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Lead-Time Bias

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21. Anchoring bias in face-to-face Time-Trade-Off valuations of health states. (PubMed)

Anchoring bias in face-to-face Time-Trade-Off valuations of health states. To test whether anchoring (a cognitive bias) occurs during face-to-face interviews to value health states by Time-Trade-Off.147 Colombian subjects (111 males, 36 females) valued five EQ-5D health states better than death during a face-to-face interview. Subjects were randomly assigned to two different starting points.Shapiro-Wilk test discarded normality, while non-parametric tests, including Kolmogorov-Smirnov (...) and Wilcoxon-Mann-Whitney, showed that anchoring was significant in four out of five health states. A higher starting point increased the elicited value by 15 %-188 %. The size of the anchoring effect was not uniform among health states.Anchoring effects may bias face-to-face Time Trade-Off valuations. The size of the anchoring effect is relevant enough for health policy.

2019 Revista de salud publica (Bogota, Colombia)

22. Metformin and Reduced Risk of Cancer in the Hong Kong Diabetes Registry: Real Effect or Immortal Time Bias? (PubMed)

Metformin and Reduced Risk of Cancer in the Hong Kong Diabetes Registry: Real Effect or Immortal Time Bias? Whether metformin reduces cancer risk has been hotly debated. One common opinion is that the observed beneficial effects of metformin are the consequence of immortal time bias.To examine whether the observed beneficial effects of metformin on cancer risk are the consequence of immortal time bias.Retrospective cohort study.A cohort of 3485 patients who started metformin before (...) or at enrollment, 1226 patients who initiated metformin after enrollment, and an unexposed group of 1392 patients who never used metformin.Metformin users were categorized into 11 groups in terms of length of time between metformin initiation and enrollment. The percent changes in immortal person-time were calculated for each group.As the groups of current metformin users (n = 3485) were added sequentially to the metformin group with potential immortal time bias (n = 1226), the proportion of immortal person

2019 Journal of General Internal Medicine

23. Immortal Time Bias: What Are the Determinants of Its Magnitude? (PubMed)

Immortal Time Bias: What Are the Determinants of Its Magnitude? In cohort studies, "immortal time" bias refers to a portion of time during which events cannot occur for a particular group of participants. Typically, immortal time bias occurs when: a) exposure can be initiated after follow-up of cohort members has begun; and b) analytically, the pre-exposure experience is combined with that which takes place following exposure, rather than (correctly) as part of the experience of non-exposed (...) individuals. Using the example of a cohort study of mortality in relation to receipt of cataract surgery, we sought to describe those study design and population characteristics that influence the magnitude of immortal time bias, so as to aid readers in gauging its impact on published research findings. These characteristics include the mean interval between cohort entry and when exposure criteria are met, the proportion of exposed study participants, and the length of study follow-up.© The Author(s) 2019

2019 American Journal of Epidemiology

24. Overall bias and sample sizes were unchanged in ICU trials over time: a meta-epidemiological study. (PubMed)

Overall bias and sample sizes were unchanged in ICU trials over time: a meta-epidemiological study. To assess time trends in risk of bias (RoB) and sample sizes in randomized clinical trials (RCTs) of adult intensive care unit (ICU) patients.A meta-epidemiological study of RCTs from Cochrane systematic reviews assessing interventions in adult ICU patients. Using run charts, we assessed time trends in the annual proportion of RCTs with overall low RoB, the annual median sample sizes (...) , and the annual proportion of RCTs with low, unclear, and high RoB in individual bias domains.We included 604 RCTs published between 1977 and 2018 from 53 Cochrane systematic reviews. Only 6.8% of the RCTs had overall low RoB. We observed only random variation in the annual proportions of RCTs with overall low RoB, in the annual median sample sizes and in most individual bias domains. For "allocation concealment," we observed an increase in the proportion of low RoB RCTs and a decrease in the unclear RoB

2019 Journal of Clinical Epidemiology

25. History of Benzodiazepine Prescriptions and Risk of Dementia: Possible Bias Due to Prevalent Users and Covariate Measurement Timing in a Nested Case-Control Study. (PubMed)

History of Benzodiazepine Prescriptions and Risk of Dementia: Possible Bias Due to Prevalent Users and Covariate Measurement Timing in a Nested Case-Control Study. Previous estimates of whether long-term exposure to benzodiazepines increases dementia risk are conflicting and are compromised by the difficulty of controlling for confounders and by reverse causation. We investigated how estimates for the association between benzodiazepine use and later dementia incidence varied based on study (...) design choices, using a case-control study nested within the United Kingdom's Clinical Practice Research Datalink. A total of 40,770 dementia cases diagnosed between April 2006 and July 2015 were matched on age, sex, available data history, and deprivation to 283,933 control subjects. Benzodiazepines and Z-drug prescriptions were ascertained in a drug-exposure period 4-20 years before dementia diagnosis. Estimates varied with the inclusion of new or prevalent users, with the timing of covariate

2019 American Journal of Epidemiology

26. QALYs without bias? Nonparametric correction of time trade-off and standard gamble weights based on prospect theory. (PubMed)

QALYs without bias? Nonparametric correction of time trade-off and standard gamble weights based on prospect theory. Common health state valuation methodologies, such as standard gamble (SG) and time trade-off (TTO), typically produce different weights for identical health states. We attempt to alleviate these differences by correcting the confounding influences modeled in prospect theory: loss aversion and probability weighting. Furthermore, we correct for nonlinear utility of life duration (...) differences in weights between TTO and SG disappeared for all health states. Our findings suggest new opportunities to account for bias in health state valuations but also the need for further validation of resulting weights.© 2019 The Authors Health Economics Published by John Wiley & Sons Ltd.

2019 Health economics

27. TEMPORARY REMOVAL: Immortal Time Bias in National Cancer Data Base Studies. (PubMed)

TEMPORARY REMOVAL: Immortal Time Bias in National Cancer Data Base Studies. The publisher regrets that this article has been temporarily removed. A replacement will appear as soon as possible in which the reason for the removal of the article will be specified, or the article will be reinstated. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.Copyright © 2019 Elsevier Inc. All rights reserved.

2019 Biology and Physics

28. 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

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. 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

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