Statistics in Nutrition and Dietetics. Michael Nelson

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СКАЧАТЬ and journals) to explore how other people have tackled similar problems, and discussing with colleagues how best to investigate the problem at hand. Once you have done that, you can think about what it is you want to achieve in your research.

Projects have different aims. An undergraduate student project with 15 subjects carried out part‐time over two months may not have much chance of establishing new findings that are statistically significant, but it will introduce the student to hypothesis formulation, designing a study, writing a research protocol, sampling, subject recruitment, data entry, computer analysis, and writing up the results. On the other hand, an MSc project carried out full‐time over four months will require the same skills as the undergraduate project, but will usually involve a more detailed consideration of design, sample size, and study power (see Chapter 12). It will also provide an opportunity to write a detailed report and to make a presentation of the findings (both for assessment), usually to an audience of postgraduate peers and their tutors. More demanding undergraduate projects may include some or all of these additional elements. For a PhD, or for funded research, all of these elements will be present, plus the requirement to write paper(s) for submission to a peer‐reviewed journal and to present findings to a public audience at scientific meetings. As a professor of mine once said, ‘If you haven’t published the paper, you haven’t done the work’.

BOX 1.3 Steps in undertaking research

	•Step 1. Make observations about the world. Science doesn't happen in a vacuum.
	•Step 2. Construct a Hypothesis. State clearly the aims and objectives of your study. Formulate the Null Hypothesis.
	Formulate the Null Hypothesis.
	•Step 3. Design the experiment.
	This is the stage at which you should seek the advice of a statistician
	regarding the hypothesis, sample selection, sample size, choice of measurements, and the type of analyses and statistical tests to be used. Failure to consult properly at this stage may mean that any work that you do may be a waste of time. Do not take that chance!
	•Step 4. Conduct the research.
	•Step 5. Analyze the data both observationally (do the numbers make sense?) and statistically.
	•Step 6. Interpret the results (draw inferences) and write your report (for marking or for publication). Work that is not marked or published may just as well never have been completed.
	•Step 7.Bask in the glory of a job well done.

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      1.6.2 Demonstrating Causality

The underlying purpose of most research is to find evidence in support of causality of the type: ‘If A, then B’. Of course, we may just be interested in describing what is going on in physiological systems (what dietary factors are associated with low serum total cholesterol levels?) or in the population (are women aged 75 years and older at greater risk of osteoporosis‐related fracture of the hip if they have low levels of physical activity?) More often, we want to know if there is a causal relationship between these factors (does an increased level of physical activity protect against osteoporosis‐related hip fracture in women aged 75 and older?). Public health recommendations to improve nutrition and nutrition‐related outcomes need strong evidence of causality before they can be promoted to the general public. Confusion in the mind of the public is often caused by the media promoting a ‘miracle cure’ based on a single study (it makes good press but bad science). Food manufactures are often guilty of using weak evidence of causality or vague terms about ‘healthiness’ to promote sales of their products.¹⁰

BOX 1.4 Bradford Hill hierarchy of causality

Strength of association	Is the evidence linking exposure and outcome strong? We shall see what we mean by ‘strong’ as we explore the different statistical tests used to evaluate associations.
Consistency of association across studies	Are the same associations seen repeatedly in different groups or across different populations in different places and times?
Specificity	Is there a specific link between exposure and outcome?
Temporal association	Does A precede B? Evidence needs to show that cause (A) is followed by consequence (B). As we shall see, A and B may be associated in a cross‐sectional analysis of data, but unless a clear time‐sequence can be established, the evidence for causality is weak.
Dose‐response	Does increased exposure result in increased likelihood of the outcome? If fruit and vegetable consumption is protective against heart disease, can it be shown that the more fruit and vegetables are eaten, the lower the risk of disease?
Plausible mechanism and coherence	Is there a clear physiological explanation for the observed link between A and B? What is it in fruit and vegetables that affect the factors that determine risk of heart disease? Does the new evidence fit in with what is already known? If not, why not? Are there any animal models that support evidence in humans?
Experimental evidence	Does experimental evidence based on intervention studies support the argument for causation? Is the experimental evidence consistent across studies?
Analogy	Are there related exposures or conditions that offer insight into the observed association?

We have seen earlier that the logic used to support notions of causality may be inductive or deductive. Whichever logical model is used, no single study in nutrition will provide conclusive evidence of the relationship between A and B. There is a hierarchy of evidence, first set out clearly by Bradford Hill [4, 5], which suggests that a clear picture of causality can only be built from multiple pieces of evidence (Box 1.4). Published over 50 years ago, these criteria СКАЧАТЬ