Medical Statistics. David Machin

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Medical Statistics - David  Machin


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and, as Loomis et al. (2016) stated ‘that positive associations reported in some studies could have been due to inadequate control for tobacco smoking, which can be strongly associated with heavy coffee drinking’.

      Statistical ideas relevant to good design and analysis are not easy and we would always advise an investigator to seek the advice of a statistician at an early stage of an investigation. Here are some ways the medical statistician might help.

      Sample Size and Power Considerations

      Questionnaires

      Rigby et al. (2004), in their survey of original articles in three UK general practice journals, found that the most common design was that of a cross‐sectional or questionnaire survey, with approximately one third of the articles classified as such.

      For all but the smallest data sets it is desirable to use a computer for statistical analysis. The responses to a questionnaire will need to be easily coded for computer analysis and a medical statistician may be able to help with this. It is important to ask for help at an early stage so that the questionnaire can be piloted and modified before use in a study. Further details on questionnaire design and surveys are given in Chapter 14.

      Choice of Sample and of Control Subjects

      The question of whether one has a representative sample is a typical problem faced by statisticians. For example, it used to be believed that migraine was associated with intelligence, perhaps on the grounds that people who used their brains were more likely to get headaches, but a subsequent population study failed to reveal any social class gradient and, by implication, any association with intelligence. The fallacy arose, perhaps, because intelligent people were more likely than the less intelligent to consult their physician about migraine.

      In many studies an investigator will wish to compare patients suffering from a certain disease with healthy (control) subjects. The choice of the appropriate control population is crucial to a correct interpretation of the results. This is discussed further in Chapter 14.

      Design of Study

      It has been emphasised that design deserves as much consideration as analysis, and a statistician can provide advice on design. In a clinical trial, for example, what is known as a double‐blind randomised design is nearly always preferable (see Chapter 15), but not always achievable. If the treatment is an intervention, such as a surgical procedure, it might be impossible to prevent individuals knowing which treatment they are receiving but it should be possible to shield their assessors from knowing. We also discuss methods of randomisation and other design issues in Chapter 15.

      Laboratory Experiments

      Medical investigators often appreciate the effect that biological variation has in patients, but overlook or underestimate its presence in the laboratory. In dose–response studies, for example, it is important to assign treatment at random, whether the experimental units are humans, animals or test tubes. A statistician can also advise on quality control of routine laboratory measurements and the measurement of within‐ and between‐observer variation.

      Displaying Data


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