Randomized controlled trials are widely considered the most robust design for evaluating the effects of clinical interventions. While generalisability is often limited, randomization aims to ensure that effects observed are genuine. However, there are common sources of bias, even in well-conducted trials, that pose a threat to this interpretation. The revised Cochrane risk-of-bias tool for trials (RoB 2) distinguishes five domains of bias that can affect the results of trials stemming from (1) the randomization process, (2) deviations from intended interventions, (3) missing outcome data, (4) outcome measurement, and (5) reporting of findings. We use RoB 2 as a framework for recommendations to help researchers mitigate these sources of bias and ensure transparency in reporting so that users of research are aware of them.

Systematic reviews are increasingly prevalent in environmental health due to their ability to synthesize evidence while reducing bias. Different systematic review methods have been developed by the US National Toxicology Program's Office of Health Assessment and Translation (OHAT), the US Environmental Protection Agency's (EPA) Integrated Risk Information System (IRIS), and by the US EPA under the Toxic Substances Control Act (TSCA), including the approach to assess risk of bias (ROB), one of the most vital steps which is used to evaluate internal validity of the studies. Our objective was to compare the performance of three tools (OHAT, IRIS, TSCA) in assessing ROB.

Epidemiology is a discipline which has evolved with the changes taking place in society and the emergence of new diseases and new discipline related to epidemiology. With these evolutions, it is important to understand epidemiology and to analyse the evolution of content of definitions of epidemiology.

Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various factors, including restricted access to preventative and therapeutic health care, limited sick leave, and household structure. Adequate influenza surveillance in these at-risk populations is a critical precursor to accurate risk assessments and effective intervention. However, the United States of America's primary national influenza surveillance system (ILINet) monitors outpatient healthcare providers, which may be largely inaccessible to lower socioeconomic populations. Recent initiatives to incorporate Internet-source and hospital electronic medical records data into surveillance systems seek to improve the timeliness, coverage, and accuracy of outbreak detection and situational awareness. Here, we use a flexible statistical framework for integrating multiple surveillance data sources to evaluate the adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google Flu Trends) data for situational awareness of influenza across poverty levels. We find that ZIP Codes in the highest poverty quartile are a critical vulnerability for ILINet that the integration of next generation data fails to ameliorate.

We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.

Expectations of fair competition underlie the assumption that academia is a meritocracy. However, bias may reinforce gender inequality in peer review processes, unfairly eliminating outstanding individuals. Here, we ask whether applicant gender biases peer review in a country top ranked for gender equality. We analyzed peer review assessments for recruitment grants at a Swedish medical university, Karolinska Institutet (KI), during four consecutive years (2014-2017) for Assistant Professor (n = 207) and Senior Researcher (n = 153). We derived a composite bibliometric score to quantify applicant productivity and compared this score with subjective external (non-KI) peer reviewer scores of applicants' merits to test their association for men and women, separately. To determine whether there was gender segregation in research fields, we analyzed publication list MeSH terms, for men and women, and analyzed their overlap. There was no gendered MeSH topic segregation, yet men and women with equal merits are scored unequally by reviewers. Men receive external reviewer scores resulting in stronger associations (steeper slopes) between computed productivity and subjective external reviewer scores, meaning that peer reviewers "reward" men's productivity with proportional merit scores. However, women applying for assistant professor or senior researcher receive only 32 or 92% of the score men receive, respectively, for each additional composite bibliometric score point. As productivity increases, the differences in merit scores between men and women increases. Accumulating gender bias is thus quantifiable and impacts the highest tier of competition, the pool from which successful candidates are ultimately chosen. Track record can be computed, and granting organizations could therefore implement a computed track record as quality control to assess whether bias affects reviewer assessments.

Publication bias is an important factor that may result in selection bias and lead to overestimation of the intervention effect. In this study, the focus was on using capture-recapture method as a statistical procedure which may possibly be a practical means for measuring the amount of publication bias.

Research articles tend to focus on positive findings in their abstract, especially if multiple outcomes have been studied. At the same time, search queries in databases are generally limited to the abstract, title and keywords fields of an article. Negative findings are therefore less likely to be detected by systematic searches and to appear in systematic reviews. We aim to assess the occurrence of this 'abstract reporting bias' and quantify its impact in the literature on the association between diesel exhaust exposure (DEE) and bladder cancer.

