Two or more samples, matched (Figure 1.4). The participants in the study groups are matched in pairs on a subject‐by‐subject basis for variables such as age and gender (so‐called ‘confounding’ variables). One group is then the control (or placebo) group, and the other group is given the active treatment(s).
FIGURE 1.3 Two‐sample (parallel) design.
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.
FIGURE 1.5 Randomized placebo‐controlled cross‐over trial.
1.6.4 Epidemiological Studies
Epidemiological studies examine relationships between exposures and health‐related outcomes in populations. In the context of nutritional epidemiology, exposures might include individual diet, community health intervention programmes, supplementation, food advertising, dietary advice, or other nutrition‐related variables. Outcomes can include changes in nutrition‐related blood biochemistry (e.g. cholesterol levels, haemoglobin), clinical outcomes (e.g. xerophthalmia, obesity), or morbidity or mortality statistics relating to nutrition.
Epidemiological studies fall into three categories.
Descriptive Studies
Descriptive studies in epidemiology include ecological studies, cross‐sectional studies, and time trend analysis. They are useful for generating hypotheses. Measurements can be made in individuals at a given point in time (cross‐sectional studies) or accumulated over time in groups of people (ecological studies). They are used to relate measures of exposure and outcome in groups of people that share common characteristics (e.g. vegetarians versus omnivores) or to compare regions or countries. For example, they might compare diet and disease patterns between countries (are heart disease rates lower in countries where people eat lots of oily fish?) or between subgroups (do vegetarians have lower risk of heart disease compared to non‐vegetarians?).
There are two main problems with this type of study. First, there may be other factors that could explain an observed association or changes in the population over time. For example, populations with higher oily fish consumption may be more active or less obese. Second, not everyone in the population or subgroup is exposed at the same level: some individuals in the population may eat lots of oily fish, while others may eat very little. Are the people with low oily fish consumption the ones that have higher rates of heart disease?
Analytical Studies
These include cohort and case‐control studies. Their primary characteristic is that they relate exposures in individuals (factors that are likely to influence the occurrence of disease or mortality within the population) to outcomes (disease or mortality rates). Analytical studies are usually based on observations relating to large numbers of people in the population (hundreds or thousands). They provide much stronger evidence of diet–disease relationships than descriptive studies. In terms of the Bradford Hill model of causality (Box 1.4), they provide evidence of temporal
