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Methods in Molecular Biology (Clifton,... 2021Clinical epidemiological research entails assessing the burden and etiology of disease, the diagnosis and prognosis of disease, the efficacy of preventive measures or...
Clinical epidemiological research entails assessing the burden and etiology of disease, the diagnosis and prognosis of disease, the efficacy of preventive measures or treatments, the analysis of the risks and benefits of diagnostic and therapeutic maneuvers, and the evaluation of health care services. In all areas, the main focus is to describe the relationship between exposure and outcome and to determine one of the following: prevalence, incidence, cause, prognosis, or effect of treatment. The accuracy of these conclusions is determined by the validity of the study. Validity is determined by addressing potential biases and possible confounders that may be responsible for the observed association. Therefore, it is important to understand the types of bias that exist and also to be able to assess their impact on the magnitude and direction of the observed effect. The following chapter reviews the epidemiological concepts of selection bias, information bias, intervention bias, and confounding and discusses ways in which these sources of bias can be minimized.
Topics: Bias; Confounding Factors, Epidemiologic; Humans; Incidence; Prevalence; Research Design; Selection Bias
PubMed: 33871836
DOI: 10.1007/978-1-0716-1138-8_2 -
Nephrology (Carlton, Vic.) Jun 2020Study quality depends on a number of factors, one of them being internal validity. Such validity can be affected by random and systematic error, the latter also known as... (Review)
Review
Study quality depends on a number of factors, one of them being internal validity. Such validity can be affected by random and systematic error, the latter also known as bias. Both make it more difficult to assess a correct frequency or the true relationship between exposure and outcome. Where random error can be addressed by increasing the sample size, a systematic error in the design, the conduct or the reporting of a study is more problematic. In this article, we will focus on bias, discuss different types of selection bias (sampling bias, confounding by indication, incidence-prevalence bias, attrition bias, collider stratification bias and publication bias) and information bias (recall bias, interviewer bias, observer bias and lead-time bias), indicate the type of studies where they most frequently occur and provide suggestions for their prevention.
Topics: Biomedical Research; Humans; Interviews as Topic; Observer Variation; Research Design; Selection Bias; Self Report
PubMed: 32133725
DOI: 10.1111/nep.13706 -
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 -
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 -
The Science of the Total Environment Sep 2023As climate change exerts wide ranging health impacts, there is a surge of interest in the associations between climatic factors and mental and behavioral disorders... (Meta-Analysis)
Meta-Analysis Review
As climate change exerts wide ranging health impacts, there is a surge of interest in the associations between climatic factors and mental and behavioral disorders (MBDs). Existing quantitative syntheses focus mainly on heat and high temperature exposure, neglecting the effects of other climatic factors and their synergies. The objective of this study is to conduct a systematic review and meta-analysis of the evidence of associations between climatic exposure and combined mental and behavioral health conditions and specific mental disorders (e.g., schizophrenia, dementia). A systematic search was conducted April 11-16, 2022 using Web of Science, Medline, ProQuest, EMBASE, PsycINFO, CINAHL, and Environment Complete. Screening and eligibility screening followed inclusion criteria based on population, exposure, comparator, and outcome guidelines. Risk of bias assessment was performed, a narrative synthesis was first presented for all studies, and random-effect meta-analyses were performed when at least three studies were available for a specific exposure-outcome pair. Certainty of evidence was evaluated following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool. The search process yielded 7696 initial results, from which we identified 88 studies to include in the review set. Climatic factors reported included air temperature, solar radiation/sunshine, barometric pressure, precipitation, relative humidity, wind direction/speed, and thermal index. Outcomes including MBD incidences (e.g., schizophrenia, mood disorders, neurotic disorders), mental health-related mortality, and self-reported psychological states. Meta-analysis showed that heatwaves (pooled RR = 1.05, 95 % CI = 1.02-1.08) and extreme high temperatures (99th percentile: pooled RR = 1.18, 95 % CI = 1.08-1.29) were associated with higher risk of MBD. Cold extremes, however, were not associated with MBD risk. The findings further identified an association between increases in a thermal index (i.e., apparent temperature) and elevated risk of MBD (pooled RR = 1.06, 95 % CI = 1.03-1.12); specifically, a 99th percentile high temperature was associated with increased schizophrenia risk (pooled RR = 1.07, 95 % CI = 1.01-1.12). Risk of bias assessment showed most studies to have low or moderately low risks, while a few studies were rated probably high in confounding, selection bias, outcome measurement, and reporting bias. GRADE evaluation revealed moderate certainty of evidence on thermal comfort index and MBD, but low certainty related to air temperature or sunshine duration. These findings call attention to the heterogeneity of exposure measures and the utility of thermal indices that consider the synergistic effects of meteorological factors. Methodological concerns such as the linearity assumption and cumulative effects are discussed.