Attentional bias has been defined as the propensity of a person to allocate selective attention automatically to salient cues (Field and Powell, 2007). In the case of smoking, this bias implies that smokers are implicitly attracted by smoking-related stimuli, which produce behavioral, memory, and emotional effects (Volkow et al., 2006; Giardini et al., 2009). In more detail, scientific evidence pointed out that smoking is strongly supported by attentional bias that activates craving and urgency to smoke a cigarette. However, poor and conflicting data are available regarding the role of this cognitive bias on former smokers. The main aim of this study is to explore the occurrence of the attentional bias on of both current and former smokers, also with the aim to identify associations with behavioral, psychological and cognitive characteristic of participants. We collected data on 245 current, volunteers (male 50.6%; female 49.4%) aged 54.81 (SD = 14.352, range = 18-63), divided in current smokers (98), former smokers (102) and non-smokers (45). A combination of neuropsychology tests (Emotional Smoke Stroop Task and Go/no-Go task), and standardized questionnaires [Behavioral Inhibition System-Behavioral Approach System (BIS-BAS), Fagerström Test for Nicotine Dependence (FTND), Barratt Impulsiveness Scale, Motivational questionnaire] were used to assess the attentional bias, psychological variables, and smoking-related characteristics. Responses at the Emotional Smoke Stroop task revealed that current and former smokers are actually slower than non-smokers are when facing smoking cues, while performances at other Stroop conditions and at the Go/no-Go task are not statistically different. These results confirmed the occurrence of the attentional bias in current smokers, and above all points out that the same effect is present in former smokers. We found only small and selective correlations between attentional bias and psychological variables (e.g., impulsiveness and inhibition). In particular, impulsivity is not directly associated with the AB intensity. Also, smoking characteristics (e.g., years of smoking and dependence level) and the length of the period of abstinence do not seem to modulate implicit cognition of smoking cue. Our data support the idea that the attentional bias may be considered relevant in sustaining smoking and favoring relapse.

Growth hormone (GH) treatment of idiopathic short stature (ISS), defined as height <-2.25 standard deviations (SD), is approved by U.S. FDA. This study determined the gender-specific prevalence of height <-2.25 SD in a pediatric primary care population, and compared it to demographics of U.S. pediatric GH recipients. Data were extracted from health records of all patients age 0.5-20 years with ≥ 1 recorded height measurement in 28 regional primary care practices and from the four U.S. GH registries. Height <-2.25 SD was modeled by multivariable logistic regression against gender and other characteristics. Of the 189,280 subjects, 2073 (1.1%) had height <-2.25 SD. No gender differences in prevalence of height <-2.25 SD or distribution of height Z-scores were found. In contrast, males comprised 74% of GH recipients for ISS and 66% for all indications. Short stature was associated (P < 0.0001) with history of prematurity, race/ethnicity, age and Medicaid insurance, and inversely related (P < 0.0001) with BMI Z-score. In conclusion, males outnumbered females almost 3:1 for ISS and 2:1 for all indications in U.S. pediatric GH registries despite no gender difference in height <-2.25 SD in a large primary care population. Treatment and/or referral bias was the likely cause of male predominance among GH recipients.

Publication bias is a widespread problem that may seriously distort attempts to estimate the effect under investigation. The literature is reviewed to determine features of the design and execution of both single studies and meta-analyses leading to publication bias, and the role the author, journal editor, and reviewer play in selecting studies for publication. Methods of detecting, correcting for, and preventing publication bias are reviewed. The design of the meta-analysis itself, and the studies included in it, are shown to be important among a number of sources of publication bias. Various factors influence an author's decision to submit results for publication. Journal editors and reviewers are crucial in deciding which studies to publish. Various methods proposed for detecting and correcting for publication bias, though useful, all have limitations. However, prevention of publication bias by registering every trial undertaken or publishing all studies is an ideal that is hard to achieve.

In cancer observational studies, differences between groups on confounding variables may have a significant effect on results when examining health outcomes. This study demonstrates the utility of propensity score matching to balance a non-cancer and cancer cohort of older adults on multiple relevant covariates. This approach matches cases to controls on a single indicator, the propensity score, rather than multiple variables. Results indicated that propensity score matching is an efficient and useful way to create a matched case-control study out of a large cohort study, and allows confidence in the strength of the observed outcomes of the study.

