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Lead-Time Bias

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81. Single time point comparisons in longitudinal randomized controlled trials: power and bias in the presence of missing data. (PubMed)

Single time point comparisons in longitudinal randomized controlled trials: power and bias in the presence of missing data. The primary analysis in a longitudinal randomized controlled trial is sometimes a comparison of arms at a single time point. While a two-sample t-test is often used, missing data are common in longitudinal studies and decreases power by reducing sample size. Mixed models for repeated measures (MMRM) can test treatment effects at specific time points, have been shown (...) to give unbiased estimates in certain missing data contexts, and may be more powerful than a two sample t-test.We conducted a simulation study to compare the performance of a complete-case t-test to a MMRM in terms of power and bias under different missing data mechanisms. Impact of within- and between-person variance, dropout mechanism, and variance-covariance structure were all considered.While both complete-case t-test and MMRM provided unbiased estimation of treatment differences when data were

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2016 BMC medical research methodology

82. The impact of healthcare visit timing on reported pertussis cough duration: Selection bias and disease pattern from reported cases in Michigan, USA, 2000-2010. (PubMed)

The impact of healthcare visit timing on reported pertussis cough duration: Selection bias and disease pattern from reported cases in Michigan, USA, 2000-2010. Pertussis is a potentially serious respiratory illness characterized by cough of exceptionally long duration of up to approximately100 days. While macrolide antibiotics are an effective treatment, there is an ongoing debate whether they also shorten the length of cough symptoms. We investigated whether public health surveillance data (...) from the observed surveillance data and truncated week-by-week to evaluate the effects of bias associated with stratification on timing of antibiotics.Cases presenting for medical evaluation later in the clinical course were more likely to have experienced delayed antibiotic therapy and longer average cough duration. A comparable magnitude of increasing cough duration was also observed in the simulated data. By stratifying on initial medical visit, selection bias effects based on timing

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2016 BMC Infectious Diseases

83. The lag-time approach improved drug-outcome association estimates in presence of protopathic bias. (PubMed)

The lag-time approach improved drug-outcome association estimates in presence of protopathic bias. Protopathic bias is a systematic error which occurs when measured exposure status may be affected by the latent onset of the target outcome. In this article, we aimed to discuss the benefits and drawbacks of the lag-time approach to address this type of bias.The lag-time approach consists in excluding from exposure assessment the period immediately preceding the outcome detection date (...) ) of short-acting beta-agonists (SABAs, an asthma reliever medication) during the 90 days before the ED admission date was associated with an increased risk of the outcome [odds ratio (OR): 1.95; 95% confidence interval (CI): 1.72, 2.22]. This paradoxical finding may be explained by protopathic bias, as SABA use prior the ED admission may be affected by preceding respiratory distress. Indeed, when a 120-day period preceding the ED admission was ignored from drug exposure assessment (lag time), SABAs were

2016 Journal of Clinical Epidemiology

84. Beta blockers and cancer prognosis - The role of immortal time bias: A systematic review and meta-analysis. (PubMed)

Beta blockers and cancer prognosis - The role of immortal time bias: A systematic review and meta-analysis. Findings from experimental and observational studies have suggested beneficial effects of beta blocker (BB) use on cancer survival. Nevertheless, results have been inconclusive and there have been concerns that the observed associations might have resulted from immortal time bias (ITB). We conducted a systematic review and meta-analysis to summarize existing evidence, paying particular

2016 Cancer Treatment Reviews

85. Erratum to: Bias and precision of methods for estimating the difference in restricted mean survival time from an individual patient data meta-analysis. (PubMed)

Erratum to: Bias and precision of methods for estimating the difference in restricted mean survival time from an individual patient data meta-analysis. 27306030 2018 11 13 1471-2288 16 1 2016 Jun 15 BMC medical research methodology BMC Med Res Methodol Erratum to: Bias and precision of methods for estimating the difference in restricted mean survival time from an individual patient data meta-analysis. 71 Lueza Béranger B Gustave Roussy, Université Paris-Saclay, Service de biostatistique et

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2016 BMC medical research methodology

