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Biometrical Journal. Biometrische Zeitschrift

Kerrie P Nelson, Aya A Mitani, Don Edwards
Large-scale agreement studies are becoming increasingly common in medical settings to gain better insight into discrepancies often observed between experts' classifications. Ordered categorical scales are routinely used to classify subjects' disease and health conditions. Summary measures such as Cohen's weighted kappa are popular approaches for reporting levels of association for pairs of raters' ordinal classifications. However, in large-scale studies with many raters, assessing levels of association can be challenging due to dependencies between many raters each grading the same sample of subjects' results and the ordinal nature of the ratings...
January 19, 2018: Biometrical Journal. Biometrische Zeitschrift
Hukum Chandra, Sushil Kumar, Kaustav Aditya
Binary data are often of interest in many small areas of applications. The use of standard small area estimation methods based on linear mixed models becomes problematic for such data. An empirical plug-in predictor (EPP) under a unit-level generalized linear mixed model with logit link function is often used for the estimation of a small area proportion. However, this EPP requires the availability of unit-level population information for auxiliary data that may not be always accessible. As a consequence, in many practical situations, this EPP approach cannot be applied...
January 19, 2018: Biometrical Journal. Biometrische Zeitschrift
Mehran Aflakparast, Mathisca C M de Gunst, Wessel N van Wieringen
Cross-sectional studies may shed light on the evolution of a disease like cancer through the comparison of patient traits among disease stages. This problem is especially challenging when a gene-gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage-wise mixtures of Gaussian graphical models (GGMs) from high-dimensional data. These mixtures are fitted by a (fused) ridge penalized EM algorithm...
January 10, 2018: Biometrical Journal. Biometrische Zeitschrift
Georg Heinze, Christine Wallisch, Daniela Dunkler
Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well-established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10-30...
January 2, 2018: Biometrical Journal. Biometrische Zeitschrift
Nathalie Grafféo, Aurélien Latouche, Ronald B Geskus, Sylvie Chevret
For estimating the causal effect of treatment exposure on the occurrence of adverse events, inverse probability weights (IPW) can be used in marginal structural models to correct for time-dependent confounding. The R package ipw allows IPW estimation by modeling the relationship between the exposure and confounders via several regression models, among which is the Cox model. For right-censored data and time-dependent exposures such as treatment switches, the ipw package allows a single switch, assuming that patients are treated once and for all...
December 27, 2017: Biometrical Journal. Biometrische Zeitschrift
Dietrich Stoyan, Arne Pommerening, Manuela Hummel, Annette Kopp-Schneider
Interrater agreement on binary measurements with more than two raters is often assessed using Fleiss' κ, which is known to be difficult to interpret. In situations where the same raters rate all items, however, the far less known κ suggested by Conger, Hubert, and Schouten is more appropriate. We try to support the interpretation of these characteristics by investigating various models or scenarios of rating. Our analysis, which is restricted to binary data, shows that conclusions concerning interrater agreement by κ heavily depend on the population of items or subjects considered, even if the raters have identical behavior...
December 27, 2017: Biometrical Journal. Biometrische Zeitschrift
Trias Wahyuni Rakhmawati, Geert Molenberghs, Geert Verbeke, Christel Faes
The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole...
December 19, 2017: Biometrical Journal. Biometrische Zeitschrift
Catherine Mercier, Amna Klich, Caroline Truntzer, Vincent Picaud, Jean-François Giovannelli, Patrick Ducoroy, Pierre Grangeat, Delphine Maucort-Boulch, Pascal Roy
Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algorithm and two more recent ones (BHI-PRO 1 and 2)...
December 12, 2017: Biometrical Journal. Biometrische Zeitschrift
Ao Zhang, Suyan Tian
Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones...
December 5, 2017: Biometrical Journal. Biometrische Zeitschrift
Marcelo A da Silva, Eduardo S B de Oliveira, Alina A von Davier, Jorge L Bazán
The deterministic inputs, noisy, "and" gate (DINA) model is a popular cognitive diagnosis model (CDM) in psychology and psychometrics used to identify test takers' profiles with respect to a set of latent attributes or skills. In this work, we propose an estimation method for the DINA model with the No-U-Turn Sampler (NUTS) algorithm, an extension to Hamiltonian Monte Carlo (HMC) method. We conduct a simulation study in order to evaluate the parameter recovery and efficiency of this new Markov chain Monte Carlo method and to compare it with two other Bayesian methods, the Metropolis Hastings and Gibbs sampling algorithms, and with a frequentist method, using the Expectation-Maximization (EM) algorithm...
December 1, 2017: Biometrical Journal. Biometrische Zeitschrift
Anne-Laure Boulesteix, Harald Binder, Michal Abrahamowicz, Willi Sauerbrei
No abstract text is available yet for this article.
