A funnel plot is a graph designed to check for the existence of publication bias; funnel plots are commonly used in systematic reviews and meta-analyses. In the absence of publication bias, it assumes that studies with high precision will be plotted near the average, and studies with low precision will be spread evenly on both sides of the average, creating a roughly funnel-shaped distribution. Deviation from this shape can indicate publication bias.
Funnel plots, introduced by Light and Pillemer in 1984 [1] and discussed in detail by Matthias Egger and colleagues, [2] [3] are useful adjuncts to meta-analyses. A funnel plot is a scatterplot of treatment effect against a measure of study precision. It is used primarily as a visual aid for detecting bias or systematic heterogeneity. A symmetric inverted funnel shape arises from a ‘well-behaved’ data set, in which publication bias is unlikely. An asymmetric funnel indicates a relationship between treatment effect estimate and study precision. This suggests the possibility of either publication bias or a systematic difference between studies of higher and lower precision (typically ‘small study effects’). Asymmetry can also arise from use of an inappropriate effect measure. Whatever the cause, an asymmetric funnel plot leads to doubts over the appropriateness of a simple meta-analysis and suggests that there needs to be investigation of possible causes.
A variety of choices of measures of ‘study precision’ is available, including total sample size, standard error of the treatment effect, and inverse variance of the treatment effect (weight). Sterne and Egger have compared these with others, and conclude that the standard error is to be recommended. [3] When the standard error is used, straight lines may be drawn to define a region within which 95% of points might lie in the absence of both heterogeneity and publication bias. [3]
In common with confidence interval plots, funnel plots are conventionally drawn with the treatment effect measure on the horizontal axis, so that study precision appears on the vertical axis, breaking with the general rule. Since funnel plots are principally visual aids for detecting asymmetry along the treatment effect axis, this makes them considerably easier to interpret.
The funnel plot is not without problems. If high-precision studies are different from low-precision studies with respect to effect size (e.g., due to different populations examined) a funnel plot may give a wrong impression of publication bias. [4] The appearance of the funnel plot can change quite dramatically depending on the scale on the y-axis — whether it is the inverse square error or the trial size. [5] Researchers have a poor ability to visually discern publication bias from funnel plots. [6]
Evidence-based medicine (EBM) is "the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients. ... [It] means integrating individual clinical expertise with the best available external clinical evidence from systematic research." The aim of EBM is to integrate the experience of the clinician, the values of the patient, and the best available scientific information to guide decision-making about clinical management. The term was originally used to describe an approach to teaching the practice of medicine and improving decisions by individual physicians about individual patients.
Meta-analysis is a method of synthesis of quantitative data from multiple independent studies addressing a common research question. An important part of this method involves computing a combined effect size across all of the studies. As such, this statistical approach involves extracting effect sizes and variance measures from various studies. By combining these effect sizes the statistical power is improved and can resolve uncertainties or discrepancies found in individual studies. Meta-analyses are integral in supporting research grant proposals, shaping treatment guidelines, and influencing health policies. They are also pivotal in summarizing existing research to guide future studies, thereby cementing their role as a fundamental methodology in metascience. Meta-analyses are often, but not always, important components of a systematic review.
A placebo is a substance or treatment which is designed to have no therapeutic value. Common placebos include inert tablets, inert injections, sham surgery, and other procedures.
A randomized controlled trial is a form of scientific experiment used to control factors not under direct experimental control. Examples of RCTs are clinical trials that compare the effects of drugs, surgical techniques, medical devices, diagnostic procedures, diets or other medical treatments.
Cochrane is a British international charitable organisation formed to synthesize medical research findings to facilitate evidence-based choices about health interventions involving health professionals, patients and policy makers. It includes 53 review groups that are based at research institutions worldwide. Cochrane has over 37,000 volunteer experts from around the world.
In a blind or blinded experiment, information which may influence the participants of the experiment is withheld until after the experiment is complete. Good blinding can reduce or eliminate experimental biases that arise from a participants' expectations, observer's effect on the participants, observer bias, confirmation bias, and other sources. A blind can be imposed on any participant of an experiment, including subjects, researchers, technicians, data analysts, and evaluators. In some cases, while blinding would be useful, it is impossible or unethical. For example, it is not possible to blind a patient to their treatment in a physical therapy intervention. A good clinical protocol ensures that blinding is as effective as possible within ethical and practical constraints.
In published academic research, publication bias occurs when the outcome of an experiment or research study biases the decision to publish or otherwise distribute it. Publishing only results that show a significant finding disturbs the balance of findings in favor of positive results. The study of publication bias is an important topic in metascience.
A systematic review is a scholarly synthesis of the evidence on a clearly presented topic using critical methods to identify, define and assess research on the topic. A systematic review extracts and interprets data from published studies on the topic, then analyzes, describes, critically appraises and summarizes interpretations into a refined evidence-based conclusion. For example, a systematic review of randomized controlled trials is a way of summarizing and implementing evidence-based medicine.