86. Systematic Bias in Meta-Analyses of Time to Antimicrobial in Sepsis Studies.

Systematic Bias in Meta-Analyses of Time to Antimicrobial in Sepsis Studies. 26974458 2016 07 14 2018 12 02 1530-0293 44 4 2016 Apr Critical care medicine Crit. Care Med. Systematic Bias in Meta-Analyses of Time to Antimicrobial in Sepsis Studies. e234-5 10.1097/CCM.0000000000001512 Kumar Anand A Sections of Critical Care Medicine and Infectious Diseases, Departments of Medicine, Medical Microbiology and Pharmacology/Therapeutics, University of Manitoba, Winnipeg, MB, Canada. eng Letter Comment

2016 Critical Care Medicine

87. Influenced from the start: anchoring bias in time trade-off valuations (PubMed)

as anchoring bias. The aim of the study was to explore the potential anchoring effect and its magnitude in TTO experiments.A total of 1249 respondents valued 8 EQ-5D health states in a Web study. We used the lead time TTO (LT-TTO) which allows eliciting negative and positive values with a uniform method. Respondents were randomized to 11 different SPs. Anchoring bias was assessed using OLS regression with SP as the independent variable. In a secondary experiment, we compared two different SPs in the UK EQ (...) Influenced from the start: anchoring bias in time trade-off valuations The de facto standard method for valuing EQ-5D health states is the time trade-off (TTO), an iterative choice procedure. The TTO requires a starting point (SP), an initial offer of time in full health which is compared to a fixed offer of time in impaired health. From the SP, the time in full health is manipulated until preferential indifference. The SP is arbitrary, but may influence respondents, an effect known

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2016 Quality of Life Research

88. In Response to: Systematic Bias in Meta-analyses of Time to Antimicrobial in Sepsis Studies (PubMed)

In Response to: Systematic Bias in Meta-analyses of Time to Antimicrobial in Sepsis Studies 26974459 2016 07 14 2018 12 02 1530-0293 44 4 2016 Apr Critical care medicine Crit. Care Med. The authors reply. e235-6 10.1097/CCM.0000000000001550 Sterling Sarah A SA Department of Emergency Medicine, University of Mississippi Medical Center, School of Medicine, Jackson, MS. Puskarich Michael A MA Jones Alan E AE eng K23 GM113041 GM NIGMS NIH HHS United States Letter Research Support, N.I.H

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2016 Critical Care Medicine

89. Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies. (PubMed)

Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies. In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time (...) -distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential

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2016 American Journal of Epidemiology

90. Time‐Dependent Bias of Tumor Growth Rate and Time to Tumor Regrowth (PubMed)

Time‐Dependent Bias of Tumor Growth Rate and Time to Tumor Regrowth 27730754 2018 11 13 2163-8306 5 11 2016 11 CPT: pharmacometrics & systems pharmacology CPT Pharmacometrics Syst Pharmacol Time-Dependent Bias of Tumor Growth Rate and Time to Tumor Regrowth. 587 10.1002/psp4.12145 Mistry H B HB Manchester School of Pharmacy, Manchester, UK. eng Letter 2016 11 10 United States CPT Pharmacometrics Syst Pharmacol 101580011 2163-8306 2016 09 15 2016 10 05 2016 10 13 6 0 2016 10 13 6 0 2016 10 13

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2016 CPT: pharmacometrics & systems pharmacology

91. Magnetic susceptibility induced echo time shifts: Is there a bias in age-related fMRI studies? (PubMed)

Magnetic susceptibility induced echo time shifts: Is there a bias in age-related fMRI studies? To evaluate the potential for bias in functional magnetic resonance imaging (fMRI) aging studies resulting from age-related differences in magnetic field distributions that can impact echo time and functional contrast.Magnetic field maps were taken on 31 younger adults (age: 22 ± 2.9 years) and 46 older adults (age: 66 ± 4.5 years) on a 3T scanner. Using the spatial gradients of the magnetic field map (...) for each participant, an echo planar imaging (EPI) trajectory was simulated. The effective echo time, time at which the k-space trajectory is the closest to the center of k-space, was calculated. This was used to examine both within-subject and across-age-group differences in the effective echo time maps. The blood oxygenation level-dependent (BOLD) percent signal change resulting from those echo time shifts was also calculated to determine their impact on fMRI aging studies.For a single subject

