Magnitude Of Bias Research Articles

Hidden biases in clinical decision-making: potential solutions, challenges, and perspectives.

Every day, we must make decisions that range from simple and risk-free to difficult and risky. Our cognitive sources' limitations, as well as the need for speed, can frequently impair the quality and accuracy of our reasoning processes. Indeed, cognitive shortcuts lead us to solutions that are sufficiently satisfying to allow us to make quick decisions. Unfortunately, heuristics frequently misguide us, and we fall victim to biases and systematic distortions of our perceptions and judgments. Because suboptimal diagnostic reasoning processes can have dramatic consequences, the clinical setting is an ideal setting for developing targeted interventions to reduce the rates and magnitude of biases. There are several approaches to bias mitigation, some of which may be impractical. Furthermore, advances in information technology have given us powerful tools for addressing and preventing errors in health care. Recognizing and accepting the role of biases is only the first and unavoidable step toward any effective intervention proposal. As a result, our narrative review aims to present some insights on this contentious topic based on both medical and psychological literature.

Resilience of parity-violation-induced chiral selectivity to nonequilibrium temperature fluctuations in open systems

We consider the sensitivity of chemical reactions, able to undergo spontaneous mirror symmetry breaking, to chiral bias in the presence of nonequilibrium temperature fluctuations and derive a selectivity criterion. For this, we estimate the magnitude of fluctuations $\ensuremath{\delta}k$ in chemical reaction rate constants $k$ arising from the nonequilibrium temperature fluctuations $\ensuremath{\delta}T$ about a mean value $T$. To leading order, the relative rate constant fluctuations $\ensuremath{\delta}{k}_{i}/{k}_{i}$ for each reaction $i$ are given by the product of the activation enthalpy $\mathrm{\ensuremath{\Delta}}{H}_{i}^{\ifmmode\ddagger\else\textdaggerdbl\fi{}}/RT$ for the $i\mathrm{th}$ reaction multiplied by the relative rms temperature fluctuations $\ensuremath{\delta}{T}_{\mathrm{rms}}/T$. The latter are determined by the system's specific heat at constant volume: ${C}_{V}$. We test this criterion with simulations carried out for an open-flow fully reversible Frank model, and for a range of parity-violating energy differences (PVED) within the theoretically estimated upper and lower bounds. Depending on the relative magnitudes of the deterministic PVED bias and the temperature fluctuations, the PVED bias can either (i) select the final stable chiral outcome deterministically or (ii) select one of two possible stable chiral outcomes with an asymmetric statistical weighting. For larger temperature fluctuations, the PVED bias loses its selectivity, and the final stable chiral outcomes are stochastic and equally probable. This paper points towards the possible design of small volume chemical flow reactors capable of detecting the elusive PVED bias in bulk systems, provided other sources of fluctuations can be sufficiently controlled and attenuated.

Reliability And Validity Of A Local Positioning System For Measuring External Load In Ice Hockey

Purpose: The purposes of this study were to i) determine the reliability of the Kinexon local positioning system (LPS) by collecting on-ice measures with two identical LPS sensors on each individual during an on-ice session, and ii) determine the validity of the LPS measures by comparing on-ice speed and acceleration between the LPS and a previously validated robotic sprint device during on-ice 40 m linear sprints. Methods: Fourteen ice hockey players (25.1 y, 78.6 kg, 176.9 cm) wore two LPS sensors on the back of their shoulder pads to examine the reliability of the LPS during five on-ice drills. Reliability LPS measures included time-on-ice, skating distance, peak skating speed, peak acceleration, peak deceleration, number of accelerations and decelerations, accumulative acceleration load, turns, changes of direction, and skating transitions. Peak speed, speed at 5 m, and 0-5 m acceleration derived from LPS were measured during three 40 m linear on-ice sprints and compared to a previously validated robotic sprint device (1080 Sprint) to examine LPS validity. The coefficient of variation (CV), standard error of measurement (SEM), and intra-class correlation coefficient (ICC) were calculated for each LPS measure. Pearson’s correlations, simple linear regressions, and Bland-Altman plots were used to test the agreement and relationship between the two systems. Statistical significance was accepted at p < 0.05. Results: The majority of LPS measures were reliable (CV < 10% and ICC > 0.9) when comparing the two sensors worn by each player. Peak speed, speed at 5 m, and 0-5 m acceleration were all comparable to those reported by the robotic sprint device, with nearly perfect (peak speed and 0-5 m acceleration; r > 0.967; p < 0.001) and very large (speed at 5 m; r = 0.892, p < 0.001) magnitudes of correlation and mean biases <0.5 km/hr for speed measures and < 0.01 m/s2 for acceleration. However, the number of accelerations and decelerations may need to be used with caution at this time. Conclusion: The present results demonstrate that the Kinexon LPS is reliable and valid for investigating on-ice external load in ice hockey players when sensors are consistently secured on the back of the players’ shoulder pads. Supported by Mitacs and PepsiCo.