In epidemiology, reporting bias is defined as "selective revealing or suppression of information" by subjects. In artificial intelligence research, the term reporting bias is used to refer to people's tendency to under-report all the information available.
A forest plot, also known as a blobbogram, is a graphical display of estimated results from a number of scientific studies addressing the same question, along with the overall results. It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials. In the last twenty years, similar meta-analytical techniques have been applied in observational studies and forest plots are often used in presenting the results of such studies also.
In homeopathy, arsenicum album (Arsenic. alb.) is a solution prepared by diluting aqueous arsenic trioxide generally until there is little amounts of Arsenic remaining in individual doses. It is used by homeopaths to treat a range of symptoms that include digestive disorders and, as an application of the Law of Similars, has been suggested by homeopathy as a treatment for arsenic poisoning. Since the arsenic oxide in a homeopathic preparation is normally non-existent, it is considered generally safe, although cases of arsenic poisoning from poorly prepared homeopathic treatments sold in India have been reported. When properly prepared, however, the extreme dilutions, typically to at least 1 in 1024, or 12C in homeopathic notation, mean that a pill would not contain even a molecule of the original arsenic used. While Anisur Khuda-Bukhsh's unblinded studies have claimed an effect on reducing arsenic toxicity, they do not recommend its large-scale use, and studies of homeopathic remedies have been shown to generally have problems that prevent them from being considered unambiguous evidence. There is no known mechanism for how arsenicum album could remove arsenic from a body, and there is insufficient evidence for it to be considered effective medicine (for any condition) by the scientific community.
A plot is a graphical technique for representing a data set, usually as a graph showing the relationship between two or more variables. The plot can be drawn by hand or by a computer. In the past, sometimes mechanical or electronic plotters were used. Graphs are a visual representation of the relationship between variables, which are very useful for humans who can then quickly derive an understanding which may not have come from lists of values. Given a scale or ruler, graphs can also be used to read off the value of an unknown variable plotted as a function of a known one, but this can also be done with data presented in tabular form. Graphs of functions are used in mathematics, sciences, engineering, technology, finance, and other areas.
The Jadad scale, sometimes known as Jadad scoring or the Oxford quality scoring system, is a procedure to assess the methodological quality of a clinical trial by objective criteria. It is named after Canadian-Colombian physician Alex Jadad who in 1996 described a system for allocating such trials a score of between zero and five (rigorous). It is the most widely used such assessment in the world, and as of May 2024, its seminal paper has been cited in over 24,500 scientific works.
John P. A. Ioannidis is a Greek-American physician-scientist, writer and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. Ioannidis studies scientific research itself - in other words, meta-research - primarily in clinical medicine and the social sciences.
Critical appraisal in evidence based medicine, is the use of explicit, transparent methods to assess the data in published research, applying the rules of evidence to factors such as internal validity, adherence to reporting standards, conclusions, generalizability and risk-of-bias. Critical appraisal methods form a central part of the systematic review process. They are used in evidence synthesis to assist clinical decision-making, and are increasingly used in evidence-based social care and education provision.
In medical testing with binary classification, the diagnostic odds ratio (DOR) is a measure of the effectiveness of a diagnostic test. It is defined as the ratio of the odds of the test being positive if the subject has a disease relative to the odds of the test being positive if the subject does not have the disease.
Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. It complements hypothesis testing approaches such as null hypothesis significance testing (NHST), by going beyond the question is an effect present or not, and provides information about how large an effect is. Estimation statistics is sometimes referred to as the new statistics.
Matthias Egger is professor of epidemiology and public health at the University of Bern in Switzerland, as well as professor of clinical epidemiology at the University of Bristol in the United Kingdom.
Allegiance bias in behavioral sciences is a bias resulted from the investigator's or researcher's allegiance to a specific school of thought. Researchers/investigators have been exposed to many types of branches of psychology or schools of thought. Naturally they adopt a school or branch that fits with their paradigm of thinking. More specifically, allegiance bias is when this leads therapists, researchers, etc. believing that their school of thought or treatment is superior to others. Their superior belief to these certain schools of thought can bias their research in effective treatments trials or investigative situations leading to allegiance bias. Reason being is that they may have devoted their thinking to certain treatments they have seen work in their past experiences. This can lead to errors in interpreting the results of their research. Their “pledge” to stay within their own paradigm of thinking may affect their ability to find more effective treatments to help the patient or situation they are investigating.
Jonathan A.C. Sterne is a British statistician, NIHR Senior Investigator, Professor of Medical Statistics and Epidemiology, and the former Head of School of Social and Community Medicine at the University of Bristol. He is co-author of “Essential Medical Statistics”, which received Highly Commended honors in the 2004 BMA Medical Book Competition.
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