Reporting Results of Studies: can passive smoking really be good for you? (Pulse Article 1999)

Passive smoking and health risks.

“Passive smoking may be good for you” or so the tobacco companies would like us to believe! This idea arose from a misrepresentation of the confidence interval for data on passive smoking, and provides a good example of why we need a working knowledge of some statistics to deal with the propaganda that comes our way in General Practice. Sadly statistics is reported to be one of the subjects least liked by medical students, and those of us who have been in practice for more than a few years may be unfamiliar with some of the ways that results of studies are now reported. There has been a shift away from the use of p values towards Confidence Intervals (CI) in many medical journals, and the British Medical Journal now expects authors of papers to present data in this way.

Don’t forget common sense

Before going into more detail about the use of Confidence Intervals the example quoted for passive smoking above may be swallowed by the public, and even in some cases by journalists, but hopefully most GPs would be suspicious that such a finding just does not make sense. It does not fit with all the other data that has emerged in the past 20 years, and therefore needs some further looking at. Never leave common sense behind when looking at statistical reports!

Confidence Intervals or P values

So what are Confidence Intervals all about and how did they get misused in this example? In general when research is undertaken the results are analysed with two separate questions in mind. The first is how big is the effect being studied (in this case how big is the risk of lung cancer for passive smokers)? The second question is how likely is it that the result is due to chance alone? The two issues are connected, because a very large effect is much less likely to have arisen purely by chance, but the statistical approach used is different depending on which question you are trying answer. The “p” value will only answer the question “what is the chance that the study could show its result if the true effect was no different from placebo”? The Confidence Interval describes how sure we are about the accuracy of the trial in predicting the true size of the effect.

Both questions relate to the fact that we cannot know what the effect would be of a treatment or risk factor on everyone in the world; any study can only look at a sample of people who are treated or exposed to the risk. We then have to assume that if, say, one hundred identical studies were carried out in the same way on different groups of patients the results found would be normally distributed around the average effect size of the treatment. The larger the number of patients included in the trial the closer the result of that trial are likely to be to the true effect in the whole population. The result of any particular trial can therefore be presented as showing an effect of a certain size, and the Confidence Interval describes the range of values between which you can be 95% certain that the true value lies.

The data on Passive Smoking

Perhaps this can be illustrated with the passive smoking data. The results were that the on passive smoking study in seven European countries showed that there was an extra risk of developing lung cancer of around 16% for non-smokers who were exposed to smoke in the workplace or who had a spouse who smoked. This was comparing 650 lung cancer cases with 1542 controls in Europe and was accompanied by an estimate that 1100 deaths occurred each year in the European Union as a result of passive smoking.

common2The 95% Confidence Interval associated with this data is shown in the diagram and the tobacco industry had just chosen to highlight the lower end of the Confidence Interval, which shows a small chance that passive smoking could be associated with a 7% lower rate of lung cancer! Unsurprisingly they did not report the equal chance that the risk may be as high as 44% more lung cancer in passive smokers, and the Sunday Telegraph swallowed the story whole. More details are provided in the excellent article by Simon Chapman in the BMJ 1998;316:945.

Gardner and Altman mention this danger in their book “Statistics with Confidence”, and they suggest that results should be presented with the effect size, confidence interval and p value to prevent this kind of misunderstanding. The first two chapters are well worth reading if you want a fuller understanding of the rationale behind the use of Confidence Intervals. A final point about the Confidence Interval is that when it crosses the no-difference line (as shown in the diagram above) then the results do not reach significance at the level chosen (usually 5%).

Simon Chapman points out however that a meta analysis in the BMJ in the 18 October 1997 issue compared 4626 cases with 477924 controls and showed a 24% excess risk of lung cancer in non-smokers living with smokers. The 95% Confidence Interval was 13%to 36% which is well clear of the no-difference line and hence highly statistically significant, with a p value of >0.001. Again this data was conveniently ignored.

The moral of the story is that you cannot believe it just because you read it in the Newspaper. As far as the advantages of passive smoking are concerned, they can join the other myths and misunderstandings documented in one of my favourite books Follies and Fallacies in Medicine by Skrabanek and McCormick.

Statistics with Confidence MJ Gardner and D Altman BMJ Publishing 1989

Follies and fallacies in Medicine Skrabanek and McCormick Tarragon 1998


Can you trust what you read? Why we need Randomised Trials (Pulse Article 1999)

How can you tell if a paper is reliable? This was the question that many of the registrars wanted to have answered at a recent half-day release session on critical reading.