Bias in exposure-outcome associations when using left truncated birth datasets.

ObjectivesRestriction to the analysis of births that survive past a specified gestational age (typically 20 weeks gestation) can lead to biased exposure-outcome associations. The objective is to estimate the influence of bias resulting from using a left truncated dataset to ascertain exposure-outcome associations in perinatal studies. ApproachWe simulated the magnitude of bias under a collider-stratification mechanism for the association between the exposure of advancing maternal age (≥ 35 years) and the outcome of stillbirth. This bias occurs when the cause of restriction (early pregnancy loss) is influenced by both the exposure and unmeasured factors that also affect the outcome. Simulation parameters were based on an original birth cohort from Western Australia and a range of plausible values for the prevalence of early pregnancy loss (&lt; 20 gestational weeks), an unmeasured factor U and the odds ratios for the selection effects. Selection effects included the effects of maternal age on early pregnancy loss, U on early pregnancy loss, and U on stillbirth. We then compared the simulated scenarios with the results from the original cohort in which bias was unadjusted. ResultsWe found the overall magnitude of bias to be minimal in the association between advancing maternal age and stillbirth. The findings indicate that the stronger the effect of the unmeasured U on early pregnancy loss and stillbirth, the greater the departure from the null. When we compared the simulated model with the results of the original cohort, we found evidence of marginal downward bias which was most prominent for women aged 40+ years. DiscussionOur simulations demonstrated a marginal downward bias in the association between advancing maternal age and stillbirth. We recommend that future studies should quantify the extent of such bias when using left truncated birth datasets to determine exposure-outcome associations.

The ExtremeX global climate model experiment: investigating thermodynamic and dynamic processes contributing to weather and climate extremes

Abstract. The mechanisms leading to the occurrence of extreme weather and climate events are varied and complex. They generally encompass a combination of dynamic and thermodynamic processes, as well as drivers external to the climate system, such as anthropogenic greenhouse gas emissions and land use change. Here we present the ExtremeX multi-model intercomparison experiment, which was designed to investigate the contribution of dynamic and thermodynamic processes to recent weather and climate extremes. The numerical experiments are performed with three Earth system models: CESM, MIROC, and EC-Earth. They include control experiments with interactive atmosphere and land surface conditions, as well as experiments wherein the atmospheric circulation, soil moisture, or both are constrained using observation-based data. The temporal evolution and magnitude of temperature anomalies during heatwaves are well represented in the experiments with a constrained atmosphere. However, the magnitude of mean climatological biases in temperature and precipitation are not greatly reduced in any of the constrained experiments due to persistent or newly introduced biases. This highlights the importance of error compensations and tuning in the standard model versions. To show one possible application, ExtremeX is used to identify the main drivers of heatwaves and warm spells. The results reveal that both atmospheric circulation patterns and soil moisture conditions substantially contribute to the occurrence of these events. Soil moisture effects are particularly important in the tropics, the monsoon areas, and the Great Plains of the United States, whereas atmospheric circulation effects are major drivers in other midlatitude and high-latitude regions.

