Assessing agreement with multiple raters on correlated kappa statistics.

In clinical studies, it is often of interest to see the diagnostic agreement among clinicians on certain symptoms. Previous work has focused on the agreement between two clinicians under two different conditions or the agreement among multiple clinicians under one condition. Few have discussed the agreement study with a design where multiple clinicians examine the same group of patients under two different conditions.

NON-HOMOGENEOUS POISSON PROCESS MODEL FOR GENETIC CROSSOVER INTERFERENCE.

The genetic crossover interference is usually modeled with a stationary renewal process to construct the genetic map. We propose two non-homogeneous, also dependent, Poisson process models applied to the known physical map. The crossover process is assumed to start from an origin and to occur sequentially along the chromosome.

A clustering method to identify who benefits most from the treatment group in clinical trials.

In randomized controlled trials (RCTs), the most compelling need is to determine whether the treatment condition was more effective than control. However, it is generally recognized that not all participants in the treatment group of most clinical trials benefit equally. While subgroup analyses are often used to compare treatment effectiveness across pre-determined subgroups categorized by patient characteristics, methods to empirically identify naturally occurring clusters of persons who benefit most from the treatment group have rarely been implemented.

Robust Analysis of High Throughput Screening (HTS) Assay Data.

Quantitative high throughput screening (qHTS) assays use cells or tissues to screen thousands of compounds in a short period of time. Data generated from qHTS assays are then evaluated using nonlinear regression models, such as the Hill model, and decisions regarding toxicity are made using the estimates of the parameters of the model. For any given compound, the variability in the observed response may either be constant across dose groups (homoscedasticity) or vary with dose (heteroscedasticity).

A HIERARCHICAL FUNCTIONAL DATA ANALYTIC APPROACH FOR ANALYZING PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELS.

Ordinary differential equation (ODE) based models find application in a wide variety of biological and physiological phenomena. For instance, they arise in the description of gene regulatory networks, study of viral dynamics and other infectious diseases, etc. In the field of toxicology, they are used in physiologically based pharmacokinetic (PBPK) models for describing absorption, distribution, metabolism and excretion (ADME) of a chemical in-vivo.

Robust nonlinear regression in applications.

Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose groups.

Accounting for Uncertainty in Heteroscedasticity in Nonlinear Regression.

Toxicologists and pharmacologists often describe toxicity of a chemical using parameters of a nonlinear regression model. Thus estimation of parameters of a nonlinear regression model is an important problem. The estimates of the parameters and their uncertainty estimates depend upon the underlying error variance structure in the model.

Multiple Hypothesis Testing for Experimental Gingivitis Based on Wilcoxon Signed Rank Statistics.

Dental research often involves repeated multivariate outcomes on a small number of subjects for which there is interest in identifying outcomes that exhibit change in their levels over time as well as to characterize the nature of that change. In particular, periodontal research often involves the analysis of molecular mediators of inflammation for which multivariate parametric methods are highly sensitive to outliers and deviations from Gaussian assumptions. In such settings, nonparametric methods may be favored over parametric ones.

A linear exponent AR(1) family of correlation structures.

In repeated measures settings, modeling the correlation pattern of the data can be immensely important for proper analyses. Accurate inference requires proper choice of the correlation model. Optimal efficiency of the estimation procedure demands a parsimonious parameterization of the correlation structure, with sufficient sensitivity to detect the range of correlation patterns that may occur.

A Robust QTL Mapping Procedure.

In quantitative-trait linkage studies using experimental crosses, the conventional normal location-shift model or other parameterizations may be unnecessarily restrictive. We generalize the mapping problem to a genuine nonparametric setup and provide a robust estimation procedure for the situation where the underlying phenotype distributions are completely unspecified. Classical Wilcoxon-Mann-Whitney statistics are employed for point and interval estimation of QTL positions and effects.

Evaluation of cluster-randomized trials on maternal and child health research in developing countries.

Objective: To summarize and evaluate all publications including cluster-randomized trials used for maternal and child health research in developing countries during the last 10 years.

Methods: All cluster-randomized trials published between 1998 and 2008 were reviewed, and those that met our criteria for inclusion were evaluated further. The criteria for inclusion were that the trial should have been conducted in maternal and child health care in a developing country and that the conclusions should have been made on an individual level.

A semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes.

We discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models. We generalize an earlier work, considering the sojourn times in health states are not identically distributed, for a given vector of covariates. Approaches based on semiparametric and parametric (exponential and Weibull distributions) methodologies are considered.

Association of leukocyte count with progression of aortic atheroma in stroke/transient ischemic attack patients.

Background And Purpose: Leukocyte count is an independent predictor of stroke. We investigated the association between leukocyte count and progression of aortic atheroma over 12 months in stroke/transient ischemic attack (TIA) patients.

Methods: Consecutive ischemic stroke and transient ischemic attack patients underwent 12-month sequential transesophageal echocardiography and were assessed for total and differential leukocyte counts on admission.

Exterior exposure estimation using a one-compartment toxicokinetic model with blood sample measurements.

Exposure assessment of individuals exposed to certain chemicals plays an important role in the analysis of occupational-as well as environmental-health problems. Biological monitoring, as an alternative to direct environmental measurements, may be applied to relate the exterior exposure with the amount of individual intake. In this paper, we estimate individuals' (inhalation) exposure retrospectively from their blood concentrations via a simplified one-compartment toxicokinetic model.

Aortic arch atheroma progression and recurrent vascular events in patients with stroke or transient ischemic attack.

Background: It is not known whether progression of aortic arch (AA) atheroma is associated with vascular events in patients with stroke or transient ischemic attack (TIA).

Methods And Results: AA atheroma was detected on baseline transesophageal echocardiogram in 167 consecutive patients who had prevalent stroke or TIA. Of these, 125 consented to a follow-up transesophageal echocardiogram at 12 months.

A copula approach for detecting prognostic genes associated with survival outcome in microarray studies.

A challenging and crucial issue in clinical studies in cancer involving gene microarray experiments is the discovery, among a large number of genes, of a relatively small panel of genes whose elements are associated with a relevant clinical outcome variable such as time-to-death or time-to-recurrence of disease. A semiparametric approach, using dependence functions known as copulas, is considered to quantify and estimate the pairwise association between the outcome and each gene expression. These time-to-event type endpoints are typically subject to censoring as not all events are realized at the time of the analysis.

Making inferences about projected completors in longitudinal studies.

In this article, we present methodology for making inferences about projected completors in the presence of attrition. The approach is motivated by a clinical trial that investigates a treatment for disability among individuals who sustain severe head injuries. Although most studies attempt to make inferences about the entire study population, our application poses important scientific questions targeting individuals who are likely to complete the study or to remain on protocol for a specified time period.

Estimating correlation by using a general linear mixed model: evaluation of the relationship between the concentration of HIV-1 RNA in blood and semen.

Estimating the correlation coefficient between two outcome variables is one of the most important aspects of epidemiological and clinical research. A simple Pearson's correlation coefficient method is usually employed when there are complete independent data points for both outcome variables. However, researchers often deal with correlated observations in a longitudinal setting with missing values where a simple Pearson's correlation coefficient method cannot be used.