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OBJECTIVES: Quantitative bias analysis (QBA) methods evaluate the impact of biases arising from systematic errors on observational study results. This systematic review aimed to summarize the range and characteristics of QBA methods for summary-level data published in the peer-reviewed literature. STUDY DESIGN AND SETTING: We searched MEDLINE, Embase, Scopus, and Web of Science for English-language articles describing QBA methods. For each QBA method, we recorded key characteristics, including applicable study designs, bias(es) addressed; bias parameters, and publicly available software. The study protocol was preregistered on the Open Science Framework (https://osf.io/ue6vm/). RESULTS: Our search identified 10,249 records, of which 53 were articles describing 57 QBA methods for summary-level data. Of the 57 QBA methods, 53 (93%) were explicitly designed for observational studies, and 4 (7%) for meta-analyses. There were 29 (51%) QBA methods that addressed unmeasured confounding, 19 (33%) misclassification bias, 6 (11%) selection bias, and 3 (5%) multiple biases. Thirty-eight (67%) QBA methods were designed to generate bias-adjusted effect estimates and 18 (32%) were designed to describe how bias could explain away observed findings. Twenty-two (39%) articles provided code or online tools to implement the QBA methods. CONCLUSION: In this systematic review, we identified a total of 57 QBA methods for summary-level epidemiologic data published in the peer-reviewed literature. Future investigators can use this systematic review to identify different QBA methods for summary-level epidemiologic data. PLAIN LANGUAGE SUMMARY: Quantitative bias analysis (QBA) methods can be used to evaluate the impact of biases on observational study results. However, little is known about the full range and characteristics of available methods in the peer-reviewed literature that can be used to conduct QBA using information reported in manuscripts and other publicly available sources without requiring the raw data from a study. In this systematic review, we identified 57 QBA methods for summary-level data from observational studies. Overall, there were 29 methods that addressed unmeasured confounding, 19 that addressed misclassification bias, six that addressed selection bias, and three that addressed multiple biases. This systematic review may help future investigators identify different QBA methods for summary-level data.

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OBJECTIVES: Registration in the Dutch national COVID-19 vaccination register requires consent from the vaccinee. This causes misclassification of nonconsenting vaccinated persons as being unvaccinated. We quantified and corrected the resulting information bias in vaccine effectiveness (VE) estimates. STUDY DESIGN AND SETTING: National data were used for the period dominated by the SARS-CoV-2 Delta variant (July 11 to November 15, 2021). VE ((1-relative risk)∗100%) against COVID-19 hospitalization and intensive care unit (ICU) admission was estimated for individuals 12 to 49, 50 to 69, and ≥70 years of age using negative binomial regression. Anonymous data on vaccinations administered by the Municipal Health Services were used to determine informed consent percentages and estimate corrected VEs by iteratively imputing corrected vaccination status. Absolute bias was calculated as the absolute change in VE; relative bias as uncorrected/corrected relative risk. RESULTS: A total of 8804 COVID-19 hospitalizations and 1692 COVID-19 ICU admissions were observed. The bias was largest in the 70+ age group where the nonconsent proportion was 7.0% and observed vaccination coverage was 87%: VE of primary vaccination against hospitalization changed from 75.5% (95% CI 73.5-77.4) before to 85.9% (95% CI 84.7-87.1) after correction (absolute bias -10.4 percentage point, relative bias 1.74). VE against ICU admission in this group was 88.7% (95% CI 86.2-90.8) before and 93.7% (95% CI 92.2-94.9) after correction (absolute bias -5.0 percentage point, relative bias 1.79). CONCLUSION: VE estimates can be substantially biased with modest nonconsent percentages for vaccination data registration. Data on covariate-specific nonconsent percentages should be available to correct this bias.

