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Epidemiology (Cambridge, Mass.) Nov 2023We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural...
We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural approach (where conditioning on selection into the study opens a noncausal path from exposure to disease in a directed acyclic graph) and the traditional definition (where a given measure of association differs between the study sample and the population eligible for inclusion). This approach is nonparametric, and selection bias under the approach can be analyzed using single-world intervention graphs both under and away from the null hypothesis. It allows the simultaneous analysis of confounding and selection bias, it explicitly links the selection of study participants to the estimation of causal effects using study data, and it can be adapted to handle selection bias in descriptive epidemiology. Through examples, we show that this approach provides a novel perspective on the variety of mechanisms that can generate selection bias and simplifies the analysis of selection bias in matched studies and case-cohort studies.
PubMed: 37708480
DOI: 10.1097/EDE.0000000000001660 -
Pharmacoepidemiology and Drug Safety Dec 2022Glucosamine is a widely used supplement to treat joint pain and osteoarthritis despite inconclusive randomized trial results on its effectiveness. In contrast,...
BACKGROUND
Glucosamine is a widely used supplement to treat joint pain and osteoarthritis despite inconclusive randomized trial results on its effectiveness. In contrast, observational studies associate glucosamine with significant reductions in mortality and cancer incidence. We evaluated the extent of bias, particularly selection bias, to explain these surprising beneficial effects.
METHODS
We searched the literature to identify all observational studies reporting on the effect of glucosamine use on major outcomes.
RESULTS
We identified 11 observational studies, reporting a mean 16% reduction in all-cause mortality (hazard ratio [HR] 0.84, 95% CI: 0.81-0.87) with glucosamine use, as well as significant reductions in cancer incidence and other major diseases including cardiovascular, respiratory and diabetes. We show that these significant effects can result from selection bias due to collider stratification, as all studies used "prevalent" cohorts, where glucosamine use started before cohort entry, and where subjects agreed to join the cohorts. Our illustration of the bias using the UK Biobank publication involving a half-million subjects shows how a true rate ratio of mortality of 1.0 in the population can result in a biased rate ratio of 0.82 in the prevalent cohort.
CONCLUSIONS
The observational studies reporting significant reductions in mortality, cancer incidence and other outcomes with glucosamine were affected by selection bias from collider stratification. In the absence of properly conducted observational studies that circumvent this bias by considering "new users", the studies to date cannot support the prescription of this supplement as a preventive measure for mortality, cancer, and other chronic diseases.
Topics: Humans; Glucosamine; Selection Bias; Bias; Cohort Studies; Neoplasms
PubMed: 36029480
DOI: 10.1002/pds.5535 -
Addiction (Abingdon, England) May 2021
Topics: Behavior, Addictive; COVID-19; Confounding Factors, Epidemiologic; Epidemiologic Research Design; Humans; SARS-CoV-2; Selection Bias; Smoking
PubMed: 33226690
DOI: 10.1111/add.15348 -
Epidemiologic Reviews Jan 2022Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be... (Review)
Review
Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be used to explore the potential impact of confounding bias, selection bias (collider stratification bias), and information bias. Quantitative bias analysis includes methods that can be used to check the robustness of study findings to multiple types of bias and methods that use simulation studies to generate data and understand the hypothetical impact of specific types of bias in a simulated data set. In this article, we review 2 strategies for quantitative bias analysis: 1) traditional probabilistic quantitative bias analysis and 2) quantitative bias analysis with generated data. An important difference between the 2 strategies relates to the type of data (real vs. generated data) used in the analysis. Monte Carlo simulations are used in both approaches, but the simulation process is used for different purposes in each. For both approaches, we outline and describe the steps required to carry out the quantitative bias analysis and also present a bias-analysis tutorial demonstrating how both approaches can be applied in the context of an analysis for selection bias. Our goal is to highlight the utility of quantitative bias analysis for practicing epidemiologists and increase the use of these methods in the epidemiologic literature.
Topics: Bias; Computer Simulation; Humans; Monte Carlo Method; Selection Bias
PubMed: 34664653
DOI: 10.1093/epirev/mxab012 -
PeerJ 2023In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to...
BACKGROUND
In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence and direction of selection bias.
METHODS
We used a BCI, a function of selection probabilities conditional on outcome and covariates, with different selection bias scenarios in a logistic regression setting. A bias-correction sensitivity plot was illustrated to analyze the associations between proctoscopy examination and sociodemographic variables obtained using the data from the Taiwan National Health Interview Survey (NHIS) and of a subset of individuals who consented to having their health insurance data further linked.
RESULTS
We included 15,247 people aged ≥20 years, and 87.74% of whom signed the informed consent. When the entire sample was considered, smokers were less likely to undergo proctoscopic examination (odds ratio (OR): 0.69, 95% CI [0.57-0.84]), than nonsmokers were. When the data of only the people who provided consent were considered, the OR was 0.76 (95% CI [0.62-0.94]). The bias-correction sensitivity plot indicated varying ORs under different degrees of selection bias.
CONCLUSIONS
When data are only available in a subsample of a population, a bias-correction sensitivity plot can be used to easily visualize varying ORs under different selection bias scenarios. The similar strategy can be applied to models other than logistic regression if an appropriate BCI is derived.
