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.
‘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 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.
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.
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.
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.
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.
Know Your Chances: Understanding Health Statistics is one of the few easily digestible statistics books that teaches anyone the most basic principles and concepts how to question and see the reality behind health news, hype, claims and ads.
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.
Terms such as significant, hypothesis testing, and p-value are usually found in research papers, here is a review explaining them.
In search of a book with simple, comprehensible definitions and examples of clinical evidence? Do you want to take the first step in understanding common terms in clinical evidence as well as commonly used methods and their pitfalls? This review will inform you if this is the book you’re looking for.