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Journal of Clinical Epidemiology Feb 2022Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questions in clinical and epidemiologic research and inform study design...
Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questions in clinical and epidemiologic research and inform study design and statistical analysis. DAGs are constructed to depict prior knowledge about biological and behavioral systems related to specific causal research questions. DAG components portray who receives treatment or experiences exposures; mechanisms by which treatments and exposures operate; and other factors that influence the outcome of interest or which persons are included in an analysis. Once assembled, DAGs - via a few simple rules - guide the researcher in identifying whether the causal effect of interest can be identified without bias and, if so, what must be done either in study design or data analysis to achieve this. Specifically, DAGs can identify variables that, if controlled for in the design or analysis phase, are sufficient to eliminate confounding and some forms of selection bias. DAGs also help recognize variables that, if controlled for, bias the analysis (e.g., mediators or factors influenced by both exposure and outcome). Finally, DAGs help researchers recognize insidious sources of bias introduced by selection of individuals into studies or failure to completely observe all individuals until study outcomes are reached. DAGs, however, are not infallible, largely owing to limitations in prior knowledge about the system in question. In such instances, several alternative DAGs are plausible, and researchers should assess whether results differ meaningfully across analyses guided by different DAGs and be forthright about uncertainty. DAGs are powerful tools to guide the conduct of clinical research.
Topics: Bias; Causality; Confounding Factors, Epidemiologic; Data Interpretation, Statistical; Humans; Selection Bias
PubMed: 34371103
DOI: 10.1016/j.jclinepi.2021.08.001 -
Journal of Human Lactation : Official... May 2020Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews...
Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration. In addition, issues related to sampling methods are described to highlight potential problems.
Topics: Humans; Patient Selection; Research Design; Selection Bias
PubMed: 32155099
DOI: 10.1177/0890334420906850 -
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 -
The Cochrane Database of Systematic... Oct 2020Respiratory distress, particularly respiratory distress syndrome (RDS), is the single most important cause of morbidity and mortality in preterm infants. In infants with... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Respiratory distress, particularly respiratory distress syndrome (RDS), is the single most important cause of morbidity and mortality in preterm infants. In infants with progressive respiratory insufficiency, intermittent positive pressure ventilation (IPPV) with surfactant has been the usual treatment, but it is invasive, potentially resulting in airway and lung injury. Continuous positive airway pressure (CPAP) has been used for the prevention and treatment of respiratory distress, as well as for the prevention of apnoea, and in weaning from IPPV. Its use in the treatment of RDS might reduce the need for IPPV and its sequelae.
OBJECTIVES
To determine the effect of continuous distending pressure in the form of CPAP on the need for IPPV and associated morbidity in spontaneously breathing preterm infants with respiratory distress.
SEARCH METHODS
We used the standard strategy of Cochrane Neonatal to search CENTRAL (2020, Issue 6); Ovid MEDLINE and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Daily and Versions; and CINAHL on 30 June 2020. We also searched clinical trials databases and the reference lists of retrieved articles for randomised controlled trials and quasi-randomised trials.
SELECTION CRITERIA
All randomised or quasi-randomised trials of preterm infants with respiratory distress were eligible. Interventions were CPAP by mask, nasal prong, nasopharyngeal tube or endotracheal tube, compared with spontaneous breathing with supplemental oxygen as necessary.
DATA COLLECTION AND ANALYSIS
We used standard methods of Cochrane and its Neonatal Review Group, including independent assessment of risk of bias and extraction of data by two review authors. We used the GRADE approach to assess the certainty of evidence. Subgroup analyses were planned on the basis of birth weight (greater than or less than 1000 g or 1500 g), gestational age (groups divided at about 28 weeks and 32 weeks), timing of application (early versus late in the course of respiratory distress), pressure applied (high versus low) and trial setting (tertiary compared with non-tertiary hospitals; high income compared with low income) MAIN RESULTS: We included five studies involving 322 infants; two studies used face mask CPAP, two studies used nasal CPAP and one study used endotracheal CPAP and continuing negative pressure for a small number of less ill babies. For this update, we included one new trial. CPAP was associated with lower risk of treatment failure (death or use of assisted ventilation) (typical risk ratio (RR) 0.64, 95% confidence interval (CI) 0.50 to 0.82; typical risk difference (RD) -0.19, 95% CI -0.28 to -0.09; number needed to treat for an additional beneficial outcome (NNTB) 6, 95% CI 4 to 11; I = 50%; 5 studies, 322 infants; very low-certainty evidence), lower use of ventilatory assistance (typical RR 0.72, 95% CI 0.54 to 0.96; typical RD -0.13, 95% CI -0.25 to -0.02; NNTB 8, 95% CI 4 to 50; I = 55%; very low-certainty evidence) and lower overall mortality (typical RR 0.53, 95% CI 0.34 to 0.83; typical RD -0.11, 95% CI -0.18 to -0.04; NNTB 9, 95% CI 2 to 13; I = 0%; 5 studies, 322 infants; moderate-certainty evidence). CPAP was associated with increased risk of pneumothorax (typical RR 2.48, 95% CI 1.16 to 5.30; typical RD 0.09, 95% CI 0.02 to 0.16; number needed to treat for an additional harmful outcome (NNTH) 11, 95% CI 7 to 50; I = 0%; 4 studies, 274 infants; low-certainty evidence). There was no evidence of a difference in bronchopulmonary dysplasia, defined as oxygen dependency at 28 days (RR 1.04, 95% CI 0.35 to 3.13; I = 0%; 2 studies, 209 infants; very low-certainty evidence). The trials did not report use of surfactant, intraventricular haemorrhage, retinopathy of prematurity, necrotising enterocolitis and neurodevelopment outcomes in childhood.
AUTHORS' CONCLUSIONS
In preterm infants with respiratory distress, the application of CPAP is associated with reduced respiratory failure, use of mechanical ventilation and mortality and an increased rate of pneumothorax compared to spontaneous breathing with supplemental oxygen as necessary. Three out of five of these trials were conducted in the 1970s. Therefore, the applicability of these results to current practice is unclear. Further studies in resource-poor settings should be considered and research to determine the most appropriate pressure level needs to be considered.
Topics: Bronchopulmonary Dysplasia; Continuous Positive Airway Pressure; Humans; Infant, Low Birth Weight; Infant, Newborn; Infant, Premature; Intermittent Positive-Pressure Ventilation; Outcome Assessment, Health Care; Pneumothorax; Pulmonary Surfactants; Randomized Controlled Trials as Topic; Respiratory Distress Syndrome, Newborn; Respiratory Insufficiency; Selection Bias; Treatment Failure
PubMed: 33058208
DOI: 10.1002/14651858.CD002271.pub3 -
Journal of the American Academy of... Jun 2021
PubMed: 34153389
DOI: 10.1016/j.jaad.2021.06.025 -
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 -
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 -
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 -
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