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Purpose: To determine the contribution of recall bias to the observed excess in mental ill-health in those reporting harassment at work. Methods: A prospective cohort of 1885 workers in welding and electrical trades was contacted every six months for up to 5 years, asking whether they were currently anxious or depressed and whether this was made worse by work. Only at the end of the study did we ask about any workplace harassment they had experienced at work. We elicited sensitivity and specificity of self-reported bullying from published reliability studies and formulated priors that reflect the possibility of over-reporting of workplace harassment (exposure) by those whose anxiety or depression was reported to be made worse by work (cases). We applied the resulting misclassification models to probabilistic bias analysis (PBA) of relative risks. Results: We observe that PBA implies that it is unlikely that biased misclassification due to the study subjects' states of mind could have caused the entire observed association. Indeed, the results demonstrated that doubling of risk of anxiety or depression following workplace harassment is plausible, with the unadjusted relative risk attenuated with understated uncertainty. Conclusions: It seems unlikely that risk of anxiety or depression following workplace harassment can be explained by the form of recall bias that we proposed.

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Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. This paper aims to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy; gold/reference standard; study size; prioritizing accuracy measures; algorithm portability; and implication for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome or covariate). Validation work should be part of routine maintenance of RWD sources.

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In a variety of laboratory preparations, several animal species prefer signaled over unsignaled outcomes. Here we examine whether pigeons prefer options that signal the delay to reward over options that do not and how this preference changes with the ratio of the delays. We offered pigeons repeated choices between two alternatives leading to a short or a long delay to reward. For one alternative (informative), the short and long delays were reliably signaled by different stimuli (e.g., SS for short delays, SL for long delays). For the other (non-informative), the delays were not reliably signaled by the stimuli presented (S1 and S2). Across conditions, we varied the durations of the short and long delays, hence their ratio, while keeping the average delay to reward constant. Pigeons preferred the informative over the non-informative option and this preference became stronger as the ratio of the long to the short delay increased. A modified version of the Δ-Σ hypothesis (González et al., J Exp Anal Behav 113(3):591-608. https://doi.org/10.1002/jeab.595 , 2020a) incorporating a contrast-like process between the immediacies to reward signaled by each stimulus accounted well for our findings. Functionally, we argue that a preference for signaled delays hinges on the potential instrumental advantage typically conveyed by information.

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BACKGROUND: Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel, alternative approach to multiple-bias adjustment involves the simultaneous adjustment of all biases via imputation and/or regression weighting. The imputed value or weight corresponds to the probability of the missing data and serves to 'reconstruct' the unbiased data that would be observed based on the provided assumptions of the degree of bias. METHODS: We motivate and describe the steps necessary to implement this method. We also demonstrate the validity of this method through a simulation study with an exposure-outcome relationship that is biased by uncontrolled confounding, exposure misclassification, and selection bias. RESULTS: The study revealed that a non-biased effect estimate can be obtained when correct bias parameters are applied. It also found that incorrect specification of every bias parameter by +/-25% still produced an effect estimate with less bias than the observed, biased effect. CONCLUSIONS: Simultaneous multi-bias analysis is a useful way of investigating and understanding how multiple sources of bias may affect naive effect estimates. This new method can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies.

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This paper investigates three controversies involving potential causes and consequences of information bias in case and death definitions during the coronavirus disease (COVID-19) pandemic. First, evidence suggests China's surveillance data were biased and misinterpreted by the World Health Organization (WHO), prompting the WHO to advise nations to copy China's lockdowns. China appeared to use narrow diagnostic definitions that undercounted cases and deaths. Second, novel genomic data disseminated during the pandemic without adequate guidance from rigorous epidemiologic studies biased infection control policies in many countries. A novel genomic sequence of a virus is insufficient to declare new cases of a novel disease. Third, media reports of COVID-19 surveillance data in many nations appeared to be biased. Broadened surveillance definitions captured additional information, but unadjusted surveillance data disseminated to the public are not true cases and deaths. Recommendations include clarification of the proper use of diagnostic and surveillance case and death definitions to avoid information bias.

