List augmentation with model based multiple imputation: a case study using a mixed-outcome factor model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73954/1/1467-9574.00220.pd

Endothelin 1 levels in relation to clinical presentation and outcome of Henoch Schonlein purpura

<p>Abstract</p> <p>Background</p> <p>Henoch Schonlein purpura (HSP) is a common vasculitis of small vessels whereas endothelin-1 (ET-1) is usually reported elevated in vasculities and systematic inflammation. The aim of the present study was to investigate whether ET-1 levels are correlated with the clinical presentation and the outcome of HSP.</p> <p>Methods</p> <p>The study sample consisted of thirty consecutive patients with HSP. An equal number of healthy patients of similar age and the same gender were served as controls. The patients' age range was 2–12.6 years with a mean ± SD = 6.3 ± 3 years. All patients had a physical examination with a renal, and an overall clinical score. Blood and urinary biochemistry, immunology investigation, a skin biopsy and ET-1 measurements in blood and urine samples were made at presentation, 1 month later and 1 year after the appearance of HSP. The controls underwent the same investigation with the exception of skin biopsy.</p> <p>Results</p> <p>ET-1 levels in plasma and urine did not differ between patients and controls at three distinct time points. Furthermore the ET-1 were not correlated with the clinical score and renal involvement was independent from the ET-1 measurements. However, the urinary ET-1 levels were a significant predictor of the duration of the acute phase of HSP (HR = 0.98, p = 0.032, CI0.96–0.99). The ET-1 levels did not correlate with the duration of renal involvement.</p> <p>Conclusion</p> <p>Urinary ET-1 levels are a useful marker for the duration of the acute phase of HSP but not for the length of renal involvement.</p

Latent variable models for ordinal data

The chapter describes an overview of the recent developments of latent variable models for ordinal data in the context of generalized linear latent variable models. The estimatin, goodness of fit and the model for longitudinal data are also discussed. An example to a real data set is illustrated

Bounded-influence robust estimation in generalized linear latent variable models

Latent variable models are used for analyzing multivariate data. Recently, generalized linear latent variable models for categorical, metric, and mixed-type responses estimated via maximum likelihood (ML) have been proposed. Model deviations, such as data contamination, are shown analytically, using the influence function and through a simulation study, to seriously affect ML estimation. This article proposes a robust estimator that is made consistent using the basic principle of indirect inference and can be easily numerically implemented. The performance of the robust estimator is significantly better than that of the ML estimators in terms of both bias and variance. A real example from a consumption survey is used to highlight the consequences in practice of the choice of the estimator