Detecting, quantifying and adjusting for publication bias in meta-analyses: protocol of a systematic review on methods.

BACKGROUND: Health professionals and policymakers aspire to make healthcare decisions based on the entire relevant research evidence. This, however, can rarely be achieved because a considerable amount of research findings are not published, especially in case of 'negative' results - a phenomenon widely recognized as publication bias. Different methods of detecting, quantifying and adjusting for publication bias in meta-analyses have been described in the literature, such as graphical approaches and formal statistical tests to detect publication bias, and statistical approaches to modify effect sizes to adjust a pooled estimate when the presence of publication bias is suspected. An up-to-date systematic review of the existing methods is lacking. METHODS/DESIGN: The objectives of this systematic review are as follows:âeuro¢ To systematically review methodological articles which focus on non-publication of studies and to describe methods of detecting and/or quantifying and/or adjusting for publication bias in meta-analyses.âeuro¢ To appraise strengths and weaknesses of methods, the resources they require, and the conditions under which the method could be used, based on findings of included studies.We will systematically search Web of Science, Medline, and the Cochrane Library for methodological articles that describe at least one method of detecting and/or quantifying and/or adjusting for publication bias in meta-analyses. A dedicated data extraction form is developed and pilot-tested. Working in teams of two, we will independently extract relevant information from each eligible article. As this will be a qualitative systematic review, data reporting will involve a descriptive summary. DISCUSSION: Results are expected to be publicly available in mid 2013. This systematic review together with the results of other systematic reviews of the OPEN project (To Overcome Failure to Publish Negative Findings) will serve as a basis for the development of future policies and guidelines regarding the assessment and handling of publication bias in meta-analyses.

Defining publication bias: protocol for a systematic review of highly cited articles and proposal for a new framework.

BACKGROUND: Selective publication of studies, which is commonly called publication bias, is widely recognized. Over the years a new nomenclature for other types of bias related to non-publication or distortion related to the dissemination of research findings has been developed. However, several of these different biases are often still summarized by the term 'publication bias'. METHODS/DESIGN: As part of the OPEN Project (To Overcome failure to Publish nEgative fiNdings) we will conduct a systematic review with the following objectives:- To systematically review highly cited articles that focus on non-publication of studies and to present the various definitions of biases related to the dissemination of research findings contained in the articles identified.- To develop and discuss a new framework on nomenclature of various aspects of distortion in the dissemination process that leads to public availability of research findings in an international group of experts in the context of the OPEN Project.We will systematically search Web of Knowledge for highly cited articles that provide a definition of biases related to the dissemination of research findings. A specifically designed data extraction form will be developed and pilot-tested. Working in teams of two, we will independently extract relevant information from each eligible article.For the development of a new framework we will construct an initial table listing different levels and different hazards en route to making research findings public. An international group of experts will iteratively review the table and reflect on its content until no new insights emerge and consensus has been reached. DISCUSSION: Results are expected to be publicly available in mid-2013. This systematic review together with the results of other systematic reviews of the OPEN project will serve as a basis for the development of future policies and guidelines regarding the assessment and prevention of publication bias.

Need for informed consent in substance use studies--harm of bias?

OBJECTIVE: The aim of this study was to examine the differences between those who gave informed consent to a study on substance use and those who did not, and to analyze whether differences changed with varying nonconsent rates. METHOD: Cross-sectional questionnaire data on demographics, alcohol, smoking, and cannabis use were obtained for 6,099 French- and 5,720 German-speaking 20-year-old Swiss men. Enrollment took place over 11 months for the Cohort Study on Substance Use Risk Factors (C-SURF). Consenters and nonconsenters were asked to complete a short questionnaire. Data for nearly the entire population were available because 94% responded. Weekly differences in consent rates were analyzed. Regressions examined the associations of substance use with consent giving and consent rates and the interaction between the two. RESULTS: Nonconsenters had higher substance use patterns, although they were more often alcohol abstainers; differences were small and not always significant and did not decrease as consent rates increased. CONCLUSIONS: Substance use currently is a minor sensitive topic among young men, resulting in small differences between nonconsenters and consenters. As consent rates increase, additional individuals are similar to those observed at lower consent rates. Estimates of analytical studies looking at associations of substance use with other variables will not differ at reasonable consent rates of 50%-80%. Descriptive prevalence studies may be biased, but only at very low rates of consent.

Correcting for the bias due to expression specificity improves the estimation of constrained evolution of expression between mouse and human.

MOTIVATION: Comparative analyses of gene expression data from different species have become an important component of the study of molecular evolution. Thus methods are needed to estimate evolutionary distances between expression profiles, as well as a neutral reference to estimate selective pressure. Divergence between expression profiles of homologous genes is often calculated with Pearson's or Euclidean distance. Neutral divergence is usually inferred from randomized data. Despite being widely used, neither of these two steps has been well studied. Here, we analyze these methods formally and on real data, highlight their limitations and propose improvements. RESULTS: It has been demonstrated that Pearson's distance, in contrast to Euclidean distance, leads to underestimation of the expression similarity between homologous genes with a conserved uniform pattern of expression. Here, we first extend this study to genes with conserved, but specific pattern of expression. Surprisingly, we find that both Pearson's and Euclidean distances used as a measure of expression similarity between genes depend on the expression specificity of those genes. We also show that the Euclidean distance depends strongly on data normalization. Next, we show that the randomization procedure that is widely used to estimate the rate of neutral evolution is biased when broadly expressed genes are abundant in the data. To overcome this problem, we propose a novel randomization procedure that is unbiased with respect to expression profiles present in the datasets. Applying our method to the mouse and human gene expression data suggests significant gene expression conservation between these species. CONTACT: marc.robinson-rechavi@unil.ch; sven.bergmann@unil.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.