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Background: Identifying high data-continuity patients in an electronic health record (EHR) system may facilitate selecting cohorts with a lower degree of variable misclassification and promote study validity. We updated a previously developed algorithm for identifying patients with high EHR data-completeness by adding demographic and health utilization factors to improve adaptability to networks serving patients of diverse backgrounds. We also expanded the algorithm to accommodate data in the ICD-10 era. Methods: We used Medicare claims linked with EHR data to identify individuals aged ≥65 years. EHR-continuity was defined as the proportion of encounters captured in EHR data relative to claims. We compared the model with additional demographic factors and their interaction terms with other predictors with the original algorithm and assessed the performance by area under the ROC curve (AUC) and net reclassification index (NRI). Results: The study cohort consisted of 264,099 subjects. The updated prediction model had an AUC of 0.93 in the validation set. Compared to the previous model, the new model had an NRI of 37.4% (p<0.001) for EHR-continuity classification. Interaction terms between demographic variables and other predictors did not improve the performance. Patients within the top 20% of predicted EHR-continuity had four times less misclassification of key variables compared to the remaining population. Conclusion: Adding demographic and healthcare utilization variables significantly improved the model performance. Patients with high predicted EHR-continuity had less misclassification of study variables compared to the remaining population in both ICD-9 and 10 eras.

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INTRODUCTION: Electronic health record (EHR) discontinuity (missing out-of-network encounters) can lead to information bias. We sought to construct an algorithm that identifies high EHR-continuity among oncology patients. METHODS: Using a linked Medicare-EHR database and regression, we sought to 1) measure how often Medicare claims for outpatient encounters were substantiated by visits recorded in the EHR, and 2) predict continuity ratio, defined as the yearly proportion of outpatient encounters reported to Medicare that were captured by EHR data. The prediction model...s performance was evaluated with the coefficient of determination and Spearman...s correlation. We quantified variable misclassification by decile of continuity ratio using standardized difference and sensitivity. RESULTS: A total of 79,678 subjects met all eligibility criteria. Predicted and observed continuity was highly correlated (σSpearman=0.86). On average across all variables measured, MSD was reduced by a factor of 1/7th and sensitivity was improved 35-fold comparing subjects in the highest vs. lowest decile of CR. CONCLUSION: In the oncology population, restricting EHR-based study cohorts to subjects with high continuity may reduce misclassification without greatly impacting representativeness. Further work is needed to elucidate the best manner of implementing continuity prediction rules in cohort studies.

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Active comparator studies are increasingly common, particularly in pharmacoepidemiology. In such studies, the parameter of interest is a contrast (difference or ratio) in the outcome risks between the treatment of interest and the selected active comparator. While it may appear treatment is dichotomous, treatment is actually polytomous as there are at least 3 levels: no treatment, the treatment of interest, and the active comparator. Because misclassification may occur between any of these groups, independent nondifferential treatment misclassification may not be toward the null (as expected with a dichotomous treatment). In this work, we describe bias from independent nondifferential treatment misclassification in active comparator studies with a focus on misclassification that occurs between each active treatment and no treatment. We derive equations for bias in the estimated outcome risks, risk difference, and risk ratio, and we provide bias correction equations that produce unbiased estimates, in expectation. Using data obtained from US insurance claims data, we present a hypothetical comparative safety study of antibiotic treatment to illustrate factors that influence bias and provide an example probabilistic bias analysis using our derived bias correction equations.

