Search

Background Published analyses of prostate cancer nested case–control and survival data in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study cohort suggested that men with higher baseline vitamin D [25(OH)D] concentrations have both (i) increased prostate cancer risk and (ii) decreased prostate cancer-specific fatality. Methods To investigate possible factors responsible for a spurious association with prostate cancer fatality, we reanalysed baseline serum vitamin D associations with prostate cancer risk and prostate cancer-specific fatality in case–control data nested within the ATBC Study (1000 controls and 1000 incident prostate cancer cases). Conditional logistic regression and Cox proportion hazard models were used, respectively, to estimate odds ratios for risk and hazard ratios for prostate cancer-specific fatality, overall and by disease aggressiveness. We replicated these case–control analyses using baseline serum measurements of alpha-tocopherol (vitamin E), beta-carotene and retinol (vitamin A), and used the entire ATBC Study cohort (⁠ n  = 29 085) to estimate marginal associations between these baseline vitamins and prostate cancer incidence and fatality following blood collection. Results Vitamin D analyses agreed closely with those originally published, with opposite risk and fatality associations. By contrast, the analyses of alpha-tocopherol, beta-carotene and retinol yielded concordant associations for prostate cancer incidence and prostate cancer-specific fatality. Conclusions We found evidence of neither artefacts in the nested prostate cancer case–control data set nor detection or collider biases in the fatality analyses. The present findings therefore support a valid inverse (i.e. beneficial) association between vitamin D and prostate cancer-specific survival that warrants further evaluation, including possibly in controlled trials. [ABSTRACT FROM AUTHOR]

Abstract Background Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes. Methods The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 – a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19. Results Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity – differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis. Conclusions In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic.

Objectives: We provide a brief overview of collider bias and its implications for criminological research. Methods: Owing to the nature of the topics studied, as well as the common data sources used to carry out much of this research, work in the field may often become vulnerable to a specific methodological problem known as collider bias. Collider bias occurs when exposure variables and outcomes independently cause a third variable, and this variable is included in statistical models. Colliders represent somewhat of a paradox in that there is scholarship discussing the issue, yet it has managed to remain a relatively cryptic threat compared to other sources of bias. Results: We argue that, far from being an obscure concern, colliders almost certainly have pervasive impact in criminal justice and criminology. Conclusion: We close by offering a general set of strategies for addressing the challenges posed by collider bias. While there is no panacea, there are better practices, many of which are underutilized in the disciplines that study crime and its attendant topics. [ABSTRACT FROM AUTHOR]

Background: Statistical methods are essential in epidemiology research, but they can generate erroneous estimates when selecting variables based only on statistical criteria. The use of directed acyclic graphs (DAG) helps to understand the causal relationships between variables and to avoid biases. Objective: Compare the estimate of the effect of skin color on heart attack obtained from three data analysis techniques: a stepwise approach based on statistical criteria, a propensity score technique, and a graphical approach based on causal criteria. Methods: Population-based cross-sectional study using data from the second National Health and Nutrition Examination Survey (NHANES). The exposure variable was skin color (black or non-black) and the outcome was heart attack (yes or no). Multivariable logistic regressions were carried out using the stepwise, propensity score techniques and the DAG-based approach to identify the association between the variables. In the stepwise technique, all variables potentially related to the outcome were included in the model and a forward or backward algorithm was used. The propensity score was applied, estimating the probability of exposure based on the covariates and helping to create balanced groups for comparison. Different possible causal models were developed between the variables in the DAG-based approach, identifying confounding, mediation, and collision factors. The models were created considering self-rated health as a confounding or collider variable, and the modeling results were verified. Results: A total of 10,351 adults were evaluated, the majority female (52.1%), aged 20 to 39 years (48.5%), and with non-black skin color (90.4%). The prevalence of heart attacks was 3.0%, and 17% rated their health as fair or poor. Using different modeling techniques, no association was found between skin color and heart attack (p > 0.05), except when self-rated health, a collider variable, was included in the stepwise models. In this case, there was an inverse and biased association between the two variables, indicating a collision bias (stepwise-backward-OR 0.48; 95%CI 0.33–0.70; stepwise-forward-OR 0.64; 95%CI 0.44–0.94). Conclusion: Skin color was not associated with heart attack when controlling for appropriate confounding factors. However, when adjusting for self-rated health in stepwise techniques, a colliding variable, there was an inverse and distorted association between the two variables, indicating a collider bias. The DAG-based approach and propensity score can avoid this bias by correctly identifying confounding factors and colliders. [ABSTRACT FROM AUTHOR]

Background: Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes. Methods: The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 – a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19. Results: Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity – differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis. Conclusions: In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04–1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36–1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B−AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci. This study demonstrated the bidirectional relationship between type 2 diabetes and heart failure, addressed the impact of collider bias on the genome-wide association study of diabetes-related heart failure, and identified and replicated two genetic loci. These discoveries offer pivotal insights into the intricate mechanisms and comorbidities associated with heart failure. [ABSTRACT FROM AUTHOR]

AbstractThis article introduces a collection of four datasets, similar to Anscombe’s quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. The article includes a mathematical summary of each dataset, as well as directed acyclic graphs that depict the relationships between the variables. Despite the fact that the statistical summaries and visualizations for each dataset are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example datasets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone. The article also includes R code for reproducing all figures and provides access to the datasets themselves through an R package named “quartets.” Supplementary materials for this article are available online.

