in England in 2010 for a range of scenarios. The model uses current prevalence and current and projected future acceptance rates, survival rates and the transitions between modalities to predict future patient numbers. National population and mortality data, published literature and data from the UK Renal Registry and UK Transplant, are used to estimate unmet need for RRT, the impact of changing demography and incidence of Type 2 diabetes, patient haemodialysis (HD) survival and transplant supply. Results. By 2010 the predicted prevalence will have increased from about 30 000 in 2000 to between 42 and 51 000 (900–1000 p.m.p.), an average annual growth of 4.5–6%. Changing transplant supply has a small effect on overall numbers but changes the proportion of patients with functioning graft by up to 8%. Even with an optimistic increase in transplant supply (11% p.a. for 5 years), numbers on HD will continue to rise substantially, especially in the elderly. The factors most influencing future patient numbers are the acceptance rate and dialysis survival. Conclusion. This model predicts a substantial growth in the RRT population to 2010 to a rate approaching 1000 p.m.p., particularly in the elderly and those on HD, with a steady state not being reached for at least 25 years

Thermal Hydraulic Behavior of the First ITER CS Module

The ITER Central Solenoid (CS) modules are under fabrication by the US ITER organization and its subcontractors. US ITER will supply seven modules to the ITER Organization (IO), six of which will be assembled in a stack that forms the ITER Central Solenoid, the last one being spare. The first module, namely CSM 1 was manufactured by General Atomics (GA) and went through Factory Acceptance Tests (FAT) including high voltage testing, Paschen testing and then cold test at 4.5 K and up to 40.0 kA in order to demonstrate compliance with coil performance requirements. The paper focuses on the results of the first CS Module thermal hydraulic characterization without current (and field). Pressure drops, transit time, and thermal coupling between pancakes are presented. Analysis results and tests are also compared