Topics: Humans; Mental Disorders; Mental Health; Selection Bias; Hot Temperature; Risk
PubMed: 37257626
DOI: 10.1016/j.scitotenv.2023.164435 -
Human Reproduction (Oxford, England) Jan 2018
Topics: Bias; Female; Humans; Publication Bias; Selection Bias
PubMed: 29165671
DOI: 10.1093/humrep/dex346 -
Fertility and Sterility Dec 2020
Topics: COVID-19; Humans; Pandemics; Parents; SARS-CoV-2; Selection Bias
PubMed: 33280724
DOI: 10.1016/j.fertnstert.2020.10.057 -
Journal of the American Geriatrics... Sep 2019Selection bias is a well-known concern in research on older adults. We discuss two common forms of selection bias in aging research: (1) survivor bias and (2) bias due... (Review)
Review
OBJECTIVES
Selection bias is a well-known concern in research on older adults. We discuss two common forms of selection bias in aging research: (1) survivor bias and (2) bias due to loss to follow-up. Our objective was to review these two forms of selection bias in geriatrics research. In clinical aging research, selection bias is a particular concern because all participants must have survived to old age, and be healthy enough, to take part in a research study in geriatrics.
DESIGN
We demonstrate the key issues related to selection bias using three case studies focused on obesity, a common clinical risk factor in older adults. We also created a Selection Bias Toolkit that includes strategies to prevent selection bias when designing a research study in older adults and analytic techniques that can be used to examine, and correct for, the influence of selection bias in geriatrics research.
RESULTS
Survivor bias and bias due to loss to follow-up can distort study results in geriatric populations. Key steps to avoid selection bias at the study design stage include creating causal diagrams, minimizing barriers to participation, and measuring variables that predict loss to follow-up. The Selection Bias Toolkit details several analytic strategies available to geriatrics researchers to examine and correct for selection bias (eg, regression modeling and sensitivity analysis).
CONCLUSION
The toolkit is designed to provide a broad overview of methods available to examine and correct for selection bias. It is specifically intended for use in the context of aging research. J Am Geriatr Soc 67:1970-1976, 2019.
Topics: Aged; Aged, 80 and over; Female; Geriatrics; Humans; Lost to Follow-Up; Male; Patient Selection; Research Design; Selection Bias; Survivors
PubMed: 31211407
DOI: 10.1111/jgs.16022 -
Epidemiology (Cambridge, Mass.) Sep 2021Confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own...
Confounding, selection bias, and measurement error are well-known sources of bias in epidemiologic research. Methods for assessing these biases have their own limitations. Many quantitative sensitivity analysis approaches consider each type of bias individually, although more complex approaches are harder to implement or require numerous assumptions. By failing to consider multiple biases at once, researchers can underestimate-or overestimate-their joint impact. We show that it is possible to bound the total composite bias owing to these three sources and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases. We derive bounds for the total composite bias under a variety of scenarios, providing researchers with tools to assess their total potential impact. We apply this technique to a study where unmeasured confounding and selection bias are both concerns and to another study in which possible differential exposure misclassification and confounding are concerns. The approach we describe, though conservative, is easier to implement and makes simpler assumptions than quantitative bias analysis. We provide R functions to aid implementation.
Topics: Bias; Confounding Factors, Epidemiologic; Epidemiologic Studies; Humans; Research Design; Selection Bias
PubMed: 34224471
DOI: 10.1097/EDE.0000000000001380 -
The Journal of Thoracic and... Apr 2020
Topics: Humans; Lung Neoplasms; Mesothelioma; Selection Bias
PubMed: 32035644
DOI: 10.1016/j.jtcvs.2019.11.083