Learn some useful tips on writing an academic article. What are your tips for beginners? Add your comments to the blog.
Understanding contextual factors is important for healthcare workers as these factors significantly influence patient outcomes. Learn what they are, and why they are important, in this blog for beginners to the topic.
Statistics can help you understand a dataset. But first up, you need to know the difference between descriptive and inferential methods.
Empowering the patient to take an active role in their health decision-making is a vital aspect of evidence-based healthcare. Explore more on this topic in this latest blog.
In this blog you will be able to understand the basic concepts about composite endpoints, their limitations and benefits, as well as an approach to how to interpret them.
What are the differences between a systematic review and a meta-analysis? Here are some tips to help you understand these two different yet related types of study.
This blog introduces you to funnel plots, guiding you through how to read them and what may cause them to look asymmetrical.
When you bring studies together in a meta-analysis, one of the things you need to consider is the variability in your studies – this is called heterogeneity. This blog presents the three types of heterogeneity, considers the different types of outcome data, and delves a little more into dealing with the variations.
Is this study valid? Can I trust this study’s methods and design? Can I apply the results of this study to other contexts? Learn more about internal and external validity in research to help you answer these questions when you next look at a paper.
Evidence gap maps (EGMs) are graphic representations of the available systematic reviews and ongoing research on relevant topics. Learn more, and test your understanding, in this blog for beginners to the topic.
Rapid reviews are often driven by requests for timely evidence for decision-making purposes. This blog is a brief introduction to the topic.
A living systematic review is a type of review which is continually updated, incorporating relevant new evidence as it becomes available.
This tutorial provides an introduction to statistical averages (mean, median and mode) for beginners to the topic.
This blog summarizes the concepts of cluster randomization, and the logistical and statistical considerations while designing a cluster randomized controlled trial.
What is a non-inferiority trial and how do patient, drug, and study design characteristics influence decisions of the non-inferiority threshold and clinical decisions.
This blog summarizes the concepts of Expertise-based randomized controlled trials with a focus on the advantages and challenges associated with this type of study.
Measures of variability are statistical tools that help us assess data variability by informing us about the quality of a dataset mean. This second of two blogs on the topic will look at coefficient of variation and the z-score.
Measures of variability are statistical tools that help us assess data variability by informing us about the quality of a dataset mean. This first of two blogs on the topic will cover basic concepts of range, standard deviation, and variance.
A tutorial for understanding and calculating probability. We go back to basics for beginners or for those just wanting a refresher.
Shared decision making (SDM) is a collaborative process between physician and patient that is applicable to any clinical decision, whether diagnostic, therapeutic, or preventive in nature. In this blog, Tiffany presents why there is an ethical imperative for SDM to be routinely incorporated into everyday patient care within all healthcare settings.
This is the third in a three-part blog which will look at a few aspects of the topic in more detail like the cost-effectiveness plane, discount rates, and other key elements in health economic evaluation.
The CONSORT statement aims at comprehensive and complete reporting of randomized controlled trials. This blog introduces you to the statement and why it is an important tool in the research world.
In the second of a series of blogs about economic analysis, Ana María explains the common techniques used in economic evaluation.
Learn more about the measures of central tendency (mean, mode, median) and how these need to be critically appraised when reading a paper.
In the first of a series of blogs about economic analysis, Ana María introduces us to the topic and why it is needed.
In this blog, Vighnesh provides an outline of multivariate analysis for beginners to this topic.
What is data dredging, how does it affect the p-value and what is its impact on the world around us?
What is grey literature, when would you use it, what are its advantages and disadvantages, and how can you find it?
Conducting successful research requires choosing the appropriate study design. This article describes the most common types of designs conducted by researchers.
Carrying out a literature search can feel daunting when faced with the task. This blog introduces you to the main databases available to enable a comprehensive search of the medical literature.
What are adverse events? Why is the recording and reporting of adverse events necessary?
The PROGRESS acronym is designed to remind researchers, and others, to consider the factors which may affect health opportunities and outcomes.
Participants in clinical trials may exit the study prior to having their results collated; in this case, what do we do with their results?
Learn about the different types of sampling methods, examples of their uses, and potential sampling errors to avoid when conducting research.
A brief guide to prevalence and incidence with definitions, explanations and example calculations.
When you see a claim that a treatment or intervention has no effect, it is important to examine the evidence as this may be a misleading statement.
A brief introduction and tips for students embarking on a rapid review, when they should be used, and their advantages and limitations.
Vaccine trials have to go through a rigorous testing process before being released for use. This topic is particularly relevant as vaccine developers aim to deliver a SARS-CoV2 vaccine to the population in record time.
This blog introduces you to crossover trials with a clear explanation and example, together with some advantages and limitations of this study design.
This blog introduces you to standardised mortality ratios. What are they, why are they used, how do you calculate them and what are their advantages and limitations.
Yousif examines the HIP ATTACK trial, appraises the primary composite endpoint, verifies the assessment and then thinks of ways to interpret the result.
This blog provides an introduction to critically appraising diagnostic studies. Find out what questions are important to ask as you go through a paper.
