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Lead-Time Bias

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21. MiniStatsBlog: Lead time bias

MiniStatsBlog: Lead time bias StatsMiniBlog: Lead time bias | ADC Online Blog by This is a cracking cartoon that highlights everything that’s misleading about lead time bias. And will also lead to the terrible situation of the parents of the bird having to try to keep it away until proper bedtime to stop this happening tomorrow too. Lead time bias is the apparent improvement in duration of a condition (often length of survival or time to relapse) without actually changing the age at death (...) /relapse etc. A great example is in neuroblastoma, which could (theoretically) be screened for in asymptomatic infants with testing. Doing so certainly seems to detect more children with neuroblastoma, but the both during and after screening was introduced and stopped in Japan. Once again, we have to look very carefully about what outcome measures actually mean. – Archi (Visited 17 times, 1 visits today) Post navigation 29 March 2019 Original article 25 March 2019 Original article 20 March 2019 Voices

2015 ADC Blog

22. The Role of Compensation Criteria to Minimize Face-Time Bias and Support Faculty Career Flexibility: An Approach to Enhance Career Satisfaction in Academic Pathology (PubMed)

The Role of Compensation Criteria to Minimize Face-Time Bias and Support Faculty Career Flexibility: An Approach to Enhance Career Satisfaction in Academic Pathology Work-life balance is important to recruitment and retention of the younger generation of medical faculty, but medical school flexibility policies have not been fully effective. We have reported that our school's policies are underutilized due to faculty concerns about looking uncommitted to career or team. Since policies include (...) leaves and accommodations that reduce physical presence, faculty may fear "face-time bias," which negatively affects evaluation of those not "seen" at work. Face-time bias is reported to negatively affect salary and career progress. We explored face-time bias on a leadership level and described development of compensation criteria intended to mitigate face-time bias, raise visibility, and reward commitment and contribution to team/group goals. Leaders from 6 partner departments participated

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2016 Academic pathology

23. Emotional Encoding Context Leads to Memory Bias in Individuals with High Anxiety (PubMed)

Emotional Encoding Context Leads to Memory Bias in Individuals with High Anxiety We investigated whether anxious individuals, who adopt an inherently negative mindset, demonstrate a particularly salient memory bias for words tainted by negative contexts. To this end, sequentially presented target words, overlayed onto negative or neutral pictures, were studied in separate blocks (within-subjects) using a deep or shallow encoding instruction (between-subjects). Following study, in Test 1 (...) . Results show that during retrieval (Test 1), participants re-entered the mode of processing (negative or neutral) engaged at encoding, tainting the encoding of foils with that same mode of processing. The findings suggest that individuals with high relative to low anxiety, adopt a particularly salient negative retrieval mode, and this creates a downstream bias in encoding and subsequent retrieval of otherwise neutral information.

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2017 Brain sciences

24. Social learning may lead to population level conformity without individual level frequency bias (PubMed)

Social learning may lead to population level conformity without individual level frequency bias A requirement of culture, whether animal or human, is some degree of conformity of behavior within populations. Researchers of gene-culture coevolution have suggested that population level conformity may result from frequency-biased social learning: individuals sampling multiple role models and preferentially adopting the majority behavior in the sample. When learning from a single role model (...) , frequency-bias is not possible. We show why a population-level trend, either conformist or anticonformist, may nonetheless be almost inevitable in a population of individuals that learn through social enhancement, that is, using observations of others' behavior to update their own probability of using a behavior in the future. The exact specification of individuals' updating rule determines the direction of the trend. These results offer a new interpretation of previous findings from simulations

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2017 Scientific reports

25. Under-representation of women in stroke randomized controlled trials: inadvertent selection bias leading to suboptimal conclusions (PubMed)

Under-representation of women in stroke randomized controlled trials: inadvertent selection bias leading to suboptimal conclusions 28529545 2018 11 13 1756-2856 10 5 2017 May Therapeutic advances in neurological disorders Ther Adv Neurol Disord Under-representation of women in stroke randomized controlled trials: inadvertent selection bias leading to suboptimal conclusions. 241-244 10.1177/1756285617699588 Tsivgoulis Georgios G Second Department of Neurology, National and Kapodistrian