The Challenge of Archie Cochrane

Before he died Archie Cochrane expressed his sadness that no-one had gathered together the most reliable data available so that it could be used a basis for practice and research in Health Care. In response to this challenge the Cochrane Collaboration has emerged as a group of dedicated individual doctors and health care professionals who have set out to collect together data from controlled clinical trials, and summarise what they have found in the form of systematic reviews. The Collaboration is an international organisation and is structured by health problem areas to avoid duplication of effort. Many Journals have been hand-searched to identify controlled trials, and the reviews are structured so as to reduce bias at each stage of the process (which includes a ban on drug companies sponsoring individual reviews). In the UK many of the editorial bases are funded through the NHS Research and Development Programme.

The output of the Collaboration which includes a database of over 250,000 controlled trials identified and over 600 systematic reviews, is published in electronic form in the Cochrane Library, and a future article in this series will give an example of how it can be used.

The place of RCTs

There is considerable misunderstanding at this point about the place of Randomised Controlled Trials (RCTs). It would be unfair to say that you should not bother to read anything that is not an RCT, but it is also true to say that the most reliable way to study causation is with a systematic review of randomised controlled trials.

The way I like to look at the issue of randomisation is as follows; ask yourself the question “Could this trial have been randomised?” If you decide that it could have been randomised but it was not, then a large question mark should be placed over conclusions about whether the paper can reliably answer any questions related to the intervention causing good or bad outcomes.

Evidence for HRT

Hormone Replacement therapy is a good current example of this. Most of the current evidence relating to the purported benefits of HRT comes from non-randomised studies, and the results are therefore likely to be biased by differences between the type of women who opt for HRT and those who do not. An excellent editorial in the British Journal of General Practice in 1998 presents the current state of play in this area is recommended reading. Randomised Controlled Trials are currently under way to assess the effects of using HRT, but these will not report findings for a few years yet.

Sometimes you cannot randomise

There are of course some areas in which Randomisation is either impossible or unethical; you could not carry out a trial in which patients were randomised into cigarette-smoking or not! The very strong evidence on the dangers of smoking comes from large well conducted cohort studies, which are quite enough to leave little doubt about the size of the dangers involved.

Does it matter how you do it?

Whilst on the subject of Randomisation, how it is done matters too. The technical term to describe the actual randomisation is “allocation concealment” and if you read reports of older trials this often used to be done by using the patient’s hospital number to decide which treatment type they should receive or even alternate between treatments. It has been shown that trials with inadequate allocation concealment of this sort tend to show larger benefits of the intervention under study and it is not too difficult to imagine why.

Magic cure for warts?

Imagine you have developed a new treatment for removing warts and you arrange a trial to test it against one of the current methods. A patient walks into your surgery with a whole mass of horrible looking large warts, which you think that no treatment on earth will remove, and you can tell from the unconcealed alternated or random allocation that they would be in turn to receive your new technique. What will you do? Human nature is such that you will find some reason that this person will not quite fit into the trial and you will move on to another patient who has a nice small wart to treat next time. Obviously in this instance the advantage of randomisation in removing bias in the allocation process has been lost.

A better way to do it

When assessing the allocation concealment in Randomised trials I would look for at least opaque sealed envelopes which contain a random sequence of numbers to determine the next patient’s treatment. Even better would be a separate centre (such as the hospital pharmacy) to randomly allocate the treatment to the patient after the decision has been made to include the patient in the trial.

Try it yourself

So next time you are reading a paper, after you have asked yourself what is the question the paper was trying to answer, just pause to consider whether the trial could have been randomised. If it was randomised how easy would it have been to tell which treatment the next patient was getting? If you are satisfied on both of these fronts then read on, and if not perhaps move on to another paper.


1) Schulz KF, Chalmers I, Hayes RJ, Altman DJ. Empirical evidence of bias: dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 119;273:408-12

2) Hannaford PC. Is there sufficient evidence for us to encourage the widespread use of hormone replacement therapy to prevent disease? BJGP 48;427:951-2

Further Reading

So what’s so special about randomisation? Kleijnen J, Gotzsche P, Kunz RA, Oxman AD, Chalmers I. Chapter 5 in Non-random reflections on Health Services Research (Eds Maynard A and Chalmers I) BMJ Publishing Group 1997, pp93-106.