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BACKGROUND AND AIMS: The definition of the metabolic syndrome consists of five components. The underlying measurements are subject to intra-individual variability. This repeated measurements study investigated the impact of intra-individual measurement variability on the stability of the diagnosis of metabolic syndrome over 12 months. METHODS AND RESULTS: Twenty-five employees of the University Medicine Greifswald aged 22-70 years were examined once a month over one year. Examinations included blood sampling and anthropometric and blood pressure measurements. Laboratory measurements included glucose, cholesterol (high-density lipoprotein [HDL], and low-density lipoprotein [LDL]), and triglycerides. The metabolic syndrome was defined according to the International Diabetes Federation modified for non-fasting blood samples. Variations in continuous metabolic markers were assessed using coefficients of variation (CV) and intra-class correlation coefficients (ICC). Overall eight participants (32%) were categorized at least once within 12 months as having a metabolic syndrome; in none of those metabolic syndrome was found consistently over the study follow-ups. The Cohen's Kappa for metabolic syndrome was 0.57. CV was highest for triglycerides (27.5%) followed by glucose (10.1%), LDL- (9.5%), and HDL-cholesterol (8.6%). ICC's were lowest for glucose (0.51), triglycerides (0.65), systolic (0.68), and diastolic blood pressure (0.69). CONCLUSION: We showed that the measurement of biomarkers defining the metabolic syndrome is a time-varying condition with implications for the concept of the metabolic syndrome. To account for this uncertainty in prevalence studies we propose to identify uncertain cases according to the current definition of the metabolic syndrome. For analysing associations we recommend to apply probabilistic sensitivity analyses.

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Measurement error is pervasive in epidemiologic research. Epidemiologists often assume that mismeasurement of study variables is nondifferential with respect to other analytical variables and then rely on the heuristic that "nondifferential misclassification will bias estimates towards the null." However, there are many exceptions to the heuristic for which bias towards the null cannot be assumed. In this paper, we compile and characterize 7 exceptions to this rule and encourage analysts to take a more critical and nuanced approach to evaluating and discussing bias from nondifferential mismeasurement.

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BACKGROUND: Smoking intensity, which is generally based on self-reported average cigarettes per day (CPD), is a major behavioural risk factor and strongly related to socioeconomic status (SES). To assess the validity of the CPD measure, correlations with objective markers of tobacco smoke exposure - such as urinary nicotine metabolites - were examined. Yet, it remains unclear, whether this correlation is affected by SES, which may indicate imprecise or biased self-reports of smoking intensity. METHODS: We investigated the role of SES in the association between CPD and nicotine metabolites in current smokers among the participants of the population-based, prospective Heinz Nixdorf Recall Study. We determined urinary cotinine and additionally trans-3'-hydroxy-cotinine. SES was assessed by the International Socio-Economic Index of occupational status, and education. We calculated correlations (Pearson's r) between logarithmised CPD and cotinine in subgroups of SES and analysed SES and further predictors of cotinine in multiple linear regression models separately by gender. RESULTS: Median reported smoking intensity was 20 CPD in male and 19 CPD in female smokers. Men showed higher cotinine concentrations (median 3652 µg/L, interquartile range (IQR) 2279-5422 µg/L) than women (3127 µg/L, IQR 1692-4920 µg/L). Logarithmised CPD correlated moderately with cotinine in both, men and women (Pearson's r 0.4), but correlations were weaker in smokers with lower SES: Pearson's r for low, intermediate, and high occupational SES was 0.35, 0.39, and 0.48 in men, and 0.28, 0.43, and 0.47 in women, respectively. Logarithmised CPD and urinary creatinine were main predictors of cotinine in multiple regression models, whereas SES showed a weak negative association in women. Results were similar for trans-3'-hydroxy-cotinine. CONCLUSIONS: Decreasing precision of self-reported CPD was indicated for low SES in men and women. We found no strong evidence for biased self-reports of smoking intensity by SES.

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Data derived from electronic health records (EHR) are heterogeneous with availability of specific measures dependent on the type and timing of patients' healthcare interactions. This creates a challenge for research using EHR-derived exposures because gold-standard exposure data, determined by a definitive assessment, may only be available for a subset of the population. Alternative approaches to exposure ascertainment in this case include restricting the analytic sample to only those patients with gold-standard exposure data available (exclusion); using gold-standard data, when available, and using a proxy exposure measure when the gold standard is unavailable (best available); or using a proxy exposure measure for everyone (common data). Exclusion may induce selection bias in outcome/exposure association estimates, while incorporating information from a proxy exposure via either the best available or common data approaches may result in information bias due to measurement error. The objective of this paper was to explore the bias and efficiency of these three analytic approaches across a broad range of scenarios motivated by a study of the association between chronic hyperglycemia and five-year mortality in an EHR-derived cohort of colon cancer survivors. We found that the best available approach tended to mitigate inefficiency and selection bias resulting from exclusion while suffering from less information bias than the common data approach. However, bias in all three approaches can be severe, particularly when both selection bias and information bias are present. When risk of either of these biases is judged to be more than moderate, EHR-based analyses may lead to erroneous conclusions.

