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Acta Obstetricia Et Gynecologica... Apr 2018Longitudinal cohort studies can provide important evidence about preventable causes of disease, but the success relies heavily on the commitment of their participants,... (Review)
Review
Longitudinal cohort studies can provide important evidence about preventable causes of disease, but the success relies heavily on the commitment of their participants, both at recruitment and during follow up. Initial participation rates have decreased in recent decades as have willingness to participate in subsequent follow ups. It is important to examine how such selection affects the validity of the results. In this article, we describe the conceptual framework for selection bias due to nonparticipation and loss to follow up in cohort studies, using both a traditional epidemiological approach and directed acyclic graphs. Methods to quantify selection bias are introduced together with analytical strategies to adjust for the bias including controlling for covariates associated with selection, inverse probability weighting and bias analysis. We use several studies conducted in the Danish National Birth Cohort as examples of how to quantify selection bias and also understand the underlying selection mechanisms. Although women who chose to participate in this cohort were typically of higher social status, healthier and with less disease than all those eligible for study, differential selection was modest and the influence of selection bias on several selected exposure-outcome associations was limited. These findings are reassuring and support enrolling a subset of motivated participants who would engage in long-term follow up rather than prioritize representativeness. Some of the presented methods are applicable even with limited data on nonparticipants and those lost to follow up, and can also be applied to other study designs such as case-control studies and surveys.
Topics: Cohort Studies; Data Interpretation, Statistical; Gynecology; Humans; Obstetrics; Research Design; Selection Bias
PubMed: 29415329
DOI: 10.1111/aogs.13319 -
Epidemiology (Cambridge, Mass.) Sep 2022Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research...
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.
Topics: Bias; Causality; Humans; Selection Bias
PubMed: 35700187
DOI: 10.1097/EDE.0000000000001516 -
Metabolomics : Official Journal of the... Jan 2019Metabolomics techniques are increasingly applied in epidemiologic research. Many available assays are still relatively expensive and therefore measurements are often... (Review)
Review
BACKGROUND
Metabolomics techniques are increasingly applied in epidemiologic research. Many available assays are still relatively expensive and therefore measurements are often performed in small patient population studies such as case series or case-control designs with strong participant selection criteria. Subsequently, metabolomics data are frequently used to assess secondary associations for which the original study was not explicitly designed. Especially in these secondary analyses, there is a risk that the original selection criteria and the conditioning that takes place due to this selection are not properly accounted for which can lead to selection bias.
AIM OF REVIEW
In this tutorial, we start with a brief theoretical introduction on the issue of selection bias. Subsequently, we demonstrate how selection bias can occur in metabolomics studies by means of an investigation into associations of metabolites with total body fat in a nested case-control study that was originally designed to study effects of elevated fasting glucose.
KEY SCIENTIFIC CONCEPTS OF REVIEW
We demonstrate that standard analytical methods, such as stratification or adjustment in regression analyses, are not suited to deal with selection bias and may even induce the bias when analysing metabolite-phenotype relationships in selected groups. Finally, we show that inverse probability weighting, also known as survey weighting, can be used in some situations to make unbiased estimates of the outcomes.
Topics: Humans; Metabolomics; Research Design; Selection Bias
PubMed: 30830435
DOI: 10.1007/s11306-018-1463-4 -
Nederlands Tijdschrift Voor Geneeskunde 2013A systematic distortion of the relationship between a treatment, risk factor or exposure and clinical outcomes is denoted by the term 'bias'. Three types of bias can be... (Review)
Review
A systematic distortion of the relationship between a treatment, risk factor or exposure and clinical outcomes is denoted by the term 'bias'. Three types of bias can be distinguished: information bias, selection bias, and confounding. These three types of bias and their potential solutions are discussed using various examples.
Topics: Bias; Biomedical Research; Data Interpretation, Statistical; Humans; Risk Factors; Selection Bias
PubMed: 25714762
DOI: No ID Found -
The Journal of Investigative Dermatology Nov 2016Systematic reviews are increasingly utilized in the medical literature to summarize available evidence on a research question. Like other studies, systematic reviews are... (Review)
Review
Systematic reviews are increasingly utilized in the medical literature to summarize available evidence on a research question. Like other studies, systematic reviews are at risk for bias from a number of sources. A systematic review should be based on a formal protocol developed and made publicly available before the conduct of the review; deviations from a protocol with selective presentation of data can result in reporting bias. Evidence selection bias occurs when a systematic review does not identify all available data on a topic. This can arise from publication bias, where data from statistically significant studies are more likely to be published than those that are not statistically significant. Systematic reviews are also susceptible to bias that arises in any of the included primary studies, each of which needs to be critically appraised. Finally, competing interests can lead to bias in favor of a particular intervention. Awareness of these sources of bias is important for authors and consumers of the scientific literature as they conduct and read systematic reviews and incorporate their findings into clinical practice and policy making.