Single-/fused-band dual-mode mid-infrared imaging with colloidal quantum-dot triple-junctions

Image data acquired with fused multispectral information can be used for effective identification and navigation owing to additional information beyond human vision, including thermal distribution, night vision, and molecular composition. However, the construction of photodetectors with such capabilities is hindered by the structural complexity arising from the integration of multiple semiconductor junctions with distinct energy gaps and lattice constants. In this work, we develop a colloidal quantum-dot dual-mode detector capable of detecting, separating, and fusing photons from various wavelength ranges. Using three vertically stacked colloidal quantum-dot homojunctions with alternating polarity, single-band short-wave infrared imaging and fused-band imaging (short-wave and mid-wave infrared) can be achieved with the same detector by controlling bias polarity and magnitude. The dual-mode detectors show detectivity up to 8 × 10 10 Jones at the fused-band mode and 3.1 × 10 11 Jones at the single-band mode, respectively. Without image post-processing algorithms, the dual-mode detectors could provide both night vision and thermal information-enhanced night vision imaging capability. To the best of our knowledge, this is the first colloidal quantum-dot detector that can achieve such functionality. The operation mode can be changed at a high frequency up to 1.7 MHz, making it possible to achieve simultaneously dual-mode imaging and remote temperature sensing.

Estimations of Mutation Rates Depend on Population Allele Frequency Distribution: The Case of Autosomal Microsatellites.

Microsatellites (or short-tandem repeats (STRs)) are widely used in anthropology and evolutionary studies. Their extensive polymorphism and rapid evolution make them the ideal genetic marker for dating events, such as the age of a gene or a population. This usage requires the estimation of mutation rates, which are usually estimated by counting the observed Mendelian incompatibilities in one-generation familial configurations (typically parent(s)–child duos or trios). Underestimations are inevitable when using this approach, due to the occurrence of mutational events that do not lead to incompatibilities with the parental genotypes (‘hidden’ or ‘covert’ mutations). It is known that the likelihood that one mutation event leads to a Mendelian incompatibility depends on the mode of genetic transmission considered, the type of familial configuration (duos or trios) considered, and the genotype(s) of the progenitor(s). In this work, we show how the magnitude of the underestimation of autosomal microsatellite mutation rates varies with the populations’ allele frequency distribution spectrum. The Mendelian incompatibilities approach (MIA) was applied to simulated parent(s)/offspring duos and trios in different populational scenarios. The results showed that the magnitude and type of biases depend on the population allele frequency distribution, whatever the type of familial data considered, and are greater when duos, instead of trios, are used to obtain the estimates. The implications for molecular anthropology are discussed and a simple framework is presented to correct the naïf estimates, along with an informatics tool for the correction of incompatibility rates obtained through the MIA.

The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to and Mortality.

Background:Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.Objectives:We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration–response relationships between annual exposure to fine particulate matter [PM in aerodynamic diameter ()] and all-cause mortality.Methods:In this simulation study, we use daily predictions at spatial resolution to estimate annual exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration–response relationship between exposure and mortality (linear, quadratic, or soft-threshold—i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration–response relationship between exposure and mortality, adjusting for the confounders.Results:We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration–response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.Discussion:Our findings suggest that the causal determination for long-term exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389

Assessment of Climate Models Performance and Associated Uncertainties in Rainfall Projection from CORDEX over the Eastern Nile Basin, Ethiopia

The adverse impact of climate change on different regionally important sectors such as agriculture and hydropower is a serious concern and is currently at the epicentre of global interest. Despite the extensive efforts to project the future climate and assess its potential impact, it is surrounded by uncertainties. This study aimed to assess climate models’ performance and associated uncertainties in rainfall projection over the eastern Nile basin, Ethiopia. Seventeen climate models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and their four ensemble models were evaluated in terms of their historical prediction performance (1986–2005) and future simulation skill (2006–2016) at rainfall station (point location), grid-scale (0.44° × 0.44°) and basin scale. Station-based and spatially interpolated observed rainfall data were used as a reference during climate model performance evaluation. In addition, CRU data was used as an alternative reference data to check the effect of the reference data source on the climate models evaluation process. As the results showed, climate models have a large discrepancy in their projected rainfall and hence prior evaluation of their performance is necessary. For instance, the bias in historical mean annual rainfall averaged over the basin ranges from +760 mm (wet bias) to −582 mm (dry bias). The spatial pattern correlation (r) of climate models output and observed rainfall ranges from −0.1 to 0.7. The ensemble formed with selected (performance-based) member models outperforms the widely used multi-model ensemble in most of the evaluation metrics. This showed the need for reconsidering the widely used multi-model approach in most climate model-based studies. The use of CRU data as a reference resulted in a change in the magnitude of climate model bias. To conclude, each climate model has a certain degree of uncertainty in the rainfall projection, which potentially affects the studies on climate change and its impact (e.g., on water resources). Therefore, climate-related studies have to consider uncertainties in climate projections, which will help end-users (decision-makers) at least to be aware of the potential range of deviation in the future projected outcomes of interest.