Background Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. Methods Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. Results Volunteer bias in all associations, as naively estimated in UKB, was substantial—in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. Conclusions Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available. [ABSTRACT FROM AUTHOR]

This article introduces a collection of four datasets, similar to Anscombe's quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. The article includes a mathematical summary of each dataset, as well as directed acyclic graphs that depict the relationships between the variables. Despite the fact that the statistical summaries and visualizations for each dataset are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example datasets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone. The article also includes R code for reproducing all figures and provides access to the datasets themselves through an R package named "quartets." for this article are available online. [ABSTRACT FROM AUTHOR]

This article proposes to use copulas to characterize the collider bias that concerns the non-substantive change in the causal dependence between variables before and after conditioning on their common effect (collider). This copula-based portrayal allows scholars (1) to capture the sophisticated (e.g., asymmetric or heavy-tail) causal dependence structure that is usually not evidenced by a summative causal effect estimate, such as the regression coefficient based on a well-matched sample; (2) to focus on the causal dependence structure that is insensitive to the influences from the marginal distributions; and (3) to directly and formally test the significance of change in the causal dependence structure using the Cramér–von Mises statistic. Both simulation and real data examples are presented, which suggest that copulas can be a handy tool for practical researchers to describe the collider bias. [ABSTRACT FROM AUTHOR]

We consider the problem of bias arising from conditioning on a post-outcome collider. We illustrate this with reference to Elwert and Winship (2014) but we go beyond their study to investigate the extent to which inverse probability weighting might offer solutions. We use linear models to derive expressions for the bias arising in different kinds of post-outcome confounding, and we show the specific situations in which inverse probability weighting will allow us to obtain estimates that are consistent or, if not consistent, less biased than those obtained via ordinary least squares regression. [ABSTRACT FROM AUTHOR]

Abstract Background Mortality data obtained from death certificates have been studied to explore causal associations between diseases. However, these analyses are subject to collider and reporting biases (selection and information biases, respectively). We aimed to assess to what extent associations of causes of death estimated from individual mortality data can be extrapolated as associations of disease states in the general population. Methods We used a multistate model to generate populations of individuals and simulate their health states up to death from national health statistics and artificially replicate collider bias. Associations between health states can then be estimated from such simulated deaths by logistic regression and the magnitude of collider bias assessed. Reporting bias can be approximated by comparing the estimates obtained from the observed death certificates (subject to collider and reporting biases) with those obtained from the simulated deaths (subject to collider bias only). As an illustrative example, we estimated the association between cancer and suicide in French death certificates and found that cancer was negatively associated with suicide. Collider bias, due to conditioning inclusion in the study population on death, increasingly downwarded the associations with cancer site lethality. Reporting bias was much stronger than collider bias and depended on the cancer site, but not prognosis. Results The magnitude of the biases ranged from 1.7 to 9.3 for collider bias, and from 4.7 to 64 for reporting bias. Conclusions These results argue for an assessment of the magnitude of both collider and reporting biases before performing analyses of cause of death associations exclusively from mortality data. If these biases cannot be corrected, results from these analyses should not be extrapolated to the general population.

Background: Mortality data obtained from death certificates have been studied to explore causal associations between diseases. However, these analyses are subject to collider and reporting biases (selection and information biases, respectively). We aimed to assess to what extent associations of causes of death estimated from individual mortality data can be extrapolated as associations of disease states in the general population. Methods: We used a multistate model to generate populations of individuals and simulate their health states up to death from national health statistics and artificially replicate collider bias. Associations between health states can then be estimated from such simulated deaths by logistic regression and the magnitude of collider bias assessed. Reporting bias can be approximated by comparing the estimates obtained from the observed death certificates (subject to collider and reporting biases) with those obtained from the simulated deaths (subject to collider bias only). As an illustrative example, we estimated the association between cancer and suicide in French death certificates and found that cancer was negatively associated with suicide. Collider bias, due to conditioning inclusion in the study population on death, increasingly downwarded the associations with cancer site lethality. Reporting bias was much stronger than collider bias and depended on the cancer site, but not prognosis. Results: The magnitude of the biases ranged from 1.7 to 9.3 for collider bias, and from 4.7 to 64 for reporting bias. Conclusions: These results argue for an assessment of the magnitude of both collider and reporting biases before performing analyses of cause of death associations exclusively from mortality data. If these biases cannot be corrected, results from these analyses should not be extrapolated to the general population. [ABSTRACT FROM AUTHOR]

In their seminal 2002 paper, "Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology," Hernán et al. (Am J Epidemiol. 2002;155(2):176–184) emphasized the importance of using theory rather than data to guide confounding control, focusing on colliders as variables that share characteristics with confounders but whose control may actually introduce bias into analyses. In this commentary, we propose that the importance of this paper stems from the connection the authors made between nonexchangeability as the ultimate source of bias and structural representations of bias using directed acyclic graphs. This provided both a unified approach to conceptualizing bias and a means of distinguishing between different sources of bias, particularly confounding and selection bias. Drawing on examples from the paper, we also highlight unresolved questions about the relationship between collider bias, selection bias, and generalizability and argue that causal knowledge is a prerequisite not only for identifying confounders but also for developing any hypothesis about potential sources of bias. [ABSTRACT FROM AUTHOR]

Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables assumptions more plausible, there is at the same time a growing risk that endogenous variables are included, which would lead to the violation of conditional independence. This article demonstrates that DML is very sensitive to the inclusion of only a few “bad controls” in the covariate space. The resulting bias varies with the nature of the theoretical causal model, which raises concerns about the feasibility of selecting control variables in a data-driven way.