This blog is an introduction to Research Priority Setting (RSP), also providing useful resources to understand how RSP exercises are carried out.
In the last of a series of three blogs about Thematic analysis (TA), Dolly Sud describes the six phases of TA and provides further reading and conclusions.
In the second of a series of three blogs about Thematic analysis (TA), Dolly Sud describes the 3 schools of TA and discusses some study design recommendations.
In the first of a series of three blogs about Thematic analysis, Dolly Sud introduces us to the topic and explains what a ‘theme’ is.
The blog explains what we mean by – and how to calculate – ‘sensitivity’, ‘specificity’, ‘positive predictive value’ and ‘negative predictive value’ in the context of diagnosing disease.
Carrying out a systematic, unbiased, transparent and reliable literature search is vital in the first stages of your research. This blog provides tips and useful information on which resources can help guide you in this process.
The GRADE-CERQual approach is a transparent method of assessing the confidence of evidence from reviews of qualitative research. This blog from Dolly Sud introduces this type of assessment and provides useful further reading and resources.
This blog provides a detailed explanation of a dyad (something that consists of two elements or parts) and how this is used within the context of healthcare research.
What is ‘Responder analysis’ and what are the benefits and limitations of this approach? Read more in this blog from Giorgio Karam.
A well-designed cohort study can provide powerful results. This blog introduces prospective and retrospective cohort studies, discussing the advantages, disadvantages and use of these type of study designs.
Randomized controlled trials (RCTs) can be subject to different kinds of bias. Read about different sources of bias in this blog and how much the magnitude of effect can be changed by the presence of bias.
This is a Portuguese translation of the tenth in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Vinicius Fiuza and Cochrane Brazil for the translation.
Hope can be a good thing, but sometimes people in need or desperation hope that treatments will work and assume they cannot do any harm. Similarly, fear can lead people to use treatments that may not work and can cause harm. As a result, they may waste time and money on treatments that have never been shown to be useful, or may actually cause harm.
This blog examines what heterogeneity is, why it matters, how you can identify and measure it and how you can then deal with it.
The growth in implementation science and research represents a growing recognition that successful dissemination and implementation is an essential part of evidence-based practice.
GRADE (Grading of Recommendations, Assessment, Development and Evaluation) is a prominent framework for evaluating the effectiveness of systematic reviews. This blog provides detail of the GRADE approach with useful links to further reading on this key process.
This blog introduces you to the concept of confounding. There is a clear explanation and then examples and methods to minimise the effect of confounding during study design and statistical analysis.
A beginner’s guide to standard deviation and standard error: what are they, how are they different and how do you calculate them?
This is a Portuguese translation of the ninth in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Ney Duarte Neto and Cochrane Brazil for the translation.
This blog explains that people often assume that early detection of disease leads to better outcomes. However screening tests can be inaccurate (e.g. misclassifying people who do not have disease as having disease). Screening can also cause harm by labelling people as being sick when they are not and because of side effects of the tests and treatments.
Carryover effects can affect outcomes and results of research, and are important to consider, particularly in the design phase of a study.
This is a Portuguese translation of the eighth in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Marília Ferreira and Cochrane Brazil for the translation.
This blog explains that increasing the dose or amount of a treatment (e.g. how many vitamin pills you take) often increases harms without increasing beneficial effects. If a treatment is believed to be beneficial, we should not assume that more of it is better.
Publication bias remains a problem in health research. This blog by Andrés explores the issues we face and provides detail of the initiatives designed to address the problem.
Informed Health Choices (IHC) ‘Key Concepts’ videos in German (3.1 – 3.6). These set of key concepts help people to make informed choices about treatments
Informed Health Choices (IHC) ‘Key Concepts’ videos in German (2.1 – 2.18). These set of key concepts help people understand whether comparisons of treatments are fair and reliable
Informed Health Choices (IHC) ‘Key Concepts’ videos in German (concepts 1.1 – 1.12). These set of key concepts help people to recognise an unreliable basis for treatment claims.
We are delighted to introduce the German translations of the Informed Health Choices (IHC) ‘Key Concepts’ videos. Thank you to 4th year Physiotherapy students from the University of hochschule 21 and Cordula Braun.
This is a Portuguese translation of the seventh in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Lucas Mello Netto and Cochrane Brazil for the translation.
This blog discusses how people with an interest in promoting a treatment (in addition to wanting to help people), such as making money, may promote treatments by exaggerating benefits and ignoring potential harmful effects. Conversely, people may be opposed to a treatment for a range of reasons, such as cultural practices.
This is a Portuguese translation of the sixth in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Leonardo Guimarães Fernandes and Cochrane Brazil for the translation.
This blog explains that doctors, researchers, patient organisations and other authorities often disagree about the effects of treatments. It explains why we should not rely on the opinions of experts or other authorities about the effects of treatments, unless they clearly base their opinions on the findings of systematic reviews of fair comparisons of treatments.
A Finnish translation (thank you to Eveliina Ilola) of the nuts and bolts 20 minute tutorial from Tim Hicks: A beginner’s guide to interpreting odds ratios, confidence intervals and p-values.
