In his 1597 play ‘Romeo and Juliet’, William Shakespeare narrates the tragic story of Romeo Montague and Juliet Capulet. The two young persons are in love, but their families are engaged in a blood feud. The consequences were tragic. The imposition of statistics in medicine evokes similarly strong emotions. The animosity may not be as ancient but is no less intense.
The disquiet about statistics in medicine is understandable. Most of us physicians did not have a loving relationship with mathematics and many of us probably despise numbers, formulas and equations. However much we dislike the intrusion of statistics in medicine, we need to have some understanding of it to make sense of medical research. An appreciation of medical statistics has become even more relevant of late where we find more and more studies that employ large databases, the interpretation of which requires an ever-increasingly complex array of statistical tests.
Medicine is not an exact science and we largely depend on the balance of probabilities to make diagnosis and treatment decisions. We don’t always make the correct decisions. Some of the treatments work, others don’t and a few may even be downright dangerous. The only way to find out what works and what does not is to put them to the test. But there are so many factors, some known some unknown, that can come into play in real life and affect the results of a test that it may be difficult to differentiate between the apparent from the real. Therefore, we need to know whether the apparent difference in effect is genuine or observed purely by chance.
Simply knowing a treatment works is not enough. If we decide that the treatment genuinely works we would also be interested to know how effective the treatment is, or what is the size of the treatment effect. If we agree that the treatment effect is sizable we should also ensure that the treatment makes a meaningful difference in the outcomes that matter and not simply make the numbers look good. For example, it is not enough to see a reduction in systolic blood pressure or serum cholesterol we need to see a reduction in cardiovascular mortality and morbidity. The utility of statistics in medicine is that it allows us to make sense of the seemingly random numbers that are generated from research and reach those conclusions.
Admittedly, we do need complex statistical equations to help make up our minds. Does that mean we all need to learn these formulas? Some physicians will happily put on the hat of researchers but for most of us practising ones that may be a challenge too far. How do we then master the web of medical statistics? We don’t.
The widespread availability of statistical software has given rise to the temptation to grab hold of software, open the spreadsheet, press a button and hope for some magical answers. This is just as risky as taking hold of the steering wheel without knowing the car or the route. This could get really messy! I do not think everyone needs to learn statistical formulas or be able to perform the tests. It is enough for the practising physician to understand what statistical tests were performed, why they were performed and what were the caveats thereof.
I set out to write this book out of a desire to help trainee medics and allied health professionals get their teeth in the daunting field of medical statistics. Although there are plenty of books currently available in the market and many of these are written by highly qualified statisticians, the books have a heavy emphasis on teaching the mathematics of statistics, a non-starter for the non-mathematical mind.
‘Making sense of medical statistics’ is thus an aid to a journey through the maze of medical statistics, largely avoiding mathematics and formulas. The book intends to help teach the learner the essential concepts rather than the formulas. There is an emphasis on active learning, one will find brain-teasers on every page. To keep the attention of busy clinicians every section is kept short in length. The hard copy appears slim so as not to overwhelm the newby learner. The slim volume is also intended to make the book more accessible and easy to carry in the pocket. We utilised examples from the whole spectrum of the medical literature so that the learning was practical and relevant rather than mathematical and abstract.
The other useful feature of this book is the use of copious illustrations. I have found it easier to understand many concepts of statistics with pictures and thought the learner might appreciate the same. The pictures will also hopefully break the monotony of words of what is often a difficult topic to understand. Keeping in mind the differing learning needs of the reader nearly all chapters have been divided into the core and extended learning sections. Those interested in a very basic overview can flip through the core learning material but the more interested learner can engage with additional material in the extended learning section. Once one completes the printed material there is an equal amount of material online for the even more advanced learner. The book ends with a list of freely available statistical software and useful websites and learning material so that one can make independent progress in learning. I hope the book would prove useful for the beginner but those at a more advanced stage of learning may also come to appreciate the light reading interspersed with historical anecdotes from the world of medical statistics. I encourage readers to get back to me via [email protected]. Your suggestions and criticisms are eagerly awaited!
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