Clinical relevance of the cagA and vacA s1m1 status and antibiotic resistance in Helicobacter pylori: a systematic review and meta-analysis

BackgroundThe role of Helicobacter pylori (H. pylori) virulence factors of such as vacA s1m1 and cagA in designating clinical outcomes and eradication rate has been deeply challenged in the last decade. The goal of this analysis was to identify the potential relevance between cagA and vacA genotypes with reported antibiotic resistance observed in clinical H. pylori isolates.MethodsThis literature search was conducted in databases such as Clarivate analytics, PubMed, Scopus, EMBASE, DOAJ, and Google Scholar by April 2022, regardless of language restrictions and publication date. Quality of the included studies was assessed by the Newcastle–Ottawa scale. Statistical analysis of retrieved studies was fulfilled using Comprehensive Meta-Analysis software version 2.2. Following quality appraisal of eligible studies, potential association between the status of cagA and vacA genes with resistance to clarithromycin, metronidazole, amoxicillin, tetracycline, and levofloxacin was measured using odds ratio with 95% confidence interval. We also used sensitivity analyses and meta-regression to eliminate the source of heterogeneity from the overall estimates. Publication bias was assessed using funnel plot, Egger’s test, Begg’s test with the trim and fill procedure to assess the presence and magnitude of publication bias in the included studies.ResultsOur findings suggested that a significant relationship between cagA status ‎and increase resistance ‎to metronidazole (OR: 2.69; 95% CI: 1.24–5.83‎‏‎). In subgroup analysis, we ‎found that in the Western ‎population, infection with cagA-positive strains could be led to increase in ‎the resistance to ‎metronidazole (OR: 1.59; 95% CI: ‎0.78–3.21‎‏‎), ‎amoxicillin (OR: ‎19.68‎; 95% CI: 2.74–‎‎141.18), ‎and ‎levofloxacin (OR: ‎11.33; 95% CI: ‎1.39–‎‎91.85). After implementation of trim and fill method, the adjusted OR was not significantly differed from original estimates which in turn represented our subgroup analysis was statistically robust. On the other hand, vacA ‎genotypes usually ‎reduce the antibiotic resistance of this bacterium, so that vacA s1m1 significantly reduces the ‎resistance to ‎metronidazole (OR: 0.41; 95% CI: 0.20–0.86‎‏‎). Surprisingly, resistance of vacA s2m2 strains to antibiotics was low, the reason may be due ‎to the non-inflammatory properties of strains containing vacA s2m2. The meta-regression and sensitivity analyses successfully reduced the effect of heterogeneity from the overall estimates. In addition, although the pooled OR is reduced after trim and fill adjustment but results do not change the conclusion regarding vacA genotypes and antibiotic resistance.ConclusionsAccording to our findings, it was clearly demonstrated that cagA-positive strains are resistance to metronidazole, especially in Western countries. In Western countries, vacA s1m1 increases resistance to amoxicillin and levofloxacin. Based on the present findings, the vacA s1m1 genotype significantly increases resistance to metronidazole, while the vacA s1m2 decreases resistance to clarithromycin and metronidazole. Resistance to antibiotics in less virulent (vacA s2m2) strains is statistically significant lower than others.

Statistical Calibration of Long-Term Reanalysis Data for Australian Fire Weather Conditions

Abstract Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century.

Sociality of future outcomes moderates the effects of warmth and competence on social optimism bias

People are overoptimistic about the future of those they like or admire (social optimism bias), expecting significantly more desirable than undesirable outcomes. By contrast, they are pessimistic about those they don’t like. To operationalize the (dis)like of social targets, warmth and competence are used as two universal dimensions of social perception. In this pre-registered study, we replicate previous findings while adding two new levels of complexity. First, we make the distinction between the sociality of future outcomes: “alone” outcomes (e.g., enjoying a quiet afternoon by oneself) and “social” outcomes (e.g., enjoying a vacation with the significant other). Second, we investigate the effect of attachment styles on one’s expectations for alone and social outcomes toward the social targets. In line with our hypotheses, the sociality of outcomes moderates both the additive and the multiplicative effects of the perceived warmth and competence of social targets on social optimism bias. Diverging from our hypotheses, we find that attachment anxiety and avoidance do not influence the effects of warmth and competence on social optimism bias. However, exploratory analyses reveal that attachment dimensions buffer the magnitude of social optimism bias for highly self-relevant social targets but do not impact social pessimism bias for irrelevant targets.

