Bayesian modeling of multiple episode occurrence and severity with a terminating event

An individual's health condition can affect the frequency and intensity of episodes that can occur repeatedly and that may be related to an event time of interest. For example, bleeding episodes during pregnancy may indicate problems predictive of preterm delivery. Motivated by this application, we propose a joint model for a multiple episode process and an event time. The frequency of occurrence and severity of the episodes are characterized by a latent variable model, which allows an individual's episode intensity to change dynamically over time. This latent episode intensity is then incorporated as a predictor in a discrete time model for the terminating event. Time-varying coefficients are used to distinguish among effects earlier versus later in gestation. Formulating the model within a Bayesian framework, prior distributions are chosen so that conditional posterior distributions are conjugate after data augmentation. Posterior computation proceeds via an efficient Gibbs sampling algorithm. The methods are illustrated using bleeding episode and gestational length data from a pregnancy study.     

A critical role of LFA-1 in the development of Th17 cells and induction of experimental autoimmune encephalomyelytis

Snu13p is a Saccharomyces cerevisiae protein essential for pre-messenger RNA splicing and pre-ribosomal RNA processing. Snu13p binds U4 snRNA of the spliceosome and box C/D snoRNAs of the pre-ribosomal RNA processing machinery to induce assembly of each ribonucleoprotein complex. Here, we present structural and biochemical analysis of Snu13p. The crystal structure of Snu13p reveals a region of the protein which could be important for protein interaction during ribonucleoprotein assembly. Using the structure of Snu13p we have designed the first temperature-sensitive mutants in Snu13p, L67W and I102A. Wild-type and mutant Snu13p proteins were assayed for binding to U4 snRNA and U3 snoRNA. Both temperature-sensitive mutants displayed significantly reduced RNA binding compared to wild-type protein. As the temperature-sensitive mutations are not in the known RNA binding region of Snu13p this indicates that these mutants indirectly influence the RNA binding properties of Snu13p. This work provides insight into Snu13p function during ribonucleoprotein assembly.     

A branched-chain organic acid linked to multiple sclerosis: first identification by NMR spectroscopy of CSF

(1)H NMR spectroscopy of cerebrospinal fluid (CSF) is currently being used to study metabolic profiles characteristic of distinct multiple sclerosis (MS) manifestations. For select MS patient groups, we have previously detected significantly increased concentrations of several identified metabolites and one unidentified compound. We now present, for the first time, the identification of the latter molecule, beta-hydroxyisobutyrate (BHIB). A combination of dedicated 1D and 2D (1)H NMR experiments was employed for signal assignment. To our knowledge, BHIB has not previously been identified in (1)H NMR spectra of biofluids or biological tissues. Our assignment suggests new biochemical pathways involved in specific MS pathologies.     

Just-identified versus overidentified two-level hierarchical linear models with missing data

The development of model-based methods for incomplete data has been a seminal contribution to statistical practice. Under the assumption of ignorable missingness, one estimates the joint distribution of the complete data for thetainTheta from the incomplete or observed data y(obs). Many interesting models involve one-to-one transformations of theta. For example, with y(i) approximately N(mu, Sigma) for i= 1, ... , n and theta= (mu, Sigma), an ordinary least squares (OLS) regression model is a one-to-one transformation of theta. Inferences based on such a transformation are equivalent to inferences based on OLS using data multiply imputed from f(y(mis) | y(obs), theta) for missing y(mis). Thus, identification of theta from y(obs) is equivalent to identification of the regression model. In this article, we consider a model for two-level data with continuous outcomes where the observations within each cluster are dependent. The parameters of the hierarchical linear model (HLM) of interest, however, lie in a subspace of Theta in general. This identification of the joint distribution overidentifies the HLM. We show how to characterize the joint distribution so that its parameters are a one-to-one transformation of the parameters of the HLM. This leads to efficient estimation of the HLM from incomplete data using either the transformation method or the method of multiple imputation. The approach allows outcomes and covariates to be missing at either of the two levels, and the HLM of interest can involve the regression of any subset of variables on a disjoint subset of variables conceived as covariates.     

Configural Frequency Analysis (CFA) Revisited — a New Look at an Old Approach

Configural frequency analysis (CFA) is a widely used method for the identification of types and syndromes in contingency tables. However, the type model of CFA shows some major deficiencies. In this paper, we propose an alternative modeling of types eliminating the shortcomings of CFA. Basically, a type is modeled as a combination of traits or symptoms that deviates from the pattern of association holding true for the complementary configurations of the contingency table. The new approach is formulated in terms of a log-linear model. It is shown that parameter estimation can be performed with methods known from the analysis of incomplete contingency tables. Test procedures for confirmatory analysis and methods for exploratory search for type configurations are developed. We illustrate the methodology with two practical examples.     

A Unified Approach to Simultaneous Rank Test Procedures in the Unbalanced One‐way Layout

A general nonparametric approach to asymptotic multiple test procedures is proposed which is based on relative effects and which includes continuous as well as discontinuous distributions. The results can be applied to all relevant multiple testing problems in the one‐way layout and include the well known Steel tests as special cases. Moreover, a general estimator for the asymptotic covariance matrix is considered that is consistent even under alternative. This estimator is used to derive simultaneous confidence intervals for the relative effects as well as a test procedure for the multiple nonparametric Behrens‐Fisher problem.     

Multiple Testing for Identifying Effective and Safe Treatments

In the literature various multiple test procedures to compare k treatments with a control have been investigated. They can be applied to establish either treatment efficacy or treatment safety. In this paper we propose procedures which control the multiple level α with respect to efficacy and safety simultaneously. On the one hand we consider a method with stagewise rejective adjustments of local levels applied to appropriately defined subfamilies of null hypotheses. When order restrictions are assumed to hold among the parameters of interest we can alternatively split the multiple level between the families of efficacy and safety null hypotheses. If either all treatments are declared to be safe or all are declared to be effective then the other family can be tested at the full multiple level α, respectively. The methods are compared in a simulation study.     

Sample Sizes for Comparisons of k Treatments with a Control Based on Different Definitions of the Power

The determination of sample sizes for the comparison of k treatments against a control by means of the test of Dunnett (1955, 1964) as well as by means of the multiple t-test will be considered. The power in multiple comparisons can be defined in different ways, see Hochberg and Tamhane (1987). We will derive formulas for the per-pair power, the any-pair power and the all-pairs power for both one- and two-sided comparisons. Tables will be provided that allow sample sizes to be determined for preassigned values of the power.