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2017 Therapeutic advances in neurological disorders

26. Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates (PubMed)

Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates Accuracy of transcript quantification with RNA-Seq is negatively affected by positional fragment bias. This article introduces Mix2 (rd. "mixquare"), a transcript quantification method which uses a mixture of probability distributions to model and thereby neutralize the effects of positional fragment bias. The parameters of Mix2 are trained by Expectation Maximization (...) resulting in simultaneous transcript abundance and bias estimates. We compare Mix2 to Cufflinks, RSEM, eXpress and PennSeq; state-of-the-art quantification methods implementing some form of bias correction. On four synthetic biases we show that the accuracy of Mix2 overall exceeds the accuracy of the other methods and that its bias estimates converge to the correct solution. We further evaluate Mix2 on real RNA-Seq data from the Microarray and Sequencing Quality Control (MAQC, SEQC) Consortia. On MAQC

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2017 PLoS computational biology

27. Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons (PubMed)

Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons In mobile social groups, influence patterns driving group movement can vary between democratic and despotic. The arrival at any single pattern of influence is thought to be underpinned by both environmental factors and group composition. To identify the specific patterns of influence driving travel decision-making in a chacma baboon troop, we used spatially explicit data (...) to extract patterns of individual movement bias. We scaled these estimates of individual-level bias to the level of the group by constructing an influence network and assessing its emergent structural properties. Our results indicate that there is heterogeneity in movement bias: individual animals respond consistently to particular group members, and higher-ranking animals are more likely to influence the movement of others. This heterogeneity resulted in a group-level network structure that consisted

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2017 Royal Society Open Science

28. Diagnostic suspicion bias

that researchers noticed that African Americans were three times (45% vs 19%) more likely to be diagnosed with schizophrenia than whites. viewed African Americans as less honest about their symptoms, with less insight into their condition. These views were associated with higher diagnosis rates with diagnostic suspicion bias being responsible for some of the disparity in diagnosis rates. Preventive steps Prospective studies, with consecutive recruitment of patients and with uniform assessment and measurement (...) Diagnostic suspicion bias Diagnostic suspicion bias - Catalog of Bias Catalogue of Bias Navigate this website Diagnostic suspicion bias Knowledge of a subject’s prior exposures or personal biases may influence both the process and the outcome of diagnostic tests. Table of Contents Background Information about a group or individual coupled with suspicions or prejudices of medical staff could influence how diagnoses are made, by affecting what examinations are performed and how quickly people

2018 Catalogue of Bias

29. Diagnostic access bias

Diagnostic access bias Diagnostic access bias - Catalog of Bias Catalogue of Bias Navigate this website Diagnostic access bias Individuals differ in their geographic, temporal and economic access to diagnostic procedures which label them as having a given disease. Table of Contents Background Individuals may have different access to diagnostic tests, due to cultural, geographic, economic or other reasons, and these factors affect the detection of disease. This leads to diagnostic access bias (...) to 19% of the general population. Health professionals were therefore 2.4 times more likely to be diagnosed with sarcoidosis (95% CI: 1.34 to 4.33) due to the presence of diagnostic access bias. have demonstrated low incidence and prevalence rates of Atrial Fibrillation despite higher rates of relevant cardiovascular risk factors in . A potential explanation is their lower access to healthcare and the diagnostic tests for Atrial Fibrillation. Impact Diagnostic access bias would over-estimate

2018 Catalogue of Bias

30. Detection bias

equivalent in unknown factors. In observational studies, potential sources of detection bias should be sought out, and if identified, adjusted for or stratified by to clarify the observed associations of interest. Statistical adjustment for perceived differences can be used to help reduce the problem, for example adjusting for age to avoid discrepancies in age leading to a spurious observation of association/effect. Detection bias can also be due to the knowledge of the allocated interventions by outcome (...) Detection bias Detection bias - Catalog of Bias Catalogue of Bias Navigate this website Detection bias S ystematic differences between groups in how outcomes are determined . Table of Contents Background A test or treatment for a disease may perform differently according to some characteristic of the study participant, which itself may influence the likelihood of disease detection or the effectiveness of the treatment. Detection bias can occur in trials when groups differ in the way outcome