It has been argued that including the lagged dependent variable in panel models will open up unintended back-door paths and bias the estimates of the causal variable. We show that panel analysis in the structural equation modeling framework can get around this issue. Including the lagged dependent variable has the benefit of closing back-door paths due to some unobserved time-varying con-founders. We demonstrate this by looking at simulated data and provide examples in the lavaan package for R as well as Mplus. [ABSTRACT FROM AUTHOR]

Female sex in patients with atrial fibrillation (AF) is a controversial and paradoxical risk factor for stroke—controversial because it increases the risk of stroke only among older women of some ethnicities and paradoxical because it appears to contradict male predominance in cardiovascular diseases. However, the underlying mechanism remains unclear. We conducted simulations to examine the hypothesis that this sex difference is generated non-causally through left truncation due to competing risks (CR) such as coronary artery diseases, which occur more frequently among men than among women and share common unobserved causes with stroke. We modeled the hazards of stroke and CR with correlated heterogeneous risk. We assumed that some people died of CR before AF diagnosis and calculated the hazard ratio of female sex in the left-truncated AF population. In this situation, female sex became a risk factor for stroke in the absence of causal roles. The hazard ratio was attenuated in young populations without left truncation and in populations with low CR and high stroke incidence, which is consistent with real-world observations. This study demonstrated that spurious risk factors can be identified through left truncation due to correlated CR. Female sex in patients with AF may be a paradoxical risk factor for stroke. [ABSTRACT FROM AUTHOR]

Objectives Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. Target audience Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. Scope We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided. [ABSTRACT FROM AUTHOR]

Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). In MI, in addition to those required for the substantive analysis, imputation models often include other variables (“auxiliary variables”). Auxiliary variables that predict the partially observed variables can reduce the standard error (SE) of the MI estimator and, if they also predict the probability that data are missing, reduce bias due to data being missing not at random. However, guidance for choosing auxiliary variables is lacking. We examine the consequences of a poorly chosen auxiliary variable: if it shares a common cause with the partially observed variable and the probability that it is missing (i.e., it is a “collider”), its inclusion can induce bias in the MI estimator and may increase the SE. We quantify, both algebraically and by simulation, the magnitude of bias and SE when either the exposure or outcome is incomplete. When the substantive analysis outcome is partially observed, the bias can be substantial, relative to the magnitude of the exposure coefficient. In settings in which a complete records analysis is valid, the bias is smaller when the exposure is partially observed. However, bias can be larger if the outcome also causes missingness in the exposure. When using MI, it is important to examine, through a combination of data exploration and considering plausible casual diagrams and missingness mechanisms, whether potential auxiliary variables are colliders.

Ecologists often rely on randomized control trials (RCTs) to quantify causal relationships in nature. Many of our foundational insights of ecological phenomena can be traced back to well‐designed experiments, and RCTs continue to provide valuable insights today. Although RCTs are often regarded as the "gold standard" for causal inference, it is important to recognize that they too rely on a set of causal assumptions that must be justified and met by the researcher to draw valid causal conclusions. We use key ecological examples to show how biases such as confounding, overcontrol, and collider bias can occur in experimental setups. In tandem, we highlight how such biases can be removed through the application of the structural causal model (SCM) framework. The SCM framework visualizes the causal structure of a system or process under study using directed acyclic graphs (DAGs) and subsequently applies a set of graphical rules to remove bias from both observational and experimental data. We show how DAGs can be applied across ecological experimental studies to ensure proper study design and statistical analysis, leading to more accurate causal estimates drawn from experimental data. Although causal conclusions drawn from RCTs are often taken at face value, ecologists are increasingly becoming aware that experimental approaches must be carefully designed and analyzed to avoid potential biases. By applying DAGs as a visual and conceptual tool, experimental ecologists can increasingly meet the causal assumptions required for valid causal inference. [ABSTRACT FROM AUTHOR]

Occupancy models are a vital tool for ecologists studying the patterns and drivers of species occurrence, but their use often involves selecting among models with different sets of occupancy and detection covariates. The information‐theoretic approach, which employs information criteria such as Akaike's information criterion (AIC) is arguably the most popular approach for model selection in ecology and is often used for selecting occupancy models. However, the information‐theoretic approach risks selecting models that produce inaccurate parameter estimates due to a phenomenon called collider bias, a type of confounding that can arise when adding explanatory variables to a model. Using simulations, we investigated the consequences of collider bias (using an illustrative example called M‐bias) in the occupancy and detection processes of an occupancy model, and explored the implications for model selection using AIC and a common alternative, the Schwarz criterion (or Bayesian information criterion, BIC). We found that when M‐bias was present in the occupancy process, AIC and BIC selected models that inaccurately estimated the effect of the focal occupancy covariate, while simultaneously producing more accurate predictions of the site‐level occupancy probability than other models in the candidate set. In contrast, M‐bias in the detection process did not impact the focal estimate; all models made accurate inferences, while the site‐level predictions of the AIC/BIC‐best model were slightly more accurate. Our results show that information criteria can be used to select occupancy covariates if the sole purpose of the model is prediction, but must be treated with more caution if the purpose is to understand how environmental variables affect occupancy. By contrast, detection covariates can usually be selected using information criteria regardless of the model's purpose. These findings illustrate the importance of distinguishing between the tasks of parameter inference and prediction in ecological modeling. Furthermore, our results underline concerns about the use of information criteria to compare different biological hypotheses in observational studies. [ABSTRACT FROM AUTHOR]