This is the final blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Decisions about whether or not to use a treatment should be informed by the balance between the potential benefits and the potential harms, costs and other advantages and disadvantages of the treatment.
This is the thirty-fifth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
The certainty of the evidence (the extent to which the research provides a good indication of the likely effects of treatments) can affect the treatment decisions people make. For example, someone might decide not to use or to pay for a treatment if the certainty of the evidence is low or very low.
This is the thirty-fourth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Comparisons designed to evaluate whether a treatment can work under ideal circumstances may not reflect what you can expect under usual circumstances.
This is a Portuguese translation of the fifth in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Henrique Pinho Volker and Cochrane Brazil for the translation.
This blog explains that new treatments are often assumed to be better simply because they are new or because they are more expensive. However, they are only very slightly likely to be better than other available treatments.
This is the thirty-third blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Are the treatments practical in your setting? Be aware that treatments available to you may be sufficiently different from those in the research studies that the results may not apply to you.
This is the thirty-second blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Are you very different from the people studied? Systematic reviews of studies that only include animals or a selected minority of people may not provide results that are relevant to most people. This may be misleading.
This is the thirty-first blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
A systematic review of fair comparisons of treatments should measure outcomes that are important. A fair comparison may not include all outcomes that are relevant to treatments. Patients, professionals and researchers may have different views about which outcomes are important.
This is the thirtieth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Systematic reviews sometimes conclude that there is “no evidence of a difference” when there is uncertainty about the difference between two treatments. This is often misinterpreted as meaning that there is “no difference” between the treatments compared.
This is the twenty-ninth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
“Statistical significance” is often confused with “importance”. The cut-off for considering a result as statistically significant is arbitrary, and statistically non-significant results can be either informative or inconclusive.
This is a Portuguese translation of the fourth in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Guilherme de Mattos Queiroz and Cochrane Brazil for the translation.
This blog explains that treatments that have not been properly evaluated but are widely used or have been used for a long time are often assumed to work. Sometimes, however, they may be unsafe or of doubtful benefit.
This is the twenty-eighth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
The confidence interval is the range within which the ‘true difference’ is likely to lie, after taking into account the play of chance. Whenever possible, consider confidence intervals when assessing estimates of treatment effects. Do not be misled by p-values.
This is the twenty-seventh blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
When there are only few outcome events, differences in outcome frequencies between the treatment comparison groups may easily have occurred by chance and may mistakenly be attributed to differences between the treatments.
Blinding is a common element used in rigorously designed trials. Most people are familiar with the general concept but what is its purpose and what is the best way to perform it? This blog by Neelam Khan explores both of these questions and discusses ways to tackle situations where blinding cannot be done.
This is the twenty-sixth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that for outcomes measured on a scale (e.g. weight, or pain) the difference between the average in one treatment group and the average in a comparison group may not make it clear how many people experienced a big enough change (e.g. in weight or pain) for them to notice it, or that they would regard as important.
This is a Portuguese translation of the third in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Izabel Oliveira and Cochrane Brazil for the translation.
This blog explains that the fact that a treatment outcome (i.e. a potential benefit or harm) is associated with a treatment does not mean that the treatment caused the outcome.
This is the twenty-fifth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Relative effects (e.g. the ratio of the probability of an outcome in one treatment group compared with that in a comparison group) are insufficient for judging the importance of the difference (between the frequencies of the outcome). A relative effect may give the impression that a difference is larger than it actually is when the likelihood of the outcome
is small to begin with.
Healthcare guidelines are an invaluable aspect of evidence-based healthcare. This blog by Neelam sheds some light on what a Guideline is, and what is isn’t.
This is the twenty-fourth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Comparisons of treatments often report results for selected groups of participants in an effort to assess whether the effect of a treatment is different for different types of people (e.g. men and women or different age groups). These analyses are often poorly planned and reported. Most differential effects suggested by these “subgroup results” are likely to be due to the play of chance and are unlikely to reflect true differences.
This is the twenty-third blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Many fair comparisons never get published, and outcomes are sometimes left out. Those that do get published are more likely to report favourable results. As a consequence, reliance on published reports sometimes results in the beneficial effects of treatments being overestimated and the adverse effects being underestimated.
This blog by Saul looks at the Audit Cycle: What is an audit, how does it differ from research, the steps involved in the audit process and how you can get involved.
This is the twenty-second blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Even though a comparison of treatments has been published in a prestigious journal, it may not be a fair comparison and the results may not be reliable. Peer review (assessment of a study by others working in the same field) does not guarantee that published studies are reliable. Assessments vary and may not be systematic.
This is a Portuguese translation of the second in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Felipe Medauar and Cochrane Brazil for the translation.
This blog explains that claims about the effects of a treatment may be misleading if they are based on stories about how a treatment helped individual people, or if those stories attribute improvements to treatments that have not been assessed in systematic reviews of fair comparisons.
This is the twenty-first blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
Reviews that do not use systematic methods may result in biased or imprecise estimates of the effects of treatments because the selection of studies for inclusion may be biased or the methods may result in some studies not being found. In addition, the appraisal of some studies may be biased, or the synthesis of the results of the selected studies may be inadequate or inappropriate.
