Is herpes zoster a marker for occult or subsequent malignancy?

BACKGROUND: It has been suggested that herpes zoster may be a marker for occult malignancy. AIM: To examine the emergence of a subsequent cancer diagnosis in patients with and without herpes zoster. DESIGN OF STUDY: Retrospective cohort study. SETTING: Results were based on the database of Intego, an ongoing Belgian general practice-based morbidity registry, covering 37 general practitioners and including about 311 000 patient years between the years 1994 and 2000. METHOD: Survival analysis comparing the emergence of malignancy in patients with and without herpes zoster. RESULTS: The number of patients below the age of 65 years with herpes zoster, cancer or both was too low to draw any sensible conclusions. Above the age of 65 years we identified a significant increase of cancer emergence in the whole group and in females (hazard ratio = 2.65, 95% confidence interval = 1.43 to 4.90), but not in males. No difference could be identified in the first year after the herpes zoster infection. CONCLUSION: Our results do not justify extensive testing for cancer in herpes zoster patients. The association we identified, however, leaves open a number of questions with respect to the physiopathology behind it.     

Investigating the criterion validity of psychiatric symptom scales using surrogate marker validation methodology

This work investigates whether techniques that are generally used for the validation of surrogate markers in clinical trials can be applied in the validation of psychiatric health measurements (often scales) and more generally to investigate relationships between treatment effects on different measurements. However, the categorical nature of some scales makes these techniques inapplicable in the way they were originally defined. In this work, we show a possible extension of this methodology to the setting in which one of the scales is an ordinal categorical variable. When psychiatric health measurements are either developed or used in a new population, reliability and validity must be investigated. Reliability, more specifically internal consistency, test-retest reliability, and inter-rater reliability, is focused on the reproducibility of the measurement. Validity is defined as the degree to which the scale measures what it purports to measure. This can be performed through the analysis of content, construct, and criterion validity. We argue that recent methodology, in particular developed to study surrogate endpoints, can be used to examine criterion validity, concurrent validity, and predictive validity. In concurrent validity, we correlate the measurement with a criterion measure, both of which are given at the same time. In predictive validity, the criterion will not be available to some point in time in the future. The surrogate methods were applied on pooled data from five trials in schizophrenia.     

Classification of sporadic Creutzfeldt-Jakob disease based on clinical and neuropathological characteristics

Creutzfeldt-Jakob disease (CJD) is a rare and fatal neurodegenerative disease of unknown cause. Patients are usually aged between 50 and 75 and typical clinical features include rapidly progressive dementia associated with myoclonus and a characteristic electroencephalographic pattern. Neuropathological examination reveals cortical spongiform change, hence the term 'spongiform encephalopathy'. Several statistical techniques were applied to classify patients with sporadic CJD (sCJD), based on clinical and neuropathological investigation. We focus on the classification of neuropathologically confirmed sCJD patients. In order to obtain a classification rule that correctly classifies this type of patients and at the same time controls the overall error rate, we apply several classification techniques, which in general, produce comparable results. The boosting method produces the best results and the variable 14-3-3 protein in cerebrospinal fluid plays the most important role in the prediction of neuropathologically confirmed sCJD.     

On the use of historical control data in pre-clinical safety studies

A number of methods to formally incorporate historical control information in pre-clinical safety evaluation studies have been proposed in literature. However, it remains unclear when one should use historical data. Focusing on the logistic-normal model, we investigate situations where historical studies may prove to be useful. Aspects of estimation (precision and bias) and testing (power) for treatment effect are investigated under different conditions such as the number of historical control studies, the degree of homogeneity amongst them, the level of treatment effect and different control rates. The possibility to use a selected subset of historical control studies is also explored.     

Correction for Model Selection Bias Using a Modified Model Averaging Approach for Supervised Learning Methods Applied to EEG Experiments

This paper proposes a modified model averaging approach for linear discriminant analysis. This approach is used in combination with a doubly hierarchical supervised learning analysis and applied to preclinical pharmaco-electroencephalographical data for classification of psychotropic drugs. Classification of a test dataset was highly improved with this method.     

Application of Semiparametric Mixed Models and Simultaneous Confidence Bands in a Cardiovascular Safety Experiment with Longitudinal Data

Several pharmacological studies involve experiments aimed at testing for a difference between experimental groups wherein the data are longitudinal in nature, frequently with long sequences per subject. Oftentimes, treatment effect, if present, is not constant over time. In such situations, imposing a parametric mean structure can be too complicated and/or restrictive. A more flexible approach is to model the mean using a semiparametric smooth function, estimated using, for example, penalized smoothing splines. We formulate a series of models exhibiting how the group-specific mean profiles could possibly differ. Once an appropriate model is chosen, interest lies in identifying specific time points where the groups differ. For this purpose, we propose the use of simultaneous confidence bands around the fitted models wherein the bands take into account within and between-subject variability, as well as variability arising from smoothing.     

Generalizability in nongaussian longitudinal clinical trial data based on generalized linear mixed models

This work investigates how generalizability, an extension of reliability, can be defined and estimated based on longitudinal data sequences resulting from, for example, clinical studies. Useful and intuitive approximate expressions are derived based on generalized linear mixed models. Data from four double-blind, randomized clinical trials into schizophrenia motivate the research and are used to estimate generalizability for a binary response parameter.