Background: When quantifying differences in health outcomes between immigrants and non-immigrants, it is common practice to adjust for observed differences in outcome risk factors between the groups being compared. However, as some of these outcome risk factors may act as mediators on the causal path between the exposure and outcome, adjusting for these may remove effects of factors that characterize the immigrants rather than removing a bias between immigrants and non-immigrants. Methods: This study investigates the underlying conditions for which adjusting for outcome risk factors in regression models can lead to the estimation of either total or direct effect for the difference in health outcomes between immigrants and non-immigrants. For this investigation, we use modern tools in causal inference to construct causal models that we believe are highly relevant in an immigrant dataset. In these models, the outcome risk factor is modeled either as a mediator, a selection factor, or a combined mediator/selection factor. Unlike mediators, selection factors are variables that affect the probability of being in the immigrant dataset and may contribute to a bias when comparing immigrants and non-immigrants. Results: When the outcome risk factor acts both as a mediator and selection factor, the adjustment for the risk factor in regression models leads to the estimation of what is known as a "controlled" direct effect. When the outcome risk factor is either a selection factor or a mediator alone, the adjustment for the risk factor in regression models leads to the estimation of a total effect or a controlled direct effect, respectively. In all regression analyses, also adjusting for various confounding paths, including mediator-outcome confounding, may be necessary to obtain valid controlled direct effects or total effects. Conclusions: Depending on the causal role of the outcome risk factors in immigrant datasets, regression adjustment for these may result in the estimation of either total effects or controlled direct effects for the difference in outcomes between immigrants and non-immigrants. Because total and controlled direct effects are interpreted differently, we advise researchers to clarify to the readers which types of effects are presented when adjusting for outcome risk factors in immigrant datasets. [ABSTRACT FROM AUTHOR]

There is substantial evidence in support of an association between periodontitis and cardiovascular disease. The most important open question related to this association is causality. This article revisits the question of causality by reviewing intervention studies and systematic reviews and meta analyses published in the last 3 years. Where are we now in answering this question? Whilst systematic reviews and epidemiological studies continue to support an association between the diseases, intervention studies fall short in determining causality. There is a dearth of good-quality, blinded randomised control trials with cardiovascular disease outcomes. Most studies use surrogate markers/biomarkers for endpoints, and this is problematic as they may not be reflective of cardiovascular disease status. This review further highlights another issue with surrogate markers/biomarkers: the potential for collider bias. Ethical considerations surrounding nontreatment have led to calls for a well-annotated database containing in-depth dental health data. Finally, a relatively new and important risk factor for cardiovascular disease, clonal haematopoiesis of indeterminate potential, is discussed. Clonal haematopoiesis of indeterminate potential increases cardiovascular risk by more than 40%, and inflammation is a contributing factor. The impact of periodontal disease on this emerging risk factor has yet to be explored. Although the question of causality in the association between periodontal disease and cardiovascular disease remains unanswered, the importance of good oral health in maintaining good heart health is reiterated.

Abstract Ecologists often rely on randomized control trials (RCTs) to quantify causal relationships in nature. Many of our foundational insights of ecological phenomena can be traced back to well‐designed experiments, and RCTs continue to provide valuable insights today. Although RCTs are often regarded as the “gold standard” for causal inference, it is important to recognize that they too rely on a set of causal assumptions that must be justified and met by the researcher to draw valid causal conclusions. We use key ecological examples to show how biases such as confounding, overcontrol, and collider bias can occur in experimental setups. In tandem, we highlight how such biases can be removed through the application of the structural causal model (SCM) framework. The SCM framework visualizes the causal structure of a system or process under study using directed acyclic graphs (DAGs) and subsequently applies a set of graphical rules to remove bias from both observational and experimental data. We show how DAGs can be applied across ecological experimental studies to ensure proper study design and statistical analysis, leading to more accurate causal estimates drawn from experimental data. Although causal conclusions drawn from RCTs are often taken at face value, ecologists are increasingly becoming aware that experimental approaches must be carefully designed and analyzed to avoid potential biases. By applying DAGs as a visual and conceptual tool, experimental ecologists can increasingly meet the causal assumptions required for valid causal inference.

COVID-19 research has relied heavily on convenience-based samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study (www.icarestudy.com). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended. [ABSTRACT FROM AUTHOR]

Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove this statement and, along with intuitions, formally examine the direction and magnitude of the associations between 2 risk factors of a binary collider using interaction contrasts. Among level one of the collider, 2 variables are independent, positively associated, and negatively associated if multiplicative risk interaction contrast is equal to, more than, and less than 0, respectively; the same results hold for the other level of the collider if the multiplicative survival interaction contrast, equal to multiplicative risk interaction contrast minus the additive risk interaction contrast, is compared with 0. The strength of the association depends on the magnitude of the interaction contrast: The stronger the interaction is, the larger the magnitude of the association will be. However, the common conditional odds ratio under the homogeneity assumption will be bounded. A figure is presented that succinctly illustrates our results and helps researchers to better visualize the associations introduced upon conditioning on a collider. [ABSTRACT FROM AUTHOR]

