How to Read a Paper: The Basics of Evidence-Based Medicine by Trisha Greenhalgh Page B

gallbladder (the ‘keyhole surgery’ approach) was no quicker than that associated with the traditional open operation. The discrepancy between this trial and its predecessors may have been because of the authors' meticulous attempt to reduce bias (see Figure 4.1 ). The patients were not randomised until after induction of general anaesthesia. Neither the patients nor their carers were aware of which operation had been performed, as all patients left the operating theatre with identical dressings (complete with blood stains!). These findings challenge previous authors to ask themselves whether it was expectation bias (see section ‘Ten questions to ask about a paper that claims to validate a diagnostic or screening test’), rather than swifter recovery, which spurred doctors to discharge the laparoscopic surgery group earlier.
    Were preliminary statistical questions addressed?
    As a non-statistician, I tend only to look for three numbers in the methods section of a paper.
a. The size of the sample;
b. The duration of follow-up; and
c. The completeness of follow-up.
    Sample size
    One crucial prerequisite before embarking on a clinical trial is to perform a sample size (‘power’) calculation. A trial should be big enough to have a high chance of detecting, as statistically significant, a worthwhile effect if it exists, and thus to be reasonably sure that no benefit exists if it is not found in the trial.
    In order to calculate sample size, the clinician must decide two things.
     
The level of difference between the two groups that would constitute a clinically significant effect. Note that this may not be the same as a statistically significant effect. To cite an example from a famous clinical trial of hypertension therapy, you could administer a new drug that lowered blood pressure by around 10 mmHg, and the effect would be a statistically significant lowering of the chances of developing stroke (i.e. the odds are less than 1 in 20 that the reduced incidence occurred by chance) [16]. However, if the people being asked to take this drug had only mildly raised blood pressure and no other major risk factors for stroke (i.e. they were relatively young, not diabetic, had normal cholesterol levels, etc.), this level of difference would only prevent around one stroke in every 850 patients treated—a clinical difference in risk which many patients would classify as not worth the hassle of taking the tablets. This was shown over 20 years ago—and confirmed by numerous studies since (see a recent Cochrane review [17]). Yet far too many doctors still treat their patients according to the statistical significance of the findings of mega trials rather than the clinical significance for their patient; hence (some argue), we now have a near-epidemic of over-treated mild hypertension [18].
The mean and the standard deviation (abbreviated SD; see ‘a’ of section ‘Have the authors set the scene correctly?’) of the principal outcome variable.
    If the outcome in question is an event (such as hysterectomy) rather than a quantity (such as blood pressure), the items of data required are the proportion of people experiencing the event in the population, and an estimate of what might constitute a clinically significant change in that proportion.
    Once these items of data have been ascertained, the minimum sample size can be easily computed using standard formulae, nomograms or tables, which may be obtained from published papers [19], textbooks [20], free access websites (try http://www.macorr.com/ss_calculator.htm ) or commercial statistical software packages (see, for example, http://www.ncss.com/pass.html ). Hence, the researchers can, before the trial begins , work out how large a sample they will need in order to have a moderate, high or very high chance of detecting a true difference between the groups. The likelihood of detecting a true difference is known as the power of the study. It is common for studies to stipulate a power