Graphical models with Koehler Symanowski distributions

In this paper, a multivariate distribution family introduced by Koehler and Symanowski (1995) is discussed as alternative assumption for graphical models which are typically connected with Conditional-Gaussian distributions. For that purpose, certain requirements which have to be fulfilled when formulating graphical models are checked. This leads to the introduction of graphical models with Koehler Symanowski distributions which are then investigated regarding some basic properties known for Gaussian graphical models

Decomposition of ML Estimation in Graphical Models with Koehler Symanowski distributions

In the framework of graphical models the graphical representation of the association structure is used in manifold respects. One is the conclusion from a decomposition of the graph to a possible decomposition of the ML estimation. Results are well-known under the assumption of the Conditional Gaussian distribution. Here, graphical models with a family of distributions are considered which is introduced by Koehler and Symanowski (1995). This approach extends the existing theory of graphical models in two respects. The family of distributions we discuss forms an alternative to the usually applied multivariate normal distribution. Furthermore, the focus lies on covariance graphs rather than on concentration graphs. For these models the decomposability of ML estimation is examined

Factorization of the Cumulative Distribution Function in Case of Conditional Independence. (REVISED, November 1999)

A decomposition of complex estimation problems is often obtained by using factorization formulas for the underlying likelihood or density function. This is, for instance, the case in so-called decomposable graphical models where under the restrictions of conditional independences induced by the graph the estimation in the original model may be decomposed into estimation problems corresponding to subgraphs. Such a decomposition is based on the property of conditional independence which can be read off the graph and on the factorization of the assumed underlying density function. In this paper analogous factorization formulas for the cumulative distribution function are introduced which can be useful in situations where the density is not tractable

Some properties of the family of Koehler Symanowski distributions

In this paper, a class of multivariate distributions introduced by Koehler and Symanowski (1995) is discussed with regard to whether it can be reasonably applied in the framework of graphical modeling. Therefore, the focus lies on properties like marginal and conditional independence, marginalization and the flexibility as far as the modeling of a dependence structure is concerned

A graphical chain model derived from a model selection strategy for the sociologists graduates study

This paper objects to the arising problems due to fitting graphical chain models to multidimensional data sets. This multivariate statistical tool is used to cope with complex research questions concerning not only direct, but also indirect associations between the variables of interest. Due to this high complexity sensible strategies for fitting such models are required. Here, a data--driven selection strategy is discussed. Its application is illustrated for an empirical data example in detail

Factorization of the Cumulative Distribution Function in Case of Conditional Independence. (REVISED, November 1999)

A decomposition of complex estimation problems is often obtained by using factorization formulas for the underlying likelihood or density function. This is, for instance, the case in so-called decomposable graphical models where under the restrictions of conditional independences induced by the graph the estimation in the original model may be decomposed into estimation problems corresponding to subgraphs. Such a decomposition is based on the property of conditional independence which can be read off the graph and on the factorization of the assumed underlying density function. In this paper analogous factorization formulas for the cumulative distribution function are introduced which can be useful in situations where the density is not tractable

The professional career of sociologists: a graphical chain model reflecting early influences and associations

Currently, German universities take great interest in how their students perform in later professional life. Information on early determinants of success is needed in order to adjust educational programs and to cope more easily with the expected increase in the number of students in the coming years. In this paper we analyze data on the occupational careers of sociologists. The complexity of the underlying research question is taken into account by modelling the associations using so--called graphical chain models. These models are in general constructed such that conditional independencies can be concluded from the corresponding graph. We present the different steps in formulating the dependence structure. For checking its appropriateness, a model selection strategy is applied based on regression techniques. The final graph gives essential hints with respect to early determinants for professional success

Long-term treatment with active Aβ immunotherapy with CAD106 in mild Alzheimer’s disease