Understanding bias in probabilistic analysis in model-based health economic evaluation.

Guidelines of economic evaluations suggest that probabilistic analysis (using probability distributions as inputs) provides less biased estimates than deterministic analysis (using point estimates) owing to the non-linear relationship of model inputs and model outputs. However, other factors can also impact the magnitude of bias for model results. We evaluate bias in probabilistic analysis and deterministic analysis through three simulation studies. The simulation studies illustrate that in some cases, compared with deterministic analyses, probabilistic analyses may be associated with greater biases in model inputs (risk ratios and mean cost estimates using the smearing estimator), as well as model outputs (life-years in a Markov model). Point estimates often represent the most likely value of the parameter in the population, given the observed data. When model parameters have wide, asymmetric confidence intervals, model inputs with larger likelihoods (e.g., point estimates) may result in less bias in model outputs (e.g., costs and life-years) than inputs with lower likelihoods (e.g., probability distributions). Further, when the variance of a parameter is large, simulations from probabilistic analyses may yield extreme values that tend to bias the results of some non-linear models. Deterministic analysis can avoid extreme values that probabilistic analysis may encounter. We conclude that there is no definitive answer on which analytical approach (probabilistic or deterministic) is associated with a less-biased estimate in non-linear models. Health economists should consider the bias of probabilistic analysis and select the most suitable approach for their analyses.

Spontaneous Alpha-Band Oscillations Bias Subjective Contrast Perception.

Perceptual decisions depend both on the features of the incoming stimulus and on the ongoing brain activity at the moment the stimulus is received. Specifically, trial-to-trial fluctuations in cortical excitability have been linked to fluctuations in the amplitude of prestimulus α oscillations (∼8-13 Hz), which are in turn are associated with fluctuations in subjects' tendency to report the detection of a stimulus. It is currently unknown whether α oscillations bias postperceptual decision-making, or even bias subjective perception itself. To answer this question, we used a contrast discrimination task in which both male and female human subjects reported which of two gratings (one in each hemifield) was perceived as having a stronger contrast. Our EEG analysis showed that subjective contrast was reduced for the stimulus in the hemifield represented in the hemisphere with relatively stronger prestimulus α amplitude, reflecting reduced cortical excitability. Furthermore, the strength of this spontaneous hemispheric lateralization was strongly correlated with the magnitude of individual subjects' biases, suggesting that the spontaneous patterns of α lateralization play a role in explaining the intersubject variability in contrast perception. These results indicate that spontaneous fluctuations in cortical excitability, indicated by patterns of prestimulus α amplitude, affect perceptual decisions by altering the phenomenological perception of the visual world.SIGNIFICANCE STATEMENT Our moment-to-moment perception of the world is shaped by the features of the environment surrounding us, as much as by the constantly evolving states that characterize our brain activity. Previous research showed how the ongoing electrical activity of the brain can influence whether a stimulus has accessed conscious perception. However, evidence is currently missing on whether these electrical brain states can be associated to the subjective experience of a sensory input. Here we show that local changes in patterns of electrical brain activity preceding visual stimulation can bias our phenomenological perception. Importantly, we show that the strength of these variations can help explain the great interindividual variability in how we perceive the visual environment surrounding us.

Evaluating sources of bias in pedigree-based estimates of breeding population size.