Topics: Dermatology; Disease Management; Humans; Research Design; Selection Bias; Skin Diseases
PubMed: 27772550
DOI: 10.1016/j.jid.2016.08.021 -
CMAJ : Canadian Medical Association... May 2017
Topics: Humans; Selection Bias
PubMed: 28483850
DOI: 10.1503/cmaj.732958 -
BMJ Evidence-based Medicine Feb 2018This article is part of a series of articles featuring the Catalogue of Bias introduced in this volume of that describes attrition bias and outlines its potential...
This article is part of a series of articles featuring the Catalogue of Bias introduced in this volume of that describes attrition bias and outlines its potential impact on research studies and the preventive steps to minimise its risk. Attrition bias is a type of selection bias due to systematic differences between study groups in the number and the way participants are lost from a study. Differences between people who leave a study and those who continue, particularly between study groups, can be the reason for any observed effect and not the intervention itself. Associations for mortality in trials of tranexamic acid and upper gastrointestinal bleeding were no longer apparent after studies with high or unclear risk of attrition bias were removed. Over-recruitment can help prevent important attrition bias. Sampling weights and tailored replenishment samples can help to compensate for the effects of attrition bias when present.
Topics: Bias; Humans; Patient Dropouts; Selection Bias
PubMed: 29367321
DOI: 10.1136/ebmed-2017-110883 -
Evolution; International Journal of... Mar 2022Mutation accumulation (MA) experiments, in which de novo mutations are sampled and subsequently characterized, are an essential tool in understanding the processes...
Mutation accumulation (MA) experiments, in which de novo mutations are sampled and subsequently characterized, are an essential tool in understanding the processes underlying evolution. In microbial populations, MA protocols typically involve a period of population growth between severe bottlenecks, such that a single individual can form a visible colony. While it has long been appreciated that the action of positive selection during this growth phase cannot be eliminated, it is typically assumed to be negligible. Here, we quantify the effect of both positive and negative selection in MA studies, demonstrating that selective effects can substantially bias the distribution of fitness effects (DFE) and mutation rates estimated from typical MA protocols in microbes. We then present a simple correction for this bias that applies to both beneficial and deleterious mutations, and can be used to correct the observed DFE in multiple environments. We use simulated MA experiments to illustrate the extent to which the MA-inferred DFE differs from the underlying true DFE, and demonstrate that the proposed correction accurately reconstructs the true DFE over a wide range of scenarios; we also provide an example of these corrections applied to experimental data. These results highlight that positive selection during microbial MA experiments is in fact not negligible, but can be corrected to gain a more accurate understanding of fundamental evolutionary parameters.
Topics: Genetic Fitness; Mutation; Mutation Accumulation; Mutation Rate; Selection Bias; Selection, Genetic
PubMed: 34989408
DOI: 10.1111/evo.14430 -
Epidemiology (Cambridge, Mass.) Mar 2023In a seminal paper, Hernán et al. 2004 provided a systematic classification of selection biases, for scenarios where the selection is a collider between the exposure...
In a seminal paper, Hernán et al. 2004 provided a systematic classification of selection biases, for scenarios where the selection is a collider between the exposure and the outcome. Hernán 2017 discussed another scenario, where the selection is statistically independent of the exposure, but associated with the outcome through common causes. In this note, we extend the discussion to scenarios where the selection is directly influenced by the outcome, but not by the exposure. We discuss whether these types of outcome-dependent selections preserve the sharp causal null hypothesis, and whether or not they allow for estimation of causal effects in the selected sample and/or in the source population.
Topics: Humans; Selection Bias; Epidemiology; Causality
PubMed: 36722800
DOI: 10.1097/EDE.0000000000001567 -
Journal of the American Dental... Jul 2015
Topics: Humans; Selection Bias
PubMed: 26113094
DOI: 10.1016/j.adaj.2015.05.010