This is the twentieth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
A single comparison of treatments rarely provides conclusive evidence and results are often available from other comparisons of the same treatments. These other comparisons may have different results or may help to provide more reliable and precise estimates of the effects of treatments.
This is a Portuguese translation of the first in a series of 36 blogs explaining 36 Key Concepts we need to be able to understand to think critically about treatment claims. With thanks to Carlos Henrique dos Santos Oliveira and Cochrane Brazil for the translation. This blog explains that people often exaggerate the benefits of treatments and ignore or downplay potential harms. However, few effective treatments are 100% safe. Always consider the possibility that a treatment may have harmful effects.
Saul Crandon provides an overview of Case-control and Cohort studies: what are they, how are they different, and what are the pros and cons you need to consider in each study design.
In this blog, Sasha Lawson-Frost explores what moral values underpin or justify the practice of Evidence-Based Medicine, specifically in response to a recent article which stated “the policy side of evidence-based medicine is basically a form of rule utilitarianism”.
In this blog, Leonardo provides 5 interpretations that you should consider when you read or hear about a reported association in observational studies.
This is the nineteenth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
People in treatment comparisons who are not followed up to the end of the study may have worse outcomes than those who are followed up. For example, they may have dropped out because the treatment was not working or because of side effects. If those people are excluded, the findings of the study may be misleading.
This blog provides a detailed overview of the Delphi Technique, a method of congregating expert opinion through a series of iterative questionnaires, with a goal of coming to a group consensus. It covers what it is, the process involved, pros and cons and when you would consider using it.
This is the eighteenth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.
If an outcome is measured differently in two comparison groups, differences in that outcome may be due to how the outcome was measured rather than because of the treatment received by people in each group. For example, if outcome assessors believe that a particular treatment works and they know which patients have received that treatment, they may be more likely to observe better outcomes in those who have received the treatment.
Tarang Sharma was lead-author of a recent article entitled “The Yusuf-Peto method was not a robust method for meta-analyses of rare events data from antidepressant trials”. In this blog, Tarang gives more details about meta-analysis methods of rare events and sparse data, and why these can lead to misleading results.
This blog, written by Leonard Goh, was the winner of Cochrane Malaysia and Penang Medical College’s recent evidence-based medicine blog writing competition. Leonard has written an insightful and informative piece to answer the question: ‘Evidence-based health practice: a fairytale or reality’.
This is the seventeenth blog in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
People in a treatment group may experience improvements (for example, less pain) because they believe they are receiving a better treatment, even if the treatment is not actually better (this is called a placebo effect), or because they behave differently (due to knowing which treatment they received, compared to how they otherwise would have behaved). If individuals know that they are receiving (they are not “blinded” to) a treatment that they believe is better, some or all of the apparent effects of the treatment may be due either to a placebo effect or because the recipients behaved differently.
This is the sixteenth blog in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
Apart from the treatments being compared, people in the treatment comparison groups should otherwise receive similar care. If, for example, people in one group receive more attention and care than people in the comparison group, differences in outcomes could be due to differences in the amount of attention each group received rather than due to the treatments that are being compared. One way of preventing this is to keep providers unaware (“blind”) of which people have been allocated to which treatment.
This blog follows on from Ammar’s previous post on meta-analysis, and provides further details on the history, value and implementation of this approach.
This is the fifteenth blog in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains how randomized allocation helps to ensure that the groups have similar characteristics. However, people sometimes do not receive or take the allocated treatments. The characteristics of such people often differ from those who do take the treatment as allocated. Therefore, excluding from the analysis people who did not receive the allocated treatment may mean that like is no longer being compared with like.
This blog provides an introduction to sample size and power; what it is, why it’s important to consider when designing a study, and how to carry out a power calculation.
This is the fourteenth blog in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that if people in the treatment comparison groups differ in ways other than the treatments being compared, the apparent effects of the treatments might reflect those differences rather than actual treatment effects. A method such as allocating people to different treatments by assigning them random numbers (the equivalent of flipping a coin) is the best way to ensure that the groups being compared are similar in terms of both measured and unmeasured characteristics.
This blogs provides an overview of linear regression. It is suitable for those with little to no experience of this type of analysis. This is not a guide on how to conduct your own analysis, but instead will serve as a taster to some of the key terms and principles of regression.
This is the thirteenth blog in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that if a treatment is not compared to something else, it is not possible to know what would happen without the treatment, so it is difficult to attribute outcomes to the treatment.
This is the twelfth in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that large treatment effects (where everyone or nearly everyone treated experiences a benefit or a harm) are easy to detect without fair comparisons, but few treatments have effects that are so large that fair comparisons are not needed.
This is the eleventh in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that treatments that should work in theory often do not work in practice, or may turn out to be harmful. An explanation of how or why a treatment might work does not prove that it works or that it is safe.
A pyramid has expressed the idea of hierarchy of medical evidence for so long, that not all evidence is the same. Systematic reviews and meta-analyses have been placed at the top of this pyramid for several good reasons. However, there are several counterarguments to this placement. This blog discusses a new, amended version of the pyramid, proposed in 2016.