Introduction: CAD106 is designed to stimulate amyloid-β (Aβ)-specific antibody responses while avoiding T-cell autoimmune responses. The CAD106 first-in-human study demonstrated a favorable safety profile and promising antibody response. We investigated long-term safety, tolerability and antibody response after repeated CAD106 injections. Methods: Two phase IIa, 52-week, multicenter, randomized, double-blind, placebo-controlled core studies (2201; 2202) and two 66-week open-label extension studies (2201E; 2202E) were conducted in patients with mild Alzheimer’s disease (AD) aged 40 to 85 years. Patients were randomized to receive 150μg CAD106 or placebo given as three subcutaneous (2201) or subcutaneous/intramuscular (2202) injections, followed by four injections (150 μg CAD106; subcutaneous, 2201E1; intramuscular, 2202E1). Our primary objective was to evaluate the safety and tolerability of repeated injections, including monitoring cerebral magnetic resonance imaging scans, adverse events (AEs) and serious AEs (SAEs). Further objectives were to assess Aβ-specific antibody response in serum and Aβ-specific T-cell response (core only). Comparable Aβ-immunoglobulin G (IgG) exposure across studies supported pooled immune response assessments. Results: Fifty-eight patients were randomized (CAD106, n = 47; placebo, n = 11). Baseline demographics and characteristics were balanced. Forty-five patients entered extension studies. AEs occurred in 74.5% of CAD106-treated patients versus 63.6% of placebo-treated patients (core), and 82.2% experienced AEs during extension studies. Most AEs were mild to moderate in severity, were not study medication-related and did not require discontinuation. SAEs occurred in 19.1% of CAD106-treated patients and 36.4% of placebo-treated patients (core). One patient (CAD106-treated; 2201) reported a possibly study drug-related SAE of intracerebral hemorrhage. Four patients met criteria for amyloid-related imaging abnormalities (ARIA) corresponding to microhemorrhages: one was CAD106-treated (2201), one placebo-treated (2202) and two open-label CAD106-treated. No ARIA corresponded to vasogenic edema. Two patients discontinued extension studies because of SAEs (rectal neoplasm and rapid AD progression, respectively). Thirty CAD106-treated patients (63.8%) were serological responders. Sustained Aβ-IgG titers and prolonged time to decline were observed in extensions versus core studies. Neither Aβ1–6 nor Aβ1–42 induced specific T-cell responses; however, positive control responses were consistently detected with the CAD106 carrier. Conclusions: No unexpected safety findings or Aβ-specific T-cell responses support the CAD106 favorable tolerability profile. Long-term treatment-induced Aβ-specific antibody titers and prolonged time to decline indicate antibody exposure may increase with additional injections. CAD106 may be a valuable therapeutic option in AD

Randomized, double-blind, parallel-group, 48-week study for efficacy and safety of a higher-dose rivastigmine patch (15 vs. 10 cm(2)) in Alzheimer’s disease

Aim: Determine whether patients with Alzheimer’s disease demonstrating functional and cognitive decline, following 24-48 weeks of open-label treatment with 9.5 mg/24 h (10 cm(2)) rivastigmine patch, benefit from a dose increase in a double-blind (DB) comparative trial of two patch doses. Methods: Patients meeting prespecified decline criteria were randomized to receive 9.5 or 13.3 mg/24 h (15 cm(2)) patch during a 48-week, DB phase. Coprimary outcomes were change from baseline to week 48 on the Instrumental Activities of Daily Living domain of the Alzheimer’s Disease Cooperative Study-Activities of Daily Living (ADCS-IADL) scale and the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog). Safety and tolerability were assessed. Results: Of 1,584 patients enrolled, 567 met decline criteria and were randomized. At all timepoints, ADCS-IADL and ADAS-cog scores favoured the 13.3 mg/24 h patch. The 13.3 mg/24 h patch was statistically superior to the 9.5 mg/24 h patch on the ADCS-IADL scale from week 16 (p = 0.025) onwards including week 48 (p = 0.002), and ADAScog at week 24 (p = 0.027), but not at week 48 (p = 0.227). No unexpected safety concerns were observed. Conclusions: The 13.3 mg/24 h rivastigmine patch significantly reduced deterioration in IADL, compared with the 9.5 mg/24 h patch, and was well tolerated. Copyright © 2012 S. Karger AG, Base