Applications of genetic-based estimates of population size are expanding, especially for species for which traditional demographic estimation methods are intractable due to the rarity of adult encounters. Estimates of breeding population size (NS ) are particularly amenable to genetic-based approaches as the parameter can be estimated using pedigrees reconstructed from genetic data gathered from discrete juvenile cohorts, therefore eliminating the need to sample adults in the population. However, a critical evaluation of how genotyping and sampling effort influence bias in pedigree reconstruction, and how these biases subsequently influence estimates of NS , is needed to evaluate the efficacy of the approach under a range of scenarios. We simulated a model system to understand the interactive effects of genotyping and sampling effort on error in genetic pedigrees reconstructed from the program COLONY. We then evaluated how errors in pedigree reconstruction influenced bias and precision in estimates of NS using three different rarefaction estimators. Results indicated that pedigree error can be minimal when adequate genetic data are available, such as when juvenile sample sizes are large and/or individuals are genotyped at many informative loci. However, even in cases for which data are limited, using results of the simulation analysis to understand the magnitude and sources of bias in reconstructed pedigrees can still be informative when estimating NS . We applied results of the simulation analysis to evaluate S for a population of federally endangered Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the Delaware River, USA. Our results indicated that NS is likely to be three orders of magnitude lower compared with historic breeding population sizes, which is a considerable advancement in our understanding of the population status of Atlantic sturgeon in the Delaware River. Our analyses are broadly applicable in the design and interpretation of studies seeking to estimate NS and can help to guide conservation decisions when ecological uncertainty is high. The utility of these results is expected to grow as rapid advances in genetic technologies increase the popularity of genetic population monitoring and estimation.

Calendar effects on surface air temperature and precipitation based on model-ensemble equilibrium and transient simulations from PMIP4 and PACMEDY

Abstract. Numerical modeling enables a comprehensive understanding not only of the Earth's system today, but also of the past. To date, a significant amount of time and effort has been devoted to paleoclimate modeling and analysis, which involves the latest and most advanced Paleoclimate Modelling Intercomparison Project phase 4 (PMIP4). The definition of seasonality, which is influenced by slow variations in the Earth's orbital parameters, plays a key role in determining the calculated seasonal cycle of the climate. In contrast to the classical calendar used today, where the lengths of the months and seasons are fixed, the angular calendar calculates the lengths of the months and seasons according to a fixed number of degrees along the Earth's orbit. When comparing simulation results for different time intervals, it is essential to account for the angular calendar to ensure that the data for comparison are from the same position along the Earth's orbit. Most models use the classical calendar, which can lead to strong distortions of the monthly and seasonal values, especially for the climate of the past. Here, by analyzing daily outputs from multiple PMIP4 model simulations, we examine calendar effects on surface air temperature and precipitation under mid-Holocene, Last Interglacial, and pre-industrial climate conditions. We came to the following conclusions. (a) The largest cooling bias occurs in boreal autumn when the classical calendar is applied for the mid-Holocene and Last Interglacial, due to the fact that the vernal equinox is fixed on 21 March. (b) The sign of the temperature anomalies between the Last Interglacial and pre-industrial in boreal autumn can be reversed after the switch from the classical to angular calendar, particularly over the Northern Hemisphere continents. (c) Precipitation over West Africa is overestimated in boreal summer and underestimated in boreal autumn when the classical seasonal cycle is applied. (d) Finally, month-length adjusted values for surface air temperature and precipitation are very similar to the day-length adjusted values, and therefore correcting the calendar based on the monthly model results can largely reduce the artificial bias. In addition, we examine the calendar effects in three transient simulations for 6–0 ka by AWI-ESM, MPI-ESM, and IPSL-CM. We find significant discrepancies between adjusted and unadjusted temperature values over continents for both hemispheres in boreal autumn, while for other seasons the deviations are relatively small. A drying bias can be found in the summer monsoon precipitation in Africa (in the classical calendar), whereby the magnitude of bias becomes smaller over time. Overall, our study underlines the importance of the application of calendar transformation in the analysis of climate simulations. Neglecting the calendar effects could lead to a profound artificial distortion of the calculated seasonal cycle of surface air temperature and precipitation.