Objectives: Small-for-gestational age (SGA) is a causal factor for malnutrition (undernutrition). The available evidence on this causal relationship is based on observational studies and suffers from confounding and collider biases. This study aimed to construct a theoretical causal model to estimate the effect of SGA on malnutrition in children aged less than 5 years., Methods: For the causal model, we designated term-SGA status as the exposure variable and malnutrition at 6 months to 5 years of age (diagnosed by World Health Organization criteria) as the outcome variable. Causal estimands were formulated for three stakeholders. A "rapid narrative review" methodology was adopted for literature synthesis. Studies (observational and randomized) listing the causal factors of malnutrition in children aged less than 5 years from the Indian subcontinent were eligible. Four databases (PubMed, Scopus, Web of Science, and ProQuest) were searched and restricted to the last 10 years (search date: December 15, 2023). Information about the causal factors (covariates) of malnutrition and study characteristics was extracted from the article abstracts. Next, a causal model in the form of a directed acyclic graph (DAG) (DAGitty software) was constructed by connecting exposure, outcome, and covariate nodes using the sequential causal criteria of temporality, face validity, recourse to theory, and counterfactual thought experiments., Results: The search yielded 4818 records, of which 342 abstracts were included. Most of the studies were conducted in India (39%) and Bangladesh (27%). The literature synthesis identified 81 factors that were grouped into 17 nodes, referring to 5 domains: socioeconomic, parental, child-related, environmental, and political. The DAG identified 12 different minimal sufficient adjustment sets (conditioning sets for regression analysis) to estimate the total effect of SGA on malnutrition., Conclusion: We offer an evidence-based causal diagram that will minimize bias due to improper selection of factors in studies focusing on malnutrition in term-SGA infants. The DAG and adjustment sets will facilitate the design and data analysis of future studies., Competing Interests: Declaration of competing interest The authors declare no conflict of interest related to this work., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Tumor progression is driven by the accumulation of genetic alterations, including both point mutations and copy number changes. Understanding the temporal sequence of these events is crucial for comprehending the disease but is not directly discernible from cross-sectional genomic data. Cancer progression models, including Mutual Hazard Networks (MHNs), aim to reconstruct the dynamics of tumor progression by learning the causal interactions between genetic events based on their co-occurrence patterns in cross-sectional data. Here, we highlight a commonly overlooked bias in cross-sectional datasets that can distort progression modeling. Tumors become clinically detectable when they cause symptoms or are identified through imaging or tests. Detection factors, such as size, inflammation (fever, fatigue), and elevated biochemical markers, are influenced by genomic alterations. Ignoring these effects leads to "conditioning on a collider" bias, where events making the tumor more observable appear anticorrelated, creating false suppressive effects or masking promoting effects among genetic events. We enhance MHNs by incorporating the effects of genetic progression events on the inclusion of a tumor in a dataset, thus correcting for collider bias. We derive an efficient tensor formula for the likelihood function and apply it to two datasets from the MSK-IMPACT study. In colon adenocarcinoma, we observe a significantly higher rate of clinical detection for TP53-positive tumors, while in lung adenocarcinoma, the same is true for EGFR-positive tumors. Compared to classical MHNs, this approach eliminates several spurious suppressive interactions and uncovers multiple promoting effects.

In this study, we test principal component analysis (PCA) of measured confounders as a method to reduce collider bias in polygenic association models. We present results from simulations and application of the method in the Collaborative Study of the Genetics of Alcoholism (COGA) sample with a polygenic score for alcohol problems, DSM-5 alcohol use disorder as the target phenotype, and two collider variables: tobacco use and educational attainment. Simulation results suggest that assumptions regarding the correlation structure and availability of measured confounders are complementary, such that meeting one assumption relaxes the other. Application of the method in COGA shows that PC covariates reduce collider bias when tobacco use is used as the collider variable. Application of this method may improve PRS effect size estimation in some cases by reducing the effect of collider bias, making efficient use of data resources that are available in many studies. [ABSTRACT FROM AUTHOR]

Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research focused on estimating causal effects, we propose to unify the various existing definitions of selection bias in the literature by considering any bias away from the true causal effect in the referent population (the population before the selection process), due to selecting the sample from the referent population, as selection bias. Given this unified definition, selection bias can be further categorized into two broad types: type 1 selection bias owing to restricting to one or more level(s) of a collider (or a descendant of a collider) and type 2 selection bias owing to restricting to one or more level(s) of an effect measure modifier. To aid in explaining these two types-which can co-occur-we start by reviewing the concepts of the target population, the study sample, and the analytic sample. Then, we illustrate both types of selection bias using causal diagrams. In addition, we explore the differences between these two types of selection bias, and describe methods to minimize selection bias. Finally, we use an example of "M-bias" to demonstrate the advantage of classifying selection bias into these two types. [ABSTRACT FROM AUTHOR]

Mendelian randomization (MR) uses genetic variants as instrumental variables to investigate the causal effect of a risk factor on an outcome. A collider is a variable influenced by two or more other variables. Naive calculation of MR estimates in strata of the population defined by a collider, such as a variable affected by the risk factor, can result in collider bias. We propose an approach that allows MR estimation in strata of the population while avoiding collider bias. This approach constructs a new variable, the residual collider, as the residual from regression of the collider on the genetic instrument, and then calculates causal estimates in strata defined by quantiles of the residual collider. Estimates stratified on the residual collider will typically have an equivalent interpretation to estimates stratified on the collider, but they are not subject to collider bias. We apply the approach in several simulation scenarios considering different characteristics of the collider variable and strengths of the instrument. We then apply the proposed approach to investigate the causal effect of smoking on bladder cancer in strata of the population defined by bodyweight. The new approach generated unbiased estimates in all the simulation settings. In the applied example, we observed a trend in the stratum-specific MR estimates at different bodyweight levels that suggested stronger effects of smoking on bladder cancer among individuals with lower bodyweight. The proposed approach can be used to perform MR studying heterogeneity among subgroups of the population while avoiding collider bias. [ABSTRACT FROM AUTHOR]