A pilot study is a small scale preliminary study conducted in order to evaluate feasibility of the key steps in a future, full-scale project. Pilot studies can teach researchers about any amendments they will need to make to the design of the future study, in order to minimise waste of time and resources.
This is the tenth in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that hope can be a good thing, but sometimes people in need or desperation hope that treatments will work and assume they cannot do any harm. Similarly, fear can lead people to use treatments that may not work and can cause harm. As a result, they may waste time and money on treatments that have never been shown to be useful, or may actually cause harm.
Debiasing is about trying to account for and eliminate the influence of biases on our decision-making. This blog discusses effective debiasing techniques.
This blog is a Portuguese translation of a blog discussing the problem of evidence-based medicine, with thanks to Cochrane Brazil Evidence Based Medicine is useful for informing healthcare professionals what works, what doesn’t, and helping to determine if the benefits outweigh the harms, but it’s far from perfect. This blog explores some of the issues.
This is the ninth in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that people often assume that early detection of disease leads to better outcomes. However screening tests can be inaccurate (e.g. misclassifying people who do not have disease as having disease). Screening can also cause harm by labelling people as being sick when they are not and because of side effects of the tests and treatments.
This blog provides a step-by-step guide on how to conduct a systematic literature search.
This is the eighth in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that increasing the dose or amount of a treatment (e.g. how many vitamin pills you take) often increases harms without increasing beneficial effects. If a treatment is believed to be beneficial, we should not assume that more of it is better.
This is the seventh in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog discusses how people with an interest in promoting a treatment (in addition to wanting to help people), such as making money, may promote treatments by exaggerating benefits and ignoring potential harmful effects. Conversely, people may be opposed to a treatment for a range of reasons, such as cultural practices.
This is the sixth in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that doctors, researchers, patient organisations and other authorities often disagree about the effects of treatments. It explains why we should not rely on the opinions of experts or other authorities about the effects of treatments, unless they clearly base their opinions on the findings of systematic reviews of fair comparisons of treatments.
This is the fifth in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that new treatments are often assumed to be better simply because they are new or because they are more expensive. However, they are only very slightly likely to be better than other available treatments.
This is the fourth in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that treatments that have not been properly evaluated but are widely used or have been used for a long time are often assumed to work. Sometimes, however, they may be unsafe or of doubtful benefit.
This blog provides a detailed overview of the concept of ‘blinding’ in randomised controlled trials (RCTs). It covers what blinding is, common methods of blinding, why blinding is important, and what researchers might do when blinding is not possible. It also explains the concept of allocation concealment.
This is the third in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that the fact that a treatment outcome (i.e. a potential benefit or harm) is associated with a treatment does not mean that the treatment caused the outcome.
This is the second in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that claims about the effects of a treatment may be misleading if they are based on stories about how a treatment helped individual people, or if those stories attribute improvements to treatments that have not been assessed in systematic reviews of fair comparisons.
This is the first in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.
This blog explains that people often exaggerate the benefits of treatments and ignore or downplay potential harms. However, few effective treatments are 100% safe.
Implication: Always consider the possibility that a treatment may have harmful effects.
Introducing a new series of 36 blogs from Students 4 Best Evidence. This series is based on a list of 36 ‘Key Concepts’ developed by an Informed Health Choices project team. These 36 ‘Key Concepts’ are things we need to understand to appraise treatment claims.
This blog discusses the issue of ‘too much medicine’; a growing concern in the medical community regarding the over-diagnosis, over-treatment and over-testing of various pathologies. In particular, focusing on the overestimation of risk and the base rate fallacy.
This blog discusses fundamental issues affecting healthcare research, which could undermine the field and mean that most medical research may be wrong. Issues discussed include: 1) contradictory findings 2) the illusion of high impact factor journals 3) the reproducibility crisis 4) a lack of translation of research findings from bench to bedside 5) medical reversal 6) bias 7) statistical issues and 8) conflicts of interest and unethical practice. The author then explores possible solutions to these.
Authorship is a way that researchers get recognition for their work. However many issues may arise when assigning the authorship of a paper. Find here what a junior researcher should know.
‘Evidence-based practice’ is a commonly used phrase. But this blog asks the question: ‘just how much can we trust published scientific literature?’ with particular reference to the problems of publication bias and statistical approaches.
This blog explains what is meant by Type I and Type II errors in statistics. Whereby we can end up with false positive and false negative results.
This blog discusses problems with peer review in research, and explores possible ways in which the modern peer review process could be improved.
This blog is a Portuguese translation of the blog ‘The bias of language’ by Katherine Stagg. Read the English version here. With thanks to Cochrane Brazil for the translation.
This blog discusses the issue of assessing ‘quality’ in research, both methodological and reporting quality. Jenni, the blog author, also points readers towards a paper she has co-authored: ‘Using quality assessment tools to critically appraise ageing research: a guide for clinicians’.
This blog discusses the issue of statistical significance (whether a difference, such as an improvement in symptoms, is unlikely to have occurred by chance) vs. clinical significance (whether a difference, such as an improvement in symptoms, is meaningful and patient to patients).