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PURPOSE: The application of simulated data in epidemiological studies enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. This was a review of the application of simulation methods to the quantification of bias in reproductive and perinatal epidemiology and an assessment of value gained. METHODS: A search of published studies available in English was conducted in August 2020 using PubMed, Medline, Embase, CINAHL, and Scopus. A gray literature search of Google and Google Scholar, and a hand search using the reference lists of included studies was undertaken. RESULTS: Thirty-nine papers were included in this study, covering information (n = 14), selection (n = 14), confounding (n = 9), protection (n = 1), and attenuation bias (n = 1). The methods of simulating data and reporting of results varied, with more recent studies including causal diagrams. Few studies included code for replication. CONCLUSIONS: Although there has been an increasing application of simulation in reproductive and perinatal epidemiology since 2015, overall this remains an underexplored area. Further efforts are required to increase knowledge of how the application of simulation can quantify the influence of bias, including improved design, analysis and reporting. This will improve causal interpretation in reproductive and perinatal studies.

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Payments for watershed ecosystem services are the most important forms of global water environmental protection. Transboundary basin ecological compensation policies in China are mainly based on the central government's appropriation to local governments or transfer payments between local governments. However, watershed ecosystem services face many problems such as the lack of interprovincial horizontal compensation policies and insufficient public participation. Most of China's rivers are distributed in vast rural areas, and the livelihoods of farmers living in these areas are highly dependent on the water environment. Since a watershed usually spans multiple administrative regions, the inconsistency between the natural and administrative boundaries of the river affects the completeness of the ecosystem services' information exchange between the service providers and payers. To promote interprovincial government water management cooperation and spark the farmers' enthusiasm for participating in the payments for watershed ecosystem services, this study examines the mechanism by which social interactions can affect farmers' willingness to pay (WTP) by mitigating the information bias. The results show that information bias plays a mediating role in the effect of social interactions on WTP. Additionally, the cadres/associations' and village-level interactions can effectively reduce the information bias of farmers, thereby increasing their WTP for transboundary basin ecosystem services. Moreover, the intensity of the psychological ownership of the watershed and government credibility have a significant moderating effect on the above-mentioned mechanisms. This study suggests that it is necessary to broaden the source channels of farmers' information on upstream ecological governance, improve the completeness of farmers' information, and curb the negative impact of information bias on WTP. Simultaneously, it is necessary to improve the government credibility and cultivate the farmers' sense of belonging and responsibility toward the watershed.

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INTRODUCTION: Quantitative patient-reported outcome (PRO) measures ideally are analyzed on their original scales and responder analyses are used to aid the interpretation of those primary analyses. As stated in the FDA PRO Guidance for Medical Product Development (2009), one way to lend meaning and interpretation to such a PRO measure is to dichotomize between values where within-patient changes are considered clinically important and those that are not. But even a PRO scale with a cutoff score that discriminates well between responder and non-responders is fraught with some misclassification. METHODS: Using estimates of sensitivity and specificity on classification of responder status from a PRO instrument, formulas are provided to correct for such responder misclassification under the assumption of no treatment misclassification. Two case studies from sexual medicine illustrate the methodology. RESULTS: Adjustment formulas on cell counts for responder misclassification are a direct extension of correction formulas for misclassification on disease from a two-way cross-classification table of disease (yes, no) and exposure (yes, no). Unadjusted and adjusted estimates of treatment effect are compared in terms of odds ratio, response ratio, and response difference. In the two case studies, there was considerable underestimation of treatment effect. DISCUSSION AND CONCLUSIONS: The methodology can be applied to different therapeutic areas. Limitations of the methodology, such as when adjusted cell estimates become negative, are highlighted. The role of anchor-based methodology is discussed for obtaining estimates of sensitivity and specificity on responder classification. Correction for treatment effect bias from misclassification of responder status on PRO measures can lead to more trustworthy interpretation and effective decision-making. Clinicaltrials.gov: NCT00343200.