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2016 Journal of magnetic resonance imaging : JMRI

92. Bias and precision of methods for estimating the difference in restricted mean survival time from an individual patient data meta-analysis. (PubMed)

Bias and precision of methods for estimating the difference in restricted mean survival time from an individual patient data meta-analysis. The difference in restricted mean survival time ([Formula: see text]), the area between two survival curves up to time horizon [Formula: see text], is often used in cost-effectiveness analyses to estimate the treatment effect in randomized controlled trials. A challenge in individual patient data (IPD) meta-analyses is to account for the trial effect. We (...) study, we varied the between-trial heterogeneity for the baseline hazard and for the treatment effect (possibly correlated), the overall treatment effect, the time horizon [Formula: see text], the number of trials and of patients, the use of fixed or DerSimonian-Laird random effects model, and the proportionality of hazards. We compared the methods in terms of bias, empirical and average standard errors. We used IPD from the Meta-Analysis of Chemotherapy in Nasopharynx Carcinoma (MAC-NPC) and its

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2016 BMC medical research methodology

93. Limited benefits of cognitive bias modification for adolescents: is it time to move on?

Limited benefits of cognitive bias modification for adolescents: is it time to move on? Limited benefits of cognitive bias modification for adolescents Search National Elf Service Search National Elf Service » » » » Limited benefits of cognitive bias modification for adolescents: is it time to move on? Mar 9 2015 Posted by Cognitive bias modification (CBM) interventions have grown extremely popular over the last decade, both with researchers, as well as with end-users. The term is an umbrella (...) for CBM interventions . Is it time to move on from cognitive bias modification? Limitations The “dose” of training might have been insufficient, though on the other hand there is no evidence that for children and adolescents a dose effect of number of sessions would exist. Participant feedback for the task was mixed, with many reporting it as too long or too stereotyped. Participants were unselected so maybe they did not really have negative interpretation biases that could be reduced through training

2015 The Mental Elf

94. Reporting Bias Leading to Discordant Venous Thromboembolism Rates in the United States Versus Non-US Countries Following Radical Cystectomy: A Systematic Review and Meta-analysis. (PubMed)

Reporting Bias Leading to Discordant Venous Thromboembolism Rates in the United States Versus Non-US Countries Following Radical Cystectomy: A Systematic Review and Meta-analysis. Postcystectomy bladder cancer (BCa) patients are at high risk for developing venous thromboembolism (VTE). The literature varies widely in the reporting of VTE in this population.To determine the VTE rate in subjects undergoing radical cystectomy (RC) and highlight specific factors affecting this rate.This meta

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2017 European urology focus

95. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. (PubMed)

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering (...) group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model

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2019 Annals of Internal Medicine

96. Failure to Fail Part 2: Types of Evaluation Bias

Failure to Fail Part 2: Types of Evaluation Bias Failure to Fail Part 2: Types of Evaluation Bias - CanadiEM Failure to Fail Part 2: Types of Evaluation Bias In by Nadim Lalani July 14, 2016 We know that the best way to evaluate learners is by directly observing what they do in the workplace. Unfortunately [for a variety of reasons] we do not do enough of this. In my I described some reasons why we sometimes fail at making appropriate judgments about failing learners. When it comes to providing (...) feedback, there is much room for improvement.We know that feedback can be influenced by the source, the recipient and the message. What most people don’t know is that, when you’re evaluating a learner, you yourself could be unwittingly introducing bias – just like when we make diagnoses. Types of Evaluation Bias: If a learner really excels in one area, this may positively influence their evaluation in other areas. For example, a resident successfully/quickly intubates a patient with respiratory arrest