Topics: Humans; Selection Bias; Surveys and Questionnaires; Insurance, Health; Odds Ratio; Informed Consent
PubMed: 38025739
DOI: 10.7717/peerj.16411 -
Pharmaceutical Statistics Nov 2021When making decisions regarding the investment and design for a Phase 3 programme in the development of a new drug, the results from preceding Phase 2 trials are an...
When making decisions regarding the investment and design for a Phase 3 programme in the development of a new drug, the results from preceding Phase 2 trials are an important source of information. However, only projects in which the Phase 2 results show promising treatment effects will typically be considered for a Phase 3 investment decision. This implies that, for those projects where Phase 3 is pursued, the underlying Phase 2 estimates are subject to selection bias. We will in this article investigate the nature of this selection bias based on a selection of distributions for the treatment effect. We illustrate some properties of Bayesian estimates, providing shrinkage of the Phase 2 estimate to counteract the selection bias. We further give some empirical guidance regarding the choice of prior distribution and comment on the consequences for decision-making in investment and planning for Phase 3 programmes.
Topics: Bayes Theorem; Bias; Humans; Selection Bias
PubMed: 34002467
DOI: 10.1002/pst.2132 -
Methods in Molecular Biology (Clifton,... 2021In this chapter, a catalog of the various types of bias that can affect the validity of clinical epidemiologic studies is presented. The biases are classified by stage...
In this chapter, a catalog of the various types of bias that can affect the validity of clinical epidemiologic studies is presented. The biases are classified by stage of research: literature review and publication, design of the study and selection of subjects, execution of the intervention, measurement of exposures and outcomes, data analysis , and interpretation and publication. Definitions are provided for each type of bias listed.
Topics: Bias; Confounding Factors, Epidemiologic; Epidemiologic Studies; Humans; Outcome and Process Assessment, Health Care; Patient Selection; Research Design; Selection Bias
PubMed: 33871837
DOI: 10.1007/978-1-0716-1138-8_3 -
Epidemiology (Cambridge, Mass.) Jan 2023Studies on the effectiveness of self-managed medication abortion may suffer from misclassification and selection bias due to self-reported outcomes and loss of...
BACKGROUND
Studies on the effectiveness of self-managed medication abortion may suffer from misclassification and selection bias due to self-reported outcomes and loss of follow-up. Monte Carlo sensitivity analysis can estimate self-managed abortion effectiveness accounting for these potential biases.
METHODS
We conducted a Monte Carlo sensitivity analysis based on data from the Studying Accompaniment model Feasibility and Effectiveness Study (the SAFE Study), to generate bias-adjusted estimates of the effectiveness of self-managed abortion with accompaniment group support. Between July 2019 and April 2020, we enrolled a total of 1051 callers who contacted accompaniment groups in Argentina and Nigeria for self-managed abortion information; 961 took abortion medications and completed at least one follow-up. Using these data, we calculated measures of effectiveness adjusted for ineligibility, misclassification, and selection bias across 50,000 simulations with bias parameters drawn from pre-specified Beta distributions in R.
RESULTS
After accounting for the potential influence of various sources of bias, bias-adjusted estimates of effectiveness were similar to observed estimates, conditional on chosen bias parameters: 92.68% (95% simulation interval: 87.80%, 95.74%) for mifepristone in combination with misoprostol (versus 93.7% in the observed data) and 98.47% (95% simulation interval: 96.79%, 99.39%) for misoprostol alone (versus 99.3% in the observed data).
CONCLUSIONS
After adjustment for multiple potential sources of bias, estimates of self-managed medication abortion effectiveness remain high. Monte Carlo sensitivity analysis may be useful in studies measuring an epidemiologic proportion (i.e., effectiveness, prevalence, cumulative incidence) while accounting for possible selection or misclassification bias.
Topics: Female; Pregnancy; Humans; Selection Bias; Misoprostol; Self-Management; Abortion, Induced; Monte Carlo Method
PubMed: 36455250
DOI: 10.1097/EDE.0000000000001546 -
American Journal of Epidemiology Feb 2024In epidemiology, collider stratification bias, the bias resulting from conditioning on a common effect of two causes, is oftentimes considered a type of selection bias,...
In epidemiology, collider stratification bias, the bias resulting from conditioning on a common effect of two causes, is oftentimes considered a type of selection bias, regardless of the conditioning methods employed. In this commentary, we distinguish between two types of collider stratification bias: collider restriction bias due to restricting to one level of a collider (or a descendant of a collider) and collider adjustment bias through inclusion of a collider (or a descendant of a collider) in a regression model. We argue that categorizing collider adjustment bias as a form of selection bias may lead to semantic confusion, as adjustment for a collider in a regression model does not involve selecting a sample for analysis. Instead, we propose that collider adjustment bias can be better viewed as a type of overadjustment bias. We further provide two distinct causal diagram structures to distinguish collider restriction bias and collider adjustment bias. We hope that such a terminological distinction can facilitate easier and clearer communication.
Topics: Humans; Selection Bias; Bias; Causality
PubMed: 37939152
DOI: 10.1093/aje/kwad213 -
BMJ (Clinical Research Ed.) Jun 2023Effect estimates may be biased when the study design or the data analysis is conditional on a collider—a variable that is caused by two other variables. Causal...
Effect estimates may be biased when the study design or the data analysis is conditional on a collider—a variable that is caused by two other variables. Causal directed acyclic graphs are a helpful tool to identify colliders that may introduce selection bias in observational research.
Topics: Humans; Selection Bias; Bias
PubMed: 37286200
DOI: 10.1136/bmj.p1135