On commercial media bias

Within the spokes model of Chen and Riordan (2007) that allowsfor non-localized competition among arbitrary numbers of media outlets, we quantify the effect of concentration of ownership on qualityand bias of media content. A main result shows that too few commercial outlets, or better, too few separate owners of commercial outlets can lead to substantial bias in equilibrium. Increasing the number of outlets (commercial and non-commercial) tends to bring down this bias; but the strongest effect occurs when the number of owners is increased. Allowing for free entry provides lower bounds on fixed costs above which substantial commercial bias occurs in equilibrium.

Intravitreal ranibizumab for age-related macular degeneration : optical coherence tomography guided retreatment intervals and their intra- and inter-individual variability

Purpose: To determine whether the need for retreatment after an initial phase of 3 monthly intravitreal injections of ranibizumab shows an intra-individual regular rhythm and to what degree it varies between different patients. Methods: Prospective study with 42 patients with exudative AMD, treatment naïve. Loading dose of 3 monthly doses of ranibizumab (0,5 mg), followed by a 12 months pro re nata (PRN) regimen according to early exudative signs on HD-OCT Cirrus, Zeiss. The follow-up visits were intensified (week 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, etc after each injection) in order to detect recurrences early, and injection followed within 3 days in cases of subretinal fluid, cysts, or central thickness increase of>50microns. Intervals were calculated between injections for the 12 month follow-up with PRN treatment. Variability was expressed as standard deviation (SD). Results: Visual acuity (VA) improved from a mean ETDRS score of 61.6 (SD 10.8) at baseline to 68.0 (SD 10.2) at month 3 and to 74.7(SD 9.0) at month 12. The 15 patients who have already completed the study showed maintenance of the VA improvement. Central foveal thickness improved from a mean value of 366 microns (baseline) to 253 microns (month 3), well maintained thereafter. Mean number of injections was 8.8 (SD 3.5,range 0-12) per 12 months of follow-up (after 3 doses), with mean individual treatment-recurrence (TR) intervals ranging from 28->365 days (mean 58). Intraindividual variability of TR intervals (SD) was 7.1 days as a mean value (range 1.7¡V22.6). It ranged within 20% of the mean intra-individual interval for 30 (91%) and within 15% for 21 patients (64%). The first interval was within 1 week of the mean intra-individual interval in 64% and within 2 weeks in 89% of patients. Conclusions: The majority of AMD patients showed a relatively stable rhythm for PRN injections of ranibizumab after initial loading phase, associated with excellent functional/anatomical results. The initial interval last loading dose-first recurrence may have a predictive value for further need of treatment, potentially facilitating follow-up and patient care.

Ambiguity seeking as a result of the status quo bias

Several factors affect attitudes toward ambiguity. What happens, however, when peopleare asked to exchange an ambiguous alternative in their possession for an unambiguousone? We present three experiments in which individuals preferred to retain the former.This status quo bias emerged both within- and between-subjects, with and withoutincentives, with different outcome distributions, and with endowments determined byboth the experimenter and the participants themselves. Findings emphasize the need toaccount for the frames of reference under which evaluations of probabilistic informationtake place as well as modifications that should be incorporated into descriptive modelsof decision making.

Examining non-response bias in substance use research-Are late respondents proxies for non-respondents?

BACKGROUND: Non-response is a major concern among substance use epidemiologists. When differences exist between respondents and non-respondents, survey estimates may be biased. Therefore, researchers have developed time-consuming strategies to convert non-respondents to respondents. The present study examines whether late respondents (converted former non-participants) differ from early respondents, non-consenters or silent refusers (consent givers but non-participants) in a cohort study, and whether non-response bias can be reduced by converting former non-respondents. METHODS: 6099 French- and 5720 German-speaking Swiss 20-year-old males (more than 94% of the source population) completed a short questionnaire on substance use outcomes and socio-demographics, independent of any further participation in a cohort study. Early respondents were those participating in the cohort study after standard recruitment procedures. Late respondents were non-respondents that were converted through individual encouraging telephone contact. Early respondents, non-consenters and silent refusers were compared to late respondents using logistic regressions. Relative non-response biases for early respondents only, for respondents only (early and late) and for consenters (respondents and silent refusers) were also computed. RESULTS: Late respondents showed generally higher patterns of substance use than did early respondents, but lower patterns than did non-consenters and silent refusers. Converting initial non-respondents to respondents reduced the non-response bias, which might be further reduced if silent refusers were converted to respondents. CONCLUSION: Efforts to convert refusers are effective in reducing non-response bias. However, converted late respondents cannot be seen as proxies of non-respondents, and are at best only indicative of existing response bias due to persistent non-respondents.

Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

Bias related to body mass index in pediatric echocardiographic z scores.

In pediatric echocardiography, cardiac dimensions are often normalized for weight, height, or body surface area (BSA). The combined influence of height and weight on cardiac size is complex and likely varies with age. We hypothesized that increasing weight for height, as represented by body mass index (BMI) adjusted for age, is poorly accounted for in Z scores normalized for weight, height, or BSA. We aimed to evaluate whether a bias related to BMI was introduced when proximal aorta diameter Z scores are derived from bivariate models (only one normalizing variable), and whether such a bias was reduced when multivariable models are used. We analyzed 1,422 echocardiograms read as normal in children ≤18 years. We computed Z scores of the proximal aorta using allometric, polynomial, and multivariable models with four body size variables. We then assessed the level of residual association of Z scores and BMI adjusted for age and sex. In children ≥6 years, we found a significant residual linear association with BMI-for-age and Z scores for most regression models. Only a multivariable model including weight and height as independent predictors produced a Z score free of linear association with BMI. We concluded that a bias related to BMI was present in Z scores of proximal aorta diameter when normalization was done using bivariate models, regardless of the regression model or the normalizing variable. The use of multivariable models with weight and height as independent predictors should be explored to reduce this potential pitfall when pediatric echocardiography reference values are evaluated.

Pseudomonas fluorescens CHA0 produces enantio-pyochelin, the optical antipode of the Pseudomonas aeruginosa siderophore pyochelin.

The siderophore pyochelin is made by a thiotemplate mechanism from salicylate and two molecules of cysteine. In Pseudomonas aeruginosa, the first cysteine residue is converted to its D-isoform during thiazoline ring formation whereas the second cysteine remains in its L-configuration, thus determining the stereochemistry of the two interconvertible pyochelin diastereoisomers as 4'R, 2''R, 4''R (pyochelin I) and 4'R, 2''S, 4''R (pyochelin II). Pseudomonas fluorescens CHA0 was found to make a different stereoisomeric mixture, which promoted growth under iron limitation in strain CHA0 and induced the expression of its biosynthetic genes, but was not recognized as a siderophore and signaling molecule by P. aeruginosa. Reciprocally, pyochelin promoted growth and induced pyochelin gene expression in P. aeruginosa, but was not functional in P. fluorescens. The structure of the CHA0 siderophore was determined by mass spectrometry, thin-layer chromatography, NMR, polarimetry, and chiral HPLC as enantio-pyochelin, the optical antipode of the P. aeruginosa siderophore pyochelin. Enantio-pyochelin was chemically synthesized and confirmed to be active in CHA0. Its potential biosynthetic pathway in CHA0 is discussed.

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

Measuring risk aversion with lists: A new bias

Various experimental procedures aimed at measuring individual risk aversion involve alist of pairs of alternative prospects. We first study the widely used method by Holt andLaury (2002), for which we find that the removal of some items from the lists yields asystematic decrease in risk aversion. This bias is quite distinct from other confoundsthat have been previously observed in the use of the Holt and Laury method. It may berelated to empirical phenomena and theoretical developments where better prospectsincrease risk aversion. Nevertheless, we have also found that the more recent elicitationmethod due to Abdellaoui et al. (2011), also based on lists, does not display anystatistically significant bias when the corresponding items of the list are removed. Ourresults suggest that methods other than the popular Holt and Laury one may bepreferable for the measurement of risk aversion.