Statistics in Nutrition and Dietetics. Michael Nelson

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FIGURE 1.4 Two‐sample (matched) design.

For example, if the aim was to test the effect of a nutrition education programme to persuade urban mothers to breast feed their infants (versus no education programme), it would be important to match on age of mother and parity (number of births). Matching could be carried out by first grouping mothers according to the number of children. Then, within each parity group, mothers could be ranked according to age. Starting with the two youngest mothers in the group with one child, one mother would be randomly assigned to the treatment group (the education programme), and the other would be assigned to the control group (no education programme).¹¹ The next two mothers would be assigned randomly to treatment or control, and so on, by parity group and age, until all the subjects have been assigned. In this way, the possible effects of age and parity would be controlled for. If there were three groups (Treatment A, Treatment B, and a control group), mothers would be matched in triplets according to parity and age and randomly assigned to Treatment A, Treatment B, or the control group. Using this technique, all the groups should have very similar age and parity structures, and it could be argued that age and parity therefore have a similar influence in each group. Of course, other factors such as maternal education, income, or social class might also influence outcome. The difficulty with including too many factors as matching variables is that it becomes increasingly difficult to find adequate matches for everyone in the study. Use the two or three factors that you think will be the most powerful influence on the outcome as the matching variables. Then measure all the other factors that you think might be associated with the outcome so that they can be taken into account in the final analyses (see Chapters 10 and 11).

      Clinical trials. These involve the assessment of the effects of clinical interventions such as drugs or feeding programmes. They are usually carried out in a controlled setting (that is, where the subject will be unable to obtain other supplies of the drug or where all aspects of diet are controlled). The intervention is compared with a placebo.

The design and analysis of clinical trials is a science in itself [7], and there are a great many variations in design which can be adopted. The so‐called Rolls Royce of clinical trials, the randomized double‐blind placebo‐controlled cross‐over clinical trial (Figure 1.5), requires careful thought in its planning, implementation, and analysis.

      Randomized controlled trials should not be embarked upon lightly! It is very demanding of time, staff, and money. Moreover, there are important limitations.

In recent years, it has been recognized that while clinical trials may be appropriate for providing proof of causality in some circumstances (e.g. understanding the impact of a new drug on disease outcomes, or a specific nutritional intervention), there may not always be equivalent, controlled circumstances that exist in the real world (e.g. promotion of five‐a‐day consumption in a sample versus a control group). The generalizability of findings may therefore be limited when it comes to saying whether or not a particular intervention is likely to be of benefit to individuals or the population as a whole. To address these circumstances, alternate designs and analytical approaches have been developed in the last decade or more that aim to take complex, real‐world circumstances into account [8]. The existing guidance is due to be updated in 2019.