Revisiting the primary bias: the role of innumeracy in the misperception of prevalence of chronic illnesses

Although people have been repeatedly found to underestimate frequencies of common illnesses and overestimate those of rare illnesses, not much is known about this consistent bias in risk perception, termed “primary bias” in the literature, as well as the origin of its variations among different subpopulations. To fill this gap, we conducted a national survey among a representative sample of the French population (n = 3,245) to compare the perceived and actual prevalence of chronic illnesses, and to test the hypothesis that numeracy may play an important role in the accuracy of judgments of risk frequencies. The participants were asked to complete a 10-items numeracy scale and to estimate the prevalence of a variety of social conditions and chronic illnesses such as cancers or heart diseases by using a percentage scale. The analyses show that (1) participants tend to greatly overestimate the prevalence of conditions affecting small percentages of people, and underestimate those affecting a large percentage of them, (2) the Tversky and Kahneman’s probability weighting function provides an adequate model to represent the discrepancy between the perceived and actual prevalence of these illnesses or occupations, and (3) the magnitude of the primary bias varies principally as a function of the respondents’ numeracy. These results suggest that the primary bias that affects perceptions of prevalence of chronic diseases is not fundamentally different from those characterizing other types of probabilistic judgments. They also confirm that numeracy plays a considerable role in people’s ability to transform epidemiological observations from their social environment into more accurate estimates.

Corrective Adjustment Procedures as a strategy to remove Relative Age Effects: Validation across male and female age-group long jumping

ObjectivesTo validate the application of Corrective Adjustment Procedures for removing Relative Age Effects in youth athletic contexts, this study estimated the longitudinal relationships between decimal age (chronological and relative) and performance in male and female long jumpers. Using trendlines, Corrective Adjustment Procedures were applied, and Relative Age Effect distributions associated with performance attainment were re-examined. DesignRetrospective longitudinal design examining competitive long-jump data between 2005 and 2019. MethodsIn Part I, participants were 689 junior Italian long jumpers (age range = 11.01–17.99 years; 56.6% females) who participated in ≥three events. Longitudinal modelling and regression equations quantified the sex-specific relationships between decimal age and long jump performance. In Part II, equations were utilised to adjust individual performance within an independent sample (N = 13,639; 50.1% females) of age-matched jumpers. Relative Age Effect distributions within attainment levels (i.e., Top 25–10%) were examined based on raw and correctively adjusted performance. ResultsIrrespective of sex, Relative Age Effects were prevalent across all age-groups with medium-large effect sizes at 12–17 (males and females) and 12–17 years (females) of age (Cramer's V range = 0.07–0.28). Relative Age Effect bias magnitude also increased with attainment level (i.e., Top 25–10%). Following Corrective Adjustment Procedure application, typical Relative Age Effects were removed with non-significant deviations in relative age distributions regardless of sex or age-group or attainment level (Top 25 or 10%). ConclusionsFindings provide efficacy for Corrective Adjustment Procedure application to remove Relative Age Effects across youth long jumping events. Corrective Adjustment Procedures suggest potential in improving performance evaluation, identification of technically skilled performers, and sporting experiences.

Vertical Atmospheric Structures Associated with Positive Biases in COSMIC-2 Refractivity Retrievals

Abstract Representation of complex vertical structures observed in the troposphere can vary depending on data sources. The radio occultation (RO) technique offers great advantages for sensing the atmosphere down to its lowermost layers using high-resolution measurements collected by satellites on low-Earth orbit (LEO). The structures are generally smoother in vertical when reproduced from atmospheric models. We evaluate the quality of troposphere retrievals from the COSMIC-2 mission and demonstrate that systematic effects in fractional refractivity deviations with respect to European Centre for Medium-Range Weather Forecasts (ECMWF) background fields are spatially correlated with positive refractivity gradients characterized as subrefraction. The magnitude of refractivity biases observed mostly over the equatorial regions can exceed 1% within altitudes of 3–5 km. Respective zonal means reveal seasonal trends linked with the distribution of atmospheric inversion layers and signal-to-noise ratio values in RO data. The positive biases are vertically collocated with significant refractivity gradients in COSMIC-2 retrievals that are not reflected in the corresponding ECMWF profiles. The analysis of gradients based on COSMIC-2 data, further supported by radiosonde observations, suggests that most of subrefractions is identified in the middle troposphere at around 4 km. While the altitudes of maximum refractivity gradients from COSMIC-2 and ECMWF data are in fairly good agreement, the magnitude of ECMWF gradients is significantly smaller and rarely exceeds positive values.

A robust Bayesian bias-adjusted random effects model forconsideration of uncertainty about bias terms in evidence synthesis.

Meta‐analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases (ie, low, unclear, and high risk of bias) in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias‐adjusted random effects model to an already published meta‐analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews.