In this blog, Saul Crandon provides the key points to consider, advantages, disadvantages, and further details regarding intercalated degrees.
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’.
Cochrane Malaysia and Penang Medical College, with the support of Students 4 Best Evidence (S4BE) recently ran an evidence-based medicine blog writing competition for undergraduate and postgraduate students of health in Malaysia.
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.
Alina provides a critical appraisal of the ARTEMIDA trial (2015) that assessed efficacy of Actovegin in poststroke cognitive impairment.
Cindy and Itzel provide us with a student perspective of their time at the Global Evidence Summit. It was the first meeting of Cochrane, the Campbell Collaboration, the Guidelines International Network (G-I-N), the International Society for Evidence-based Health Care and the Joanna Briggs Institute, which took place in September 2017. “…for our luck, it was our very first time attending a Colloquium. This event took place in the beautiful city of Cape Town, South Africa, the land of the first heart transplant”.
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.
The Global Evidence Summit took place between 13th and 17th September 2017 in Cape Town, South Africa. The event saw over 1400 delegates from 77 countries gather to discuss how to use evidence to improve lives. Heidi Gardner (a PhD student in Applied Health Sciences) blogs on her thoughts, experience, tips and hope for the future after attending the Summit.
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.