Purpose: Workers' compensation claims consist of occupational injuries severe enough to meet a compensability threshold. Theoretically, systems with higher thresholds should have fewer claims but greater average severity. For research that relies on claims data, particularly cross-jurisdictional comparisons of compensation systems, this results in collider bias that can lead to spurious associations confounding analyses. In this study, I use real and simulated claims data to demonstrate collider bias and problems with methods used to account for it. Methods: Using Australian claims data, I used a linear regression to test the association between claim rate and mean disability durations across Statistical Areas. Analyses were repeated with nesting by state/territory to account for variations in compensability thresholds across compensation systems. Both analyses are repeated on left-censored data. Simulated claims data are analysed with Cox survival analyses to illustrate how left-censoring can reverse effects. Results: The claim rate within a Statistical Area was inversely associated with disability duration. However, this reversed when Statistical Areas were nested by state/territory. Left-censoring resulted in an attenuation of the unnested association to non-significance, while the nested association remained significantly positive. Cox regressions with simulated claims data demonstrated how left-censoring can reverse effects. Conclusions: Collider bias can seriously confound work disability research, particularly cross-jurisdictional comparisons. Work disability researchers must grapple with this challenge by using appropriate study designs and analytical approaches, and considering how it affects the interpretation of results. [ABSTRACT FROM AUTHOR]

This commentary discusses causal estimands of same-age and same-grade comparisons for assessing grade-retention effects on student ability and performance. Using potential outcomes notation, we show that same-age and same-grade comparisons refer to different retention-promotion contrasts and therefore assess different causal questions. We also comment on deleting versus censoring records of students who dropped out of the study or do not belong to the treatment regimes under investigation. Whereas deleting entire student records potentially induces collider bias, censoring circumvents bias if censoring is ignorable given the observed pretreatment covariates.

Electronic health records (EHRs) offer unprecedented opportunities to answer epidemiologic questions. However, unlike in ordinary cohort studies or randomized trials, EHR data are collected somewhat idiosyncratically. In particular, patients who have more contact with the medical system have more opportunities to receive diagnoses, which are then recorded in their EHRs. The goal of this article is to shed light on the nature and scope of this phenomenon, known as informative presence, which can bias estimates of associations. We show how this can be characterized as an instance of misclassification bias. As a consequence, we show that informative presence bias can occur in a broader range of settings than previously thought, and that simple adjustment for the number of visits as a confounder may not fully correct for bias. Additionally, where previous work has considered only underdiagnosis, investigators are often concerned about overdiagnosis; we show how this changes the settings in which bias manifests. We report on a comprehensive series of simulations to shed light on when to expect informative presence bias, how it can be mitigated in some cases, and cases in which new methods need to be developed. [ABSTRACT FROM AUTHOR]

Bone research is a dynamic area of scientific investigation that usually encompasses multidisciplines. Virtually all basic cellular research, clinical research and epidemiologic research rely on statistical concepts and methodology for inference. This paper discusses common issues and suggested solutions concerning the application of statistical thinking in bone research, particularly in clinical and epidemiological investigations. The issues are sample size estimation, biases and confounders, analysis of longitudinal data, categorization of continuous data, selection of significant variables, over-fitting, P-values, false positive finding, confidence interval, and Bayesian inference. It is hoped that by adopting the suggested measures the scientific quality of bone research can improve.

Bernd Kowall,1 Andreas Stang,1,2 Raimund Erbel,3 Susanne Moebus,4 Astrid Petersmann,5 Antje Steveling,6 Karl-Heinz Jöckel,3 Henry Völzke7,8 1Center of Clinical Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, Medical Faculty, University Duisburg-Essen, Essen, Germany; 2School of Public Health, Department of Epidemiology, Boston University, Boston, MA 02118, USA; 3Institute for Medical Informatics, Biometry and Epidemiology, University Clinic Essen, University Duisburg-Essen, Essen, Germany; 4Center for Urban Epidemiology, Institute for Medical Informatics, Biometry and Epidemiology, University Clinic Essen, Essen, Germany; 5Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany; 6Department of Internal Medicine, University Medicine Greifswald, Greifswald, Germany; 7Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; 8German Center for Diabetes Research, Site Greifswald, Greifswald, GermanyCorrespondence: Bernd KowallCenter of Clinical Epidemiology, c/o Institute of Medical Informatics, Biometry and Epidemiology (IMIBE), Hufelandstraße 55, Essen 45147, GermanyTel +49-201-92239-295Fax +49-201-92239-333Email bernd.kowall@uk-essen.deAims/Hypothesis: There is controversy on whether an obesity paradox exists in type 2 diabetes, ie, that mortality is lowest in overweight or obesity. We examined the role of potential biases in the obesity paradox.Methods: From two regional population-based German cohort studies – the Heinz Nixdorf Recall Study and the Study of Health in Pomerania (baseline examinations 2000– 2003/1997– 2001) – 1187 persons with diabetes at baseline were included (mean age 62.6 years, 60.9% males). Diabetes was ascertained by self-report of physician’s diagnosis, antidiabetic medication, fasting/random glucose or haemoglobin A1c. Mortality data were assessed for up to 17.7 years. We used restricted cubic splines and Cox regression models to assess associations between body mass index (BMI) and mortality. Sensitivity analyses addressed, inter alia, exclusion of early death cases, of persons with cancer, kidney disease or with history of cardiovascular diseases, and of ever smokers. Furthermore, we examined the role of treatment bias and collider bias for the obesity paradox.Results: In spline models, mortality risk was lowest for BMI at about 31 kg/m2. Sensitivity analyses carried out one after another had hardly any impact on this result. In our cohort, persons with diabetes and BMI ≥ 30 kg/m2 did not have better treatment than non-obese patients, and we found that collider bias played only a minor role in the obesity paradox.Conclusion: In a cohort of 1187 persons with diabetes, mortality risk was lowest in persons with moderate obesity. We cannot explain this result by a variety of sensitivity analyses.Keywords: collider bias, diabetes, mortality, obesity paradox, overweight