Large-scale cross-sectional and cohort studies have transformed our understanding of the genetic and environmental determinants of health outcomes. However, the representativeness of these samples may be limited-either through selection into studies, or by attrition from studies over time. Here we explore the potential impact of this selection bias on results obtained from these studies, from the perspective that this amounts to conditioning on a collider (i.e. a form of collider bias). Whereas it is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates, it is often assumed that it should not have a strong impact on estimates of associations. We argue that because selection can induce collider bias (which occurs when two variables independently influence a third variable, and that third variable is conditioned upon), selection can lead to substantially biased estimates of associations. In particular, selection related to phenotypes can bias associations with genetic variants associated with those phenotypes. In simulations, we show that even modest influences on selection into, or attrition from, a study can generate biased and potentially misleading estimates of both phenotypic and genotypic associations. Our results highlight the value of knowing which population your study sample is representative of. If the factors influencing selection and attrition are known, they can be adjusted for. For example, having DNA available on most participants in a birth cohort study offers the possibility of investigating the extent to which polygenic scores predict subsequent participation, which in turn would enable sensitivity analyses of the extent to which bias might distort estimates.

To examine whether the prestige of a journal, measured by its impact factor, influences the numbers of citations obtained by published articles, independently of their scientific merit.

In 1999, a survey of the health status of six rural central New York counties was performed using double sampling. The resulting impact of the methodology on health outcome prevalence estimation was assessed.

Publication and other forms of selection biases pose a threat to the validity of meta-analysis. Funnel plots are usually used to detect such biases; asymmetrical plots are interpreted to suggest that biases are present. Using 198 published meta-analyses, we demonstrate that the shape of a funnel plot is largely determined by the arbitrary choice of the method to construct the plot. When a different definition of precision and/or effect measure were used, the conclusion about the shape of the plot was altered in 37 (86%) of the 43 meta-analyses with an asymmetrical plot suggesting selection bias. In the absence of a consensus on how the plot should be constructed, asymmetrical funnel plots should be interpreted cautiously. These findings also suggest that the discrepancies between large trials and corresponding meta-analyses and heterogeneity in meta-analyses may also be determined by how they are evaluated.

Classical swine fever (CSF), caused by CSF virus (CSFV), is a highly contagious disease in pigs causing 100% mortality in susceptible adult pigs and piglets. High mortality rate in pigs causes huge economic loss to pig farmers. CSFV has a positive-sense RNA genome of 12.3 kb in length flanked by untranslated regions at 5' and 3' end. The genome codes for a large polyprotein of 3900 amino acids coding for 11 viral proteins. The 1300 codons in the polyprotein are coded by different combinations of three nucleotides which help the infectious agent to evolve itself and adapt to the host environment. This study performed and employed various methods/techniques to estimate the changes occurring in the process of CSFV evolution by analyzing the codon usage pattern.

To investigate whether analysis of the modified intention-to-treat (mITT) population with postrandomization exclusion of patients from analysis is associated with biased estimates of treatment effect compared to the conservative intention-to-treat (ITT) population.

Multiple studies have documented bias in medical decision making, but no studies have examined whether this bias extends to medical coding practices. Medical coding is foundational to the US health care enterprise. We evaluate whether bias based on patient characteristics influences specific coding practices of professional medical coders.

Animal studies aimed at understanding influenza virus mutations that change host specificity to adapt to replication in mammalian hosts are necessarily limited in sample numbers due to high cost and safety requirements. As a safe, higher-throughput alternative, we explore the possibility of using readily available passage bias data obtained mostly from seasonal H1 and H3 influenza strains that were differentially grown in mammalian (MDCK) and avian cells (eggs). Using a statistical approach over 80,000 influenza hemagglutinin sequences with passage information, we found that passage bias sites are most commonly found in three regions: (i) the globular head domain around the receptor binding site, (ii) the region that undergoes pH-dependent structural changes and (iii) the unstructured N-terminal region harbouring the signal peptide. Passage bias sites were consistent among different passage cell types as well as between influenza A subtypes. We also find epistatic interactions of site pairs supporting the notion of host-specific dependency of mutations on virus genomic background. The sites identified from our large-scale sequence analysis substantially overlap with known host adaptation sites in the WHO H5N1 genetic changes inventory suggesting information from passage bias can provide candidate sites for host specificity changes to aid in risk assessment for emerging strains.