This blog is a Portuguese translation of the blog ‘Meta-analysis: what, why and how’. Thanks to Cochrane Brazil for the translation.
This blog is a Portuguese translation of the blog ‘Defining Bias’ written by Dabean Faraj. Thanks to Cochrane Brazil for the translation.
This blog uses 3 examples to demonstrate that, even though there may be an association between two events or variables, this does not mean that one has caused the other.
This blog takes a detailed look at the issue of attrition bias (bias that can arise when participants drop out of a study). It also describes measures that can be taken by researchers to minimize this bias (including different types of statistical analyses).
Beware dodgy research (particularly when it’s pharmaceutical-funded)! This blog shines a spotlight on some of the appalling ‘tricks’ that researchers and sponsors can (and do!) play to help them get the results they want from their trials. From fiddling with the study design, to fiddling with the data analysis and ‘spinning’ results…
This blog provides a basic overview of: 1) what a meta-analysis is; 2) why they’re considered the ‘gold standard’ of evidence; and 3) how a meta-analysis is carried out.
In the final blog from our Understanding Evidence launch week, Martin Burton explores absence of evidence… Join in the conversation on Twitter @CochraneUK @MartinJBurton #understandingevidence.
In the fifth blog of our new series, Understanding Evidence, Lynda Ware gives us a flavour of how she’s taking Cochrane and evidence-based medicine to Community Halls. Join in the conversation on Twitter @CochraneUK #understandingevidence.
In the second blog of our new series Understanding Evidence, Iain Chalmers, the founding director of Cochrane UK, looks at developments in research on prenatal corticosteroids since the work which gave rise to the Cochrane logo. Join in the conversation on Twitter @iainchalmersTTi @CochraneUK #understandingevidence
We all need to be able to make sense of evidence, whether we’re making decisions about treatments, or weighing up the latest health story to hit the headlines. We’re partnering with Cochrane UK to put the spotlight on common errors and misunderstandings with a new campaign, Understanding Evidence.
‘O que é medicina baseada em evidências?’ This is a Portuguese translation of the blog ‘What is evidence-based medicine?’
You are sat down with an article or review. Now you want to critically appraise it. This blog features a checklist of 20 questions to allow you to do just that.
This blog explains what allocation concealment is & why it’s important, in terms of preventing researchers from (intentionally or otherwise) influencing which participants are assigned to a given intervention group.
This is a nuts and bolts tutorial published in Portuguese.
Existem quatro passos fundamentais em MBE e os recursos no website estão ligados a estes.
[There are four key steps in EBM and the resources on the website are linked to these].
You might rely too much on big journal brands because you hope they have highly rigorous peer-review processes. But are they always really reliable? Let’s find out.
A nuts and bolts tutorial on how to read a forest plot, featuring a couple of exercises so that you can test your own understanding.
Let’s figure out how to get the essential information from a meta-analysis at a glance, by studying a forest plot.
Median has come to be known for its fair reflection in the case of outliers. However, it is not a perfect statistic. Let me tell you about 3 defects the median as a measure of average.
Let’s find out why physicians sometimes contradict each other from a statistical perspective. And see how students can learn from that.
Let’s figure out how the epidemiologists determine the diagnostic thresholds by studying the cases of anemia and type II diabetes.
Come with me. I’ll show you the best way to display the efficacy of a drug. And the pitfalls around it. Ladies and gentlemen, welcome to the world of Number needed to treat.
When dealing with a difficult question, we tend to seek the answer for a simpler one, that seems to be relevant. However, a seductive trap awaits us here. Come with me, I’ll show you the world of surrogate endpoints.
Confused about Hazard Ratios and their confidence intervals? This blog provides a handy tutorial.
This post talks about the real meaning of p-value. No fancy words. No complicated definitions. Only simple notions included.
Here we will address the problem with cancer screening interventions regarding the potential benefits and harms of these strategies.
Outcome switching is a major problem in clinical trial reporting that distorts the evidence doctors and patients use to make real-world clinical decisions. Numerous prevalence studies have already shown this to be an extremely common problem, even in top medical journals. However the CEBM Outcome Monitoring Project (COMPare) has taken a new approach: writing to journals to correct the record on individual trials, in the hope that individual accountability and open data sharing will help solve this important problem. Our main question was: how will the journals respond? This blog tells the story of COMPare so far.
Reviews tend to provide summaries of the literature on a topic. However, there are differences between them in terms of the stages and applicability of findings. This post will highlight such differences between traditional reviews and systematic reviews.
We hear the word “evidence-based medicine” too often but why is evidence-based medicine important? And what’s the difference between eminence-based medicine? This post addresses those questions and give some examples of both evidence and eminence-based medicine practice.
Patients, carers and members of the public offer a unique perspective in health and social care research, adding to the expertise of the research team. Improving healthcare services will only be possible by involving the people accessing those services.
“This treatment lowers your high risk of heart attack considerably”. Wait, what is “risk”? This post explains you a definition of risk that is useful to understand in health-related questions.
How can you tell if a variable is nominal, ordinal, or numerical? Why does it even matter? Determining the appropriate variable type used in a study is essential to determining the correct statistical method to use when obtaining your results. It is important not to take the variables out of context because more often than not, the same variable that can be ordinal can also be numerical, depending on how the data was recorded and analyzed. This post will give you a specific example that may help you better grasp this concept.
Doctors must always ensure they are doing the right thing for each patient. But what are benefits and harms, and how do we ‘balance’ them?
A description of the two types of data analysis – “As Treated” and “Intention to Treat” – using a hypothetical trial as an example
Randomised Controlled Trials (RCTs) are central to evidence-based healthcare; but they themselves are riddled with inefficiency. Trial Forge aims to change that.
A brief overview of the concept of bias and what it means. This blog also describes 2 particular types of bias that are perhaps less well known to students.
Katherine Stagg explores the impact of language bias and how the language of publications can affect our evidence base.
In this post you are going to figure out how to interpret the evaluation of diagnostic tests through sensitivity and specificity.
Deevia takes a look at ‘effect modification’ and ‘confounding’ and explains the differences.
This blog describes what is meant by a positive predictive value and a negative predictive value, their purpose and how they can be interpreted
Let’s be honest, Evidence-Based Medicine is great. But it’s not perfect. Issues such as the lack of publishing of negative results need to be understood and tackled. In this Youtube video, Prof David Nealy does just that.
Iván Murrieta Álvarez takes an in depth look at determining the probability that a patient has a certain illness, using only a pen and paper.
Does industry sponsorship of research inevitably lead to bias? And does this bias extend to government advice and policy?
Thankfully, this “less is more” idea seems to be a movement gaining serious momentum in the medical world to “wind back the harms of too much medicine”.
Danny Minkow looks into how the COMET initative is working to developing and apply an agreed-upon set of outcomes measures in medical research. Why is it needed?
In Richard’s Reviews this week, we look at progress in sharing clinical trial data through the All Trials campaign, and the nature of patient-centred outcomes research.
Publication bias is generally ascribed to failure by researchers to submit studies for publication. This current study aims to further evaluate whether the editorial and peer review process also contributes to publication bias.
The relationship between Shared Decision Making and EBM; two separate disciplines or not? Read Ammar’s piece on this subject and have your say.
Advancing techniques and mechanization in every field has led to newer computer or written questionnaires in the field of medicine.
One often is confused whether to rely on these questionnaires or carry out oral history taking which has been prevalent for ages?
here’s an insight to it through various researches…
The rising ills of media affect our lives in ways deeper than we can imagine. As a matter of fact, it is a rising cause of psychiatric disorders, lets have a look why.
As calculating the mean is so popular it might lead to many intuitive misconceptions. Here are some precautions you can take when interpreting the mean.
Sham devices can have a larger effect than placebo, should they remain to be under-regulated? Yamama tells us more.
Key message: Evidence Based Medicine is useful for informing healthcare professionals what works, what doesn’t, and helping to determine if the benefits outweigh the harms, but it’s far from perfect. There are valuable lessons learned about research that we can share across disciplines. What is the Evidence Based Medicine problem? In 2005, Dr. John Ioannidis, a well-known meta-researcher, published an article in PLoS Medicine called Why Most Published Research Findings Are False. This article caused a splash and has been making
The Systematic Review is the highest level of research design and brings available evidence to find an answer to a research question. Read Danny’s blog.
Surrogate endpoints are like a double edged sword. Even though they do have some benefits on some occasions, they can easily mislead doctors into withdrawing the wrong conclusions. It is, therefore, important to use them with caution.
After 8 long years of University education I have to admit that I still do it. What’s worse; I’ve even been known to do it for my own area of research. So why do I still Wikipedia when I can access the literature? And why I am becoming a Wikipedia editor for for the S4BE Editathon?
Ever heard of the Placebo effect’s evil twin; the Nacebo Effect? A harmful reaction from a harmless treatment. Read Danny’s blog to know more.
Do placebos really promote physiological change or is it just the patient’s perspective? How are placebos used in practice? And how ethical is it to use placebos in clinical trials?
Terms such as significant, hypothesis testing, and p-value are usually found in research papers, here is a review explaining them.
Richard takes a look at Greenhalgh and colleagues, BMJ article “Evidence based medicine: a movement in crisis?”.
You probably have heard a debate between clinical judgment and Evidence Based Medicine. Is there a real reason to oppose these two concepts? See here for more…
What happens when you have a test result? Do you believe it, can you act on it? It all depends where you are. Check out this discussion of post-test probabilities and how they help in the interpretation of test results.
Sean reviews ‘What is Evidence Based Medicine and Why Should I care?’, an article for students and healthcare professionals which covers Evidence-Based Medicine from first principles to medical statistics in the course of one free paper.
On the uniform of every fine detective, badges which salute their sensitivity and specificity are worn. From crime to clinic, find out what defines these “pre-test” probabilities.
Danny takes us on a tour of the Evidence-Based Medicine Pyramid and the wonders within.
Pre-test probabilities can help clinicians select and interpret diagnostic tests. To see a recent, real life application check out Aaron’s review of “Diagnostic Accuracy of Point-of-Care Tests for Detecting Albuminuria” from the Annals of Internal Medicine.
Casper takes a look at the IDEAL Collaboration and evidence-based surgery.
I have a test, and I know its measure of sensitivity. What does this tell me? When should I use this test? How do I expect this test to perform? Read more about the clinical application of sensitivity.
On the uniform of every fine detective, badges which salute their “sensitivity” and “specificity” are worn. From crime to clinic, find out what defines these “pre-test” probabilities.
Systematic reviews take a long time to produce, and are often not up to date. In PLOS Medicine last month, a ‘living systematic review’ was proposed, to reduce the gap between evidence and practice.
In a BMJ editorial last month, Des Spence suggested that EBM may be broken. Alice takes a closer look.
Want to learn more about the past, present and future of EBM? The BMJ and JAMA have brought together a collection of EBM pioneers for this panel discussion.
The Lancet has recently published a series of papers looking at problems with waste and inefficiency in research, with recommendations for how these could be overcome.
An introduction to the role of statistical power in the search for evidence.
Find out from Iain Chalmers why we should all be “encouraged to think critically”
Observational research is an important method in evidence-based medicine, especially when it is performed to support or assess effectiveness results from randomized controlled trials. An unwanted (but not always observable) confounder in observational research is confounding by indication and should be eliminated from the research design when possible for the results to be meaningful. Let’s find out what this confounder is!
David blogs about Open Access. The practice of free, online, immediate access via the Internet to peer-reviewed scholarly research with full re-use rights
Evidence-based medicine is not just about applying a systematic review letter for letter – but the ‘art’ of evidence-based medicine is in applying the science.
Because of the increase in health care costs and the limited available resource costs, economic evaluation becomes more important in daily practice. What do we have to know for an economic evaluation?
Evidence-based medicine has a large variety of different sub-fields. Let’s begin our journey towards one of them – evidence-based physical examination.
The nuts and bolts 20 minute tutorial from Tim.
Anna reminds us of the value of observational evidence in low income countries.
Systematic reviews aren’t cheap or quick – Alice looks at some suggestions from the blogs of Jon Brassey from TRIP and Mona Nasser from Cochrane.
Reporting and discussing clinical trials clearly and accurately can be challenging, both for journalists, and also for students. Ruth Francis has compiled 11 top tips to make it easier.
Conducting trials where the trialled therapeutic must be commenced urgently raises specific practical and ethical problems. Here I discuss a recent New England Journal of Medicine paper looking at the use of intracranial pressure monitoring for severe traumatic brain injury as an example of how these issues may affect a trial’s utility and how this can be managed.
The BMJ Open Data Campaign seeks to make the data from unpublished trials available, so that decisions can be based on all the evidence.
What is the future of EBM in the US, both in policy and in reality?
Your patient has mild hypertension. What should you do?
Treat the hypertension.
Okay, how should you treat the hypertension?
Well, let’s start with HCTZ, that’s well-tolerated.
What will that do?
It’s a diuretic; it’ll help her get rid of the extra volume.
Okay, what will that do?
It’ll lower her blood pressure.
Okay, what will that do?
What do you mean, what will that do?
What will lowering her blood pressure do?
It’ll lower her blood pressure! Seriously—who are you anyway?
A meta-epidemiological study published in the BMJ last month has found that smaller trials consistently report larger effect sizes.
Critical appraisal is the process of carefully and systematically examining research to judge its trustworthiness, value and relevance in a particular context (Amanda Burls 2009).
An essay discussing the underpinnings of EBM and the difficulties of using it in clinical practice
Beginners often get confused with odds ratio and relative risk, which are almost used in same sense.
A simple way to understand both.
There are over 100 diseases that the National Screening Committee in the UK considers screening for – but only a fraction of these are approved for one reason or another. This blog hopes to give an introduction to the issues with screening programmes, in particular those involved in detecting cancer.
Seven short slides giving a brief introduction to evidence-based medicine
The importance of Evidence Based Medicine has been recognized in many countries around the world for decades now. This recognition lead to the formation of organisations promoting EBM and to the introduction of courses preaching EBM principles in universities. Unfortunately, my country, Syria, isn’t one of these countries yet and here’s how it’s going to get there.
AllTrials, putting the evidence back in evidence-based medicine.
PharmAware is a network of students committed to the use of the best evidence in healthcare.
Evidence-Based Medicine is a growing field that has already made a tremendous impact on world healthcare. It’s only rational to teach it to medical students from the beginning, however, this is not always the case. Let me give you an example: me.
Bias is often an issue within clinical research, and we take many measures to avoid it. However, these measures are often neglected in preclinical animal studies, which give us the results upon which clinical trial study designs are based.
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Another 20 minute tutorial from Tim.
The nuts and bolts 20 minute tutorial from Tim.
This new webpage from Cochrane UK is aimed at students of all ages. What is evidence-based practice? What is ‘best available research evidence’? Which resources will help you understand evidence and evidence-based practice, and search for evidence?