The test-negative design (TND) is a popular method for evaluating vaccine effectiveness (VE). A "classical" TND study includes symptomatic individuals tested for the disease targeted by the vaccine to estimate VE against symptomatic infection. However, recent applications of the TND have attempted to estimate VE against infection by including all tested individuals, regardless of their symptoms. In this article, we use directed acyclic graphs and simulations to investigate potential biases in TND studies of COVID-19 VE arising from the use of this "alternative" approach, particularly when applied during periods of widespread testing. We show that the inclusion of asymptomatic individuals can potentially lead to collider stratification bias, uncontrolled confounding by health and healthcare-seeking behaviors (HSBs), and differential outcome misclassification. While our focus is on the COVID-19 setting, the issues discussed here may also be relevant in the context of other infectious diseases. This may be particularly true in scenarios where there is either a high baseline prevalence of infection, a strong correlation between HSBs and vaccination, different testing practices for vaccinated and unvaccinated individuals, or settings where both the vaccine under study attenuates symptoms of infection and diagnostic accuracy is modified by the presence of symptoms., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Selection bias has long been central in methodological discussions across epidemiology and other fields. In epidemiology, the concept of selection bias has been continually evolving over time. In this issue of the Journal, Mathur and Shpitser (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) present simple graphical rules for using a Single World Intervention Graph (SWIG) to assess the presence of selection bias when estimating treatment effects in both the general population and a selected sample. Notably, the authors examine the setting in which the treatment affects selection, an issue not well-addressed in the existing literature on selection bias. To place the work by Mathur and Shpitser in context, we review the evolution of the concept of selection bias in epidemiology, with a primary focus on the developments in the last 20-30 years since the introduction of causal directed acyclic graphs (DAGs) to epidemiologic research., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

A 2015 experiment by Hanson and Delft colleagues provided further confirmation that the quantum world violates the Bell inequalities, being the first Bell test to close two known experimental loopholes simultaneously. The experiment was also taken to provide new evidence of 'spooky action at a distance'. Here we argue for caution about the latter claim. The Delft experiment relies on entanglement swapping, and our main claim is that this geometry introduces an additional loophole in the argument from violation of the Bell inequalities to action at a distance: the apparent action at a distance may be an artifact of 'collider bias'. In the absence of retrocausality, the sensitivity of such experiments to this 'Collider Loophole' (CL) depends on the temporal relation between the entanglement swapping measurement C and the two measurements A and B between which we seek to infer a causal connection. CL looms large if the C is in the future of A and B, but not if C is in the past. The Delft experiment itself is the intermediate case, in which the separation is spacelike. We argue that this leaves it vulnerable to CL, unable to establish conclusively that it avoids it. [ABSTRACT FROM AUTHOR]

Background: Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB., Methods: Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias., Results: Volunteer bias in all associations, as naively estimated in UKB, was substantial-in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size., Conclusions: Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Compositional data comprise the parts of some whole, for which all parts sum to that whole. They are prevalent in many epidemiological contexts. Although many of the challenges associated with analysing compositional data have been discussed previously, we do so within a formal causal framework by utilizing directed acyclic graphs (DAGs).Methods: We depict compositional data using DAGs and identify two distinct effect estimands in the generic case: (i) the total effect, and (ii) the relative effect. We consider each in the context of three specific example scenarios involving compositional data: (1) the relationship between the economically active population and area-level gross domestic product; (2) the relationship between fat consumption and body weight; and (3) the relationship between time spent sedentary and body weight. For each, we consider the distinct interpretation of each effect, and the resulting implications for related analyses.Results: For scenarios (1) and (2), both the total and relative effects may be identifiable and causally meaningful, depending upon the specific question of interest. For scenario (3), only the relative effect is identifiable. In all scenarios, the relative effect represents a joint effect, and thus requires careful interpretation.Conclusions: DAGs are useful for considering causal effects for compositional data. In all analyses involving compositional data, researchers should explicitly consider and declare which causal effect is sought and how it should be interpreted. [ABSTRACT FROM AUTHOR]

Longitudinal studies have sought to establish whether environmental exposures such as smoking accelerate the attrition of individuals' telomeres over time. These studies typically control for baseline telomere length (TL) by including it as a covariate in statistical models. However, baseline TL also differs between smokers and non-smokers, and telomere attrition is spuriously linked to baseline TL via measurement error and regression to the mean. Using simulated datasets, we show that controlling for baseline TL overestimates the true effect of smoking on telomere attrition. This bias increases with increasing telomere measurement error and increasing difference in baseline TL between smokers and non-smokers. Using a meta-analysis of longitudinal datasets, we show that as predicted, the estimated difference in telomere attrition between smokers and non-smokers is greater when statistical models control for baseline TL than when they do not, and the size of the discrepancy is positively correlated with measurement error. The bias we describe is not specific to smoking and also applies to other exposures. We conclude that to avoid invalid inference, models of telomere attrition should not control for baseline TL by including it as a covariate. Many claims of accelerated telomere attrition in individuals exposed to adversity need to be re-assessed.

