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.

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.

Systematic review of the empirical evidence of study publication bias and outcome reporting bias - an updated review.

BACKGROUND: The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias and outcome reporting bias have been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. METHODOLOGY/PRINCIPAL FINDINGS: In this update, we review and summarise the evidence from cohort studies that have assessed study publication bias or outcome reporting bias in randomised controlled trials. Twenty studies were eligible of which four were newly identified in this update. Only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Fifteen of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40-62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies. CONCLUSIONS: This update does not change the conclusions of the review in which 16 studies were included. Direct empirical evidence for the existence of study publication bias and outcome reporting bias is shown. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.

The contributions of sensory dominance and attentional bias to cross-modal enhancement of visual cortex excitability.

Approaching or looming sounds (L-sounds) have been shown to selectively increase visual cortex excitability [Romei, V., Murray, M. M., Cappe, C., & Thut, G. Preperceptual and stimulus-selective enhancement of low-level human visual cortex excitability by sounds. Current Biology, 19, 1799-1805, 2009]. These cross-modal effects start at an early, preperceptual stage of sound processing and persist with increasing sound duration. Here, we identified individual factors contributing to cross-modal effects on visual cortex excitability and studied the persistence of effects after sound offset. To this end, we probed the impact of different L-sound velocities on phosphene perception postsound as a function of individual auditory versus visual preference/dominance using single-pulse TMS over the occipital pole. We found that the boosting of phosphene perception by L-sounds continued for several tens of milliseconds after the end of the L-sound and was temporally sensitive to different L-sound profiles (velocities). In addition, we found that this depended on an individual's preferred sensory modality (auditory vs. visual) as determined through a divided attention task (attentional preference), but not on their simple threshold detection level per sensory modality. Whereas individuals with "visual preference" showed enhanced phosphene perception irrespective of L-sound velocity, those with "auditory preference" showed differential peaks in phosphene perception whose delays after sound-offset followed the different L-sound velocity profiles. These novel findings suggest that looming signals modulate visual cortex excitability beyond sound duration possibly to support prompt identification and reaction to potentially dangerous approaching objects. The observed interindividual differences favor the idea that unlike early effects this late L-sound impact on visual cortex excitability is influenced by cross-modal attentional mechanisms rather than low-level sensory processes.

Using register data to assess the impact of response enhancement methods on the risk of nonresponse bias and survey costs

The pursuit of high response rates to minimise the threat of nonresponse bias continues to dominate decisions about resource allocation in survey research. Yet a growing body of research has begun to question this practice. In this study, we use previously unavailable data from a new sampling frame based on population registers to assess the value of different methods designed to increase response rates on the European Social Survey in Switzerland. Using sampling data provides information about both respondents and nonrespondents, making it possible to examine how changes in response rates resulting from the use of different fieldwork methods relate to changes in the composition and representativeness of the responding sample. We compute an R-indicator to assess representativity with respect to the sampling register variables, and find little improvement in the sample composition as response rates increase. We then examine the impact of response rate increases on the risk of nonresponse bias based on Maximal Absolute Bias (MAB), and coefficients of variation between subgroup response rates, alongside the associated costs of different types of fieldwork effort. The results show that increases in response rate help to reduce MAB, while only small but important improvements to sample representativity are gained by varying the type of effort. These findings lend further support to research that has called into question the value of extensive investment in procedures aimed at reaching response rate targets and the need for more tailored fieldwork strategies aimed both at reducing survey costs and minimising the risk of bias.

Addressing trypsin bias in large scale (phospho)proteome analysis by size exclusion chromatography and secondary digestion of large post-trypsin peptides.

In the vast majority of bottom-up proteomics studies, protein digestion is performed using only mammalian trypsin. Although it is clearly the best enzyme available, the sole use of trypsin rarely leads to complete sequence coverage, even for abundant proteins. It is commonly assumed that this is because many tryptic peptides are either too short or too long to be identified by RPLC-MS/MS. We show through in silico analysis that 20-30% of the total sequence of three proteomes (Schizosaccharomyces pombe, Saccharomyces cerevisiae, and Homo sapiens) is expected to be covered by Large post-Trypsin Peptides (LpTPs) with M(r) above 3000 Da. We then established size exclusion chromatography to fractionate complex yeast tryptic digests into pools of peptides based on size. We found that secondary digestion of LpTPs followed by LC-MS/MS analysis leads to a significant increase in identified proteins and a 32-50% relative increase in average sequence coverage compared to trypsin digestion alone. Application of the developed strategy to analyze the phosphoproteomes of S. pombe and of a human cell line identified a significant fraction of novel phosphosites. Overall our data indicate that specific targeting of LpTPs can complement standard bottom-up workflows to reveal a largely neglected portion of the proteome.

Radiographic total disc replacement angle measurement accuracy using the Oxford Cobbometer: precision and bias.

Total disc replacement (TDR) clinical success has been reported to be related to the residual motion of the operated level. Thus, accurate measurement of TDR range of motion (ROM) is of utmost importance. One commonly used tool in measuring ROM is the Oxford Cobbometer. Little is known however on its accuracy (precision and bias) in measuring TDR angles. The aim of this study was to assess the ability of the Cobbometer to accurately measure radiographic TDR angles. An anatomically accurate synthetic L4-L5 motion segment was instrumented with a CHARITE artificial disc. The TDR angle and anatomical position between L4 and L5 was fixed to prohibit motion while the motion segment was radiographically imaged in various degrees of rotation and elevation, representing a sample of possible patient placement positions. An experienced observer made ten readings of the TDR angle using the Cobbometer at each different position. The Cobbometer readings were analyzed to determine measurement accuracy at each position. Furthermore, analysis of variance was used to study rotation and elevation of the motion segment as treatment factors. Cobbometer TDR angle measurements were most accurate (highest precision and lowest bias) at the centered position (95.5%), which placed the TDR directly inline with the x-ray beam source without any rotation. In contrast, the lowest accuracy (75.2%) was observed in the most rotated and off-centered view. A difference as high as 4 degrees between readings at any individual position, and as high as 6 degrees between all the positions was observed. Furthermore, the Cobbometer was unable to detect the expected trend in TDR angle projection with changing position. Although the Cobbometer has been reported to be reliable in different clinical applications, it lacks the needed accuracy to measure TDR angles and ROM. More accurate ROM measurement methods need to be developed to help surgeons and researchers assess radiological success of TDRs.

Bewertung des Risikos für Bias in kontrollierten Studien [Assessment of risk of bias in controlled studies].

BACKGROUND: Practicing physicians are faced with many medical decisions daily. These are mainly influenced by personal experience but should also consider patient preferences and the scientific evidence reflected by a constantly increasing number of medical publications and guidelines. With the objective of optimal medical treatment, the concept of evidence-based medicine is founded on these three aspects. It should be considered that there is a high risk of misinterpreting evidence, leading to medical errors and adverse effects without knowledge of the methodological background. OBJECTIVES: This article explains the concept of systematic error (bias) and its importance. Causes and effects as well as methods to minimize bias are discussed. This information should impart a deeper understanding, leading to a better assessment of studies and implementation of its recommendations in daily medical practice. CONCLUSION: Developed by the Cochrane Collaboration, the risk of bias (RoB) tool is an assessment instrument for the potential of bias in controlled trials. Good handling, short processing time, high transparency of judgements and a graphical presentation of findings that is easily comprehensible are among its strengths. Attached to this article the German translation of the RoB tool is published. This should facilitate the applicability for non-experts and moreover, support evidence-based medical decision-making.