2018 Catalogue of Bias

31. Chronological bias

Chronological bias has been investigated in randomised controlled trials, specifically looking at the extent of time-related bias in trials in which block randomisation is used. Chronological bias can strongly influence estimates of treatment effects if randomisation sequences are used that are not balanced over time. Undertaking block randomisation with small sample blocks can reduce this bias, but this needs to be balanced against the problems that small block sizes can lead to. Methods Inf Med. 2014 (...) Chronological bias Chronological bias - Catalog of Bias Catalogue of Bias Navigate this website Chronological bias When study participants allocated earlier to an intervention or a group are subject to different exposures or are at a different risk from participants who are recruited later. Table of Contents Background It can take time to recruit participants into a study, whether observational or interventional and sometimes there are differences between those recruited earlier in the process

2018 Catalogue of Bias

32. Ascertainment bias

. In describing how to avoid ascertainment bias, the authors write:“In order to study facial injuries, cases should be limited to serious injuries (lacerations and fractures) that would result in an emergency department visit whether or not a head injury was also present” People with minor facial injuries may be identified because bicyclists seek care for head injuries.” This example illustrates the notion of biased screening for the outcome leading to ascertainment bias. A database analysis found that were (...) Ascertainment bias Ascertainment bias - Catalog of Bias Catalogue of Bias Navigate this website Ascertainment bias Systematic differences in the identification of individuals included in a study or distortion in the collection of data in a study. Table of Contents Background Ascertainment bias arises when data for a study or analysis is collected (or surveyed, screened, or recorded) such that some members of the intended population are less likely to be included than others. The resulting study

2018 Catalogue of Bias

33. Attrition bias

of the exposure or intervention. Losses may be influenced by such factors as unsatisfactory treatment efficacy or intolerable adverse events. When participants leave, it may not be known whether they continue or discontinue an intervention; there may be no data on outcomes for these participants after that time. Systematic differences between people who leave the study and those who continue can introduce bias into a study’s results – this is attrition bias. However, the results may not necessarily be biased (...) Attrition bias Attrition bias - Catalog of Bias Catalogue of Bias Navigate this website Attrition bias Unequal loss of participants from study groups in a trial. Table of Contents Background Attrition occurs when participants leave during a study. It almost always happens to some extent. Different rates of loss to follow-up in the exposure groups, or losses of different types of participants, whether at similar or different frequencies, may change the characteristics of the groups, irrespective

2018 Catalogue of Bias

34. Selection bias

Selection bias Selection bias - Catalog of Bias Catalogue of Bias Navigate this website Selection bias occurs when individuals or groups in a study differ systematically from the population of interest leading to a systematic error in an association or outcome. Table of Contents Background Participants in research may differ systematically from the population of interest. For example, participants included in an influenza vaccine trial may be healthy young adults, whereas those who are most (...) likely to receive the intervention in practice may be elderly and have many comorbidities, and are therefore not representative. Similarly, in observational studies, conclusions from the research population may not apply to real-world people, as the observed effect may be exaggerated or it is not possible to assume an effect in those not included in the study. Selection bias can arise in studies because groups of participants may differ in ways other than the interventions or exposures under

2018 Catalogue of Bias

35. Recall bias

the GET-IT definition of this term" >retrospective cohort studies . In case-control studies, researchers must be careful to ask each study participant in the same way so as not to influence their responses. Bias in recall can be greater when the study participant has poorer recall in general, and when the time interval being asked about is longer. Other issues that influence recall include age, education, socioeconomic status and how important the disease is to the patient. Added to this, undesirable (...) habits such as smoking or eating unhealthy foods tend to be underreported, and are therefore subject to recall bias. Pre-existing beliefs may also impact on recall of previous events. Example Parents of children diagnosed with cancer may be more likely to recall infections earlier in the child’s life than parents of children without cancer. This may lead to observing an entirely or partially untrue association between childhood infection and disease. Recall can be particularly problematic when