Case fatality rates in severe falciparum malaria depend on the pattern and degree of vital organ dysfunction. Recent large-scale case-control analyses of pooled severe malaria data reported that glucose-6-phosphate dehydrogenase deficiency (G6PDd) was protective against cerebral malaria but increased the risk of severe malarial anaemia. A novel formulation of the balancing selection hypothesis was proposed as an explanation for these findings, whereby the selective advantage is driven by the competing risks of death from cerebral malaria and death from severe malarial anaemia. We re-analysed these claims using causal diagrams and showed that they are subject to collider bias. A simulation based sensitivity analysis, varying the strength of the known effect of G6PDd on anaemia, showed that this bias is sufficient to explain all of the observed association. Future genetic epidemiology studies in severe malaria would benefit from the use of causal reasoning.

Suppose we are interested in the effect of variable X on variable Y. If X and Y both influence, or are associated with variables that influence, a common outcome, called a collider, then conditioning on the collider (or on a variable influenced by the collider – its "child") induces a spurious association between X and Y, which is known as collider bias. Characterizing the magnitude and direction of collider bias is crucial for understanding the implications of selection bias and for adjudicating decisions about whether to control for variables that are known to be associated with both exposure and outcome but could be either confounders or colliders. Considering a class of situations where all variables are binary, and where X and Y either are, or are respectively influenced by, two marginally independent causes of a collider, we derive collider bias that results from (i) conditioning on specific levels of the collider or its child (on the covariance, risk difference, and in two cases odds ratio, scales), or (ii) linear regression adjustment for, the collider or its child. We also derive simple conditions that determine the sign of such bias. [ABSTRACT FROM AUTHOR]

Background: Selection bias affects Mendelian randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor-outcome association. However, the relative importance of selection bias for Mendelian randomization compared with other potential biases is unclear.Methods: We performed an extensive simulation study to assess the impact of selection bias on a typical Mendelian randomization investigation. We considered inverse probability weighting as a potential method for reducing selection bias. Finally, we investigated whether selection bias may explain a recently reported finding that lipoprotein(a) is not a causal risk factor for cardiovascular mortality in individuals with previous coronary heart disease.Results: Selection bias had a severe impact on bias and Type 1 error rates in our simulation study, but only when selection effects were large. For moderate effects of the risk factor on selection, bias was generally small and Type 1 error rate inflation was not considerable. Inverse probability weighting ameliorated bias when the selection model was correctly specified, but increased bias when selection bias was moderate and the model was misspecified. In the example of lipoprotein(a), strong genetic associations and strong confounder effects on selection mean the reported null effect on cardiovascular mortality could plausibly be explained by selection bias.Conclusions: Selection bias can adversely affect Mendelian randomization investigations, but its impact is likely to be less than other biases. Selection bias is substantial when the effects of the risk factor and confounders on selection are particularly large. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: There is conflicting evidence about the obesity paradox—the counterintuitive survival advantage of obesity among certain subpopulations of individuals with chronic conditions. It is believed that results supporting the obesity paradox are due to methodological flaws, such as collider bias. The aim of this study was to examine the association between obesity and mortality in Australian men and women. In addition, we explored whether obesity would appear to be protective if the analysis was restricted to a subpopulation with disease, and to discuss the potential role of collider bias in producing such a result. Methods: The examined cohort included 10,575 Australian adults (4844 men and 5731 women) aged 25–91 years who were recruited for the AusDiab baseline survey in 1999 and followed-up through 2014. The main predictor variable was BMI categorised as normal weight (18.5 to <25 kg/m2), overweight (25 to <30 kg/m2) and obese (≥30 kg/m2), and the outcome of interest was all-cause mortality. Hazard ratios were estimated from Cox proportional hazards regression models in the entire cohort and then in subpopulations with and without diabetes. Results: A total of 1477 deaths occurred during 145,384 person-years (median 14.6 years) of follow-up. Mortality was higher in obese than in normal-weight individuals for the full population (HR 1.18; 95% CI 1.05, 1.32). When an interaction between diabetes status and BMI category was added to the model, there was no evidence of an interaction between BMI and diabetes status (p = 0.92). When participants with and without diabetes were analysed separately, there was no evidence of an association between obesity and mortality in those with diabetes (HR 0.91; 95% CI 0.62, 1.33). Conclusions/interpretation: In the entire AusDiab cohort, we found a significantly higher mortality among obese participants as compared with their normal-weight counterparts. We found no difference in the obesity–mortality association between individuals with and without diabetes. [ABSTRACT FROM AUTHOR]

An editorial is presented in which the authors discuss the concept of collider bias in addiction research efforts, and its significance concerning the potential effect of smoking upon COVID-19. The article also addresses confounder bias and how researchers should approach research biases.