November 29, 2017: Biometrical Journal. Biometrische Zeitschrift
Anna K Rieger, Ulrich R Mansmann
Colorectal cancer screening is well established. The identification of high risk populations is the key to implement effective risk-adjusted screening. Good statistical approaches for risk prediction do not exist. The family's colorectal cancer history is used for identification of high risk families and usually assessed by a questionnaire. This paper introduces a prediction algorithm to designate a family for colorectal cancer risk and discusses its statistical properties. The new algorithm uses Bayesian reasoning and a detailed family history illustrated by a pedigree and a Lexis diagram...
November 8, 2017: Biometrical Journal. Biometrische Zeitschrift
Yuanyuan Tang, Michelle Xia, Liangrui Sun, John A Spertus, Philip G Jones
The risk difference is an intelligible measure for comparing disease incidence in two exposure or treatment groups. Despite its convenience in interpretation, it is less prevalent in epidemiological and clinical areas where regression models are required in order to adjust for confounding. One major barrier to its popularity is that standard linear binomial or Poisson regression models can provide estimated probabilities out of the range of (0,1), resulting in possible convergence issues. For estimating adjusted risk differences, we propose a general framework covering various constraint approaches based on binomial and Poisson regression models...
November 7, 2017: Biometrical Journal. Biometrische Zeitschrift
Luca Grassano, Guido Pagana, Marco Daperno, Enrico Bibbona, Mauro Gasparini
In clinical research and in more general classification problems, a frequent concern is the reliability of a rating system. In the absence of a gold standard, agreement may be considered as an indication of reliability. When dealing with categorical data, the well-known kappa statistic is often used to measure agreement. The aim of this paper is to obtain a theoretical result about the asymptotic distribution of the kappa statistic with multiple items, multiple raters, multiple conditions, and multiple rating categories (more than two), based on recent work...
November 7, 2017: Biometrical Journal. Biometrische Zeitschrift
Ipek Guler, Christel Faes, Carmen Cadarso-Suárez, Laetitia Teixeira, Anabela Rodrigues, Denisa Mendonça
In many follow-up studies different types of outcomes are collected including longitudinal measurements and time-to-event outcomes. Commonly, it is of interest to study the association between them. Joint modeling approaches of a single longitudinal outcome and survival process have recently gained increasing attention from both frequentist and Bayesian perspective. However, in many studies several longitudinal biomarkers are of interest and instead of selecting one single biomarker, the relationships between all these outcomes and their association with survival needs to be investigated...
November 2017: Biometrical Journal. Biometrische Zeitschrift
Il Do Ha, Maengseok Noh, Youngjo Lee
In longitudinal studies, a subject may have different types of outcomes that could be correlated. For example, a response variable of interest would be measured repeatedly over time on the same subject and at the same time, an event time representing a single event or competing-risks event is also observed. In this paper, we propose a joint modeling framework that accounts for the inherent association between such multiple outcomes via frailties (unobserved random effects). Among outcomes, at least one outcome is an event time that has a type of a single event or competing-risks event...
November 2017: Biometrical Journal. Biometrische Zeitschrift
Carmen Cadarso Suárez, Nadja Klein, Thomas Kneib, Geert Molenberghs, Dimitris Rizopoulos
No abstract text is available yet for this article.
November 2017: Biometrical Journal. Biometrische Zeitschrift
Elisa Duarte, Bruno de Sousa, Carmen Cadarso-Suárez, Nadja Klein, Thomas Kneib, Vítor Rodrigues
Studies addressing breast cancer risk factors have been looking at trends relative to age at menarche and menopause. These studies point to a downward trend of age at menarche and an upward trend for age at menopause, meaning an increase of a woman's reproductive lifespan cycle. In addition to studying the effect of the year of birth on the expectation of age at menarche and a woman's reproductive lifespan, it is important to understand how a woman's cohort affects the correlation between these two variables...
November 2017: Biometrical Journal. Biometrische Zeitschrift
Kevin Kunzmann, Meinhard Kieser
Inference after two-stage single-arm designs with binary endpoint is challenging due to the nonunique ordering of the sampling space in multistage designs. We illustrate the problem of specifying test-compatible confidence intervals for designs with nonconstant second-stage sample size and present two approaches that guarantee confidence intervals consistent with the test decision. Firstly, we extend the well-known Clopper-Pearson approach of inverting a family of two-sided hypothesis tests from the group-sequential case to designs with fully adaptive sample size...
October 27, 2017: Biometrical Journal. Biometrische Zeitschrift
Stephen R Cole, Jessie K Edwards, Daniel Westreich, Catherine R Lesko, Bryan Lau, Michael J Mugavero, W Christopher Mathews, Joseph J Eron, Sander Greenland
Marginal structural models for time-fixed treatments fit using inverse-probability weighted estimating equations are increasingly popular. Nonetheless, the resulting effect estimates are subject to finite-sample bias when data are sparse, as is typical for large-sample procedures. Here we propose a semi-Bayes estimation approach which penalizes or shrinks the estimated model parameters to improve finite-sample performance. This approach uses simple symmetric data-augmentation priors. Limited simulation experiments indicate that the proposed approach reduces finite-sample bias and improves confidence-interval coverage when the true values lie within the central "hill" of the prior distribution...
October 27, 2017: Biometrical Journal. Biometrische Zeitschrift
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