2018 Catalogue of Bias

36. Previous opinion bias

at speed and with numerous colleagues previous opinion bias may introduce cognitive errors. Previous opinion bias has many similarities with confirmation bias, where initial or preconceived ideas about something lead to the collection of information that confirms a given view. Preventive steps To reduce uncertainty, health care professionals often consult colleagues for a second opinion. Minimise previous opinion bias can be reduced by asking open-ended questions: ‘Could you examine this patient – I (...) need a second opinion,’ and by not asking leading questions: ‘Could you confirm that….’. To reduce biases such as previous opinion bias, every researcher and healthcare practitioner must strive to observe and use the best available information, in the best possible way, being aware that one’s preconceptions can be misleading. Cite as Catalogue of Bias Collaboration, Heneghan C, Spencer EA. In: Catalogue of Bias 2017. Previous opinion bias. https://catalogofbias.org/biases/previous-opinion-bias

2018 Catalogue of Bias

37. Positive results bias

Positive results bias Positive results bias - Catalog of Bias Catalogue of Bias Navigate this website Positive results bias The tendency to submit, accept and publish positive results rather than non-significant or negative results. Table of Contents Background Positive results bias occurs because a considerable amount of research evidence goes unpublished, which contains more negative or null results than positive ones. This leads to spurious claims and overestimation of the results (...) of systematic reviews and can also be considered unethical. Non-publication of results can also lead to research wastage as researchers may unnecessarily repeat studies because the results are unpublished. Example A review of empirical studies and assessment of 300 systematic review found that trials with positive outcomes are twice as likely to be published, and published faster, compared with trials with negative outcomes (Song et al.). This review also found that there was ‘ It is also possible

2018 Catalogue of Bias

38. One-sided reference bias

One-sided reference bias One-sided reference bias - Catalog of Bias Catalogue of Bias Navigate this website One-sided reference bias When authors restrict their references to only those works that support their position. Table of Contents Background One-sided reference bias occurs when a study author cites only publications that demonstrate one side of the picture of available evidence. This bias may arise when researchers cite publications that support their preconceptions or hypotheses (...) , ignoring evidence that does not support their view. This can happen in any study report, but a particular problem arises when this occurs in literature reviews, which are supposed to represent a comprehensive collection of all relevant information, along with description and appraisal of quality and content. The result can be a misrepresentation of the current totality of evidence and can lead to spurious claims or needless additional research. Example ‘Retrieving literature by scanning reference lists

2018 Catalogue of Bias

39. Prevalence-incidence (Neyman) bias

Prevalence-incidence (Neyman) bias Prevalence-incidence (Neyman) bias - Catalog of Bias Catalogue of Bias Navigate this website Prevalence-incidence (Neyman) bias Exclusion of individuals with severe or mild disease resulting in a systematic error in the estimated association or effect of an exposure on an outcome. Table of Contents Background Prevalence-incidence bias or Neyman’s bias occurs due to the timing of when cases are included in a research study. David Sackett wrote in 1979: “A late (...) look at those exposed (or affected) early will miss fatal and other short episodes, plus mild or ‘silent’ cases and cases in which evidence of exposure disappears with disease onset.” Excluding patients who have died will make the disease appear less severe. Excluding patients who have recovered will make the disease seem more severe. The Greater the time between exposure and investigation means more likelihood of individuals dying or recovering from the disease and therefore being excluded from

2018 Catalogue of Bias

40. Non-contemporaneous control bias

Non-contemporaneous control bias Non-contemporaneous control bias - Catalog of Bias Catalogue of Bias Navigate this website Non-contemporaneous control bias Differences in the timing of selection of case and controls within in a study influence exposures and outcomes resulting in biased estimates. Table of Contents Background If in a case-control study, cases are selected during one period and controls are selected during another period, then the relationships observed between exposures (...) and outcomes of interest might be affected. Changes in disease or diagnostic definitions, exposures over time and treatments could all contribute to non-contemporaneous bias. Case-control studies can use historical controls in their design. For practical reasons, this can be a useful approach since it avoids the need to collect new information for the control group. However, this risks introducing non-contemporaneous control bias as over time there may have been changing factors affecting controls

2018 Catalogue of Bias

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