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Clinical epidemiological research entails assessing the burden and etiology of disease, the diagnosis and prognosis of disease, the efficacy of preventive measures or treatments, the analysis of the risks and benefits of diagnostic and therapeutic maneuvers, and the evaluation of health care services. In all areas, the main focus is to describe the relationship between exposure and outcome and to determine one of the following: prevalence, incidence, cause, prognosis, or effect of treatment. The accuracy of these conclusions is determined by the validity of the study. Validity is determined by addressing potential biases and possible confounders that may be responsible for the observed association. Therefore, it is important to understand the types of bias that exist and also to be able to assess their impact on the magnitude and direction of the observed effect. The following chapter reviews the epidemiological concepts of selection bias, information bias, intervention bias, and confounding and discusses ways in which these sources of bias can be minimized.

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PURPOSE: Strategies to identify and validate acute myocardial infarction (AMI) and stroke in primary-care electronic records may impact effect measures, but to an unknown extent. Additionally, the validity of cardiovascular risk factors that could act as confounders in studies on those endpoints has not been thoroughly assessed in the United Kingdom Clinical Practice Research Datalink's (CPRD's) GOLD database. We explored the validity of algorithms to identify cardiovascular outcomes and risk factors and evaluated different outcome-identification strategies using these algorithms for estimation of adjusted incidence rate ratios (IRRs). METHODS: First, we identified AMI, stroke, smoking, obesity, and menopausal status in a cohort treated for overactive bladder by applying computerized algorithms to primary care medical records (2004-2012). We validated these cardiovascular outcomes and risk factors with physician questionnaires (gold standard for this analysis). Second, we estimated IRRs for AMI and stroke using algorithm-identified and questionnaire-confirmed cases, comparing these with IRRs from cases identified through linkage with hospitalization/mortality data (best estimate). RESULTS: For AMI, the algorithm's positive predictive value (PPV) was >90%. Initial algorithms for stroke performed less well because of inclusion of codes for prevalent stroke; algorithm refinement increased PPV to 80% but decreased sensitivity by 20%. Algorithms for smoking and obesity were considered valid. IRRs based on questionnaire-confirmed cases only were closer to IRRs estimated from hospitalization/mortality data than IRRs from algorithm-identified cases. CONCLUSIONS: AMI, stroke, smoking, obesity, and postmenopausal status can be accurately identified in CPRD. Physician questionnaire-validated AMI and stroke cases yield IRRs closest to the best estimate.

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Information bias is common in epidemiology and can substantially diminish the validity of study results. Validation studies, in which an investigator compares the accuracy of a measure with a gold standard measure, are an important way to understand and mitigate this bias. More attention is being paid to the importance of validation studies in recent years, yet they remain rare in epidemiologic research and, in our experience, they remain poorly understood. Many epidemiologists have not had any experience with validations studies, either in the classroom or in their work. We present an example of misclassification of a dichotomous exposure to elucidate some important misunderstandings about how to conduct validation studies to generate valid information. We demonstrate that careful attention to the design of validation studies is central to determining how the bias parameters (e.g. sensitivity and specificity or positive and negative predictive values) can be used in quantitative bias analyses to appropriately correct for misclassification. Whether sampling is done based on the true gold standard measure, the misclassified measure or at random will determine which parameters are valid and the precision of those estimates. Whether or not the validation is done stratified by other key variables (e.g. by the exposure) will also determine the validity of those estimates. We also present sample questions that can be used to teach these concepts. Increasing the presence of validation studies in the classroom could have a positive impact on their use and improve the validity of estimates of effect in epidemiologic research.

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