2016 CandiEM

97. Gender-Bias Bias Part 2: Unpicking Cherry-Picking

is a highly-cited review on gender bias that I’d been asked for an opinion on ( ). It’s in the Proceedings of the National Academy of Sciences of the USA (PNAS). And it doesn’t need take long to realize it’s a very shaky house of cards. But it took a long time to work through thoroughly. I had several false starts, going down roads that would need far too much time to finish. I hadn’t found a recent rigorous systematic review. So I tried to weigh up how much cherry-picking there was from within the study (...) Gender-Bias Bias Part 2: Unpicking Cherry-Picking Gender-Bias Bias Part 2: Unpicking Cherry-Picking | PLOS Blogs Network PLOS Blogs Staff Blogs Blogs by Topic Biology & Life Sciences Earth & Environmental Sciences Multi-disciplinary Sciences Medicine & Health Research Analysis & Scientific Policy Diverse perspectives on science and medicine Staff Blogs Blogs by Topic Biology & Life Sciences Earth & Environmental Sciences Multi-disciplinary Sciences Medicine & Health Research Analysis

2016 PLOS Blogs Network

98. Applying quantitative bias analysis to estimate the plausible effects of selection bias in a cluster randomised controlled trial: secondary analysis of the Primary care Osteoarthritis Screening Trial (POST) (PubMed)

of bias present in studies. Due to lack of information on the selection probability, probabilistic bias analysis with a range of triangular distributions was also used, applied at all three follow-up time points; 3, 6, and 12 months post consultation. A simple bias analysis was also applied to the study.Worse pain outcomes were observed among intervention participants than control participants (crude odds ratio at 3, 6, and 12 months: 1.30 (95% CI 1.01, 1.67), 1.39 (1.07, 1.80), and 1.17 (95% CI 0.90 (...) Applying quantitative bias analysis to estimate the plausible effects of selection bias in a cluster randomised controlled trial: secondary analysis of the Primary care Osteoarthritis Screening Trial (POST) Selection bias is a concern when designing cluster randomised controlled trials (c-RCT). Despite addressing potential issues at the design stage, bias cannot always be eradicated from a trial design. The application of bias analysis presents an important step forward in evaluating whether

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2017 Trials

99. This is How Research Gender-Bias Bias Works

and publication selection bias: hands up who’s not here? The Ceci and Williams paper included 35 studies to come to their conclusions that “it’s a level playing field” and it’s women’s choices that lead to their underrepresentation. Some of those were reviews, and some were a few – but not all! – of the studies in those reviews. It was an odd set and we don’t know how or why they were selected for the review. Take the level playing field they say exists at journals. There were at least 16,000 active science (...) This is How Research Gender-Bias Bias Works This is How Research Gender-Bias Bias Works | Absolutely Maybe PLOS Blogs Staff Blogs Blogs by Topic Biology & Life Sciences Earth & Environmental Sciences Multi-disciplinary Sciences Medicine & Health Research Analysis & Scientific Policy Diverse perspectives on science and medicine Staff Blogs Blogs by Topic Biology & Life Sciences Earth & Environmental Sciences Multi-disciplinary Sciences Medicine & Health Research Analysis & Scientific Policy Post

2016 Absolutely Maybe

100. Gender-Bias Bias Part 2: Unpicking Cherry-Picking

on gender bias that I’d been asked for an opinion on ( ). It’s in the Proceedings of the National Academy of Sciences of the USA (PNAS). And it doesn’t take long to realize it’s a very shaky house of cards. But it took a long time to work through thoroughly. I had several false starts, going down roads that would need far too much time to finish. I hadn’t found a recent rigorous systematic review. So I tried to weigh up how much cherry-picking there was from within the study itself: by looking at all (...) Gender-Bias Bias Part 2: Unpicking Cherry-Picking Gender-Bias Bias Part 2: Unpicking Cherry-Picking | Absolutely Maybe PLOS Blogs Staff Blogs Blogs by Topic Biology & Life Sciences Earth & Environmental Sciences Multi-disciplinary Sciences Medicine & Health Research Analysis & Scientific Policy Diverse perspectives on science and medicine Staff Blogs Blogs by Topic Biology & Life Sciences Earth & Environmental Sciences Multi-disciplinary Sciences Medicine & Health Research Analysis & Scientific

2016 Absolutely Maybe

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