reasonably representative of all such women in the Leeds area (their study population), then they would perhaps have felt justified in generalising their findings to this population, and maybe to all women in the UK (a possible target population). But if they knew that the women in their sample were all from a particularly deprived (or particularly affluent) part of the city, or if some ethnic minority formed a noticeably large proportion of the women, then such a generalisation would be more risky.

Exercise 6.2 What is the principal advantage of random sampling? What is the principal drawback of this approach? Describe another method of getting samples that is used in clinical research.

Types of study

With this brief look at the data collection problem, I want to return now to the study design question. Study design divides into two main types. Some alternative ways of classifying these are:

Observational versus experimental studies.

Prospective versus retrospective studies.

Longitudinal versus cross-sectional studies.

I am going to use the first classification, although I will explain the other terms along the way. Broadly speaking, an observational study is one in which researchers actively observe the subjects involved, perhaps asking questions, or taking some measurements, or looking at clinical records, but they don’t control, change or effect in any way, their selection, treatment or care. An experimental study, on the other hand, does involve some sort of active intervention with the subjects. I will first discuss a number of types of observational study designs.

Exercise 6.3 What is the fundamental difference between an observational study and an experimental study?

Observational studies

There are four principal types of observational study:

Case-series.

Cross-section studies.

Cohort studies.

Case-control studies.

Case-series studies

A health carer may see a series of patients (cases) with similar but unusual symptoms or outcomes, find something interesting and write it up as a study. This is a case-series.

An example from practice

In 1981 a drug technician at the Centre for Disease Control in the USA, noticed an unusually high number of requests for the drug pentamidine, used to treat Pneumocystis carinii pneumonia (PCP). This led to a scientific report, in effect a case-series study, of PCP occurring unusually in five gay men in Los Angeles. At the same time a similar outbreak of Kaposi’s Sarcoma (previously rare except in elderly men) in a small number of young gay men in New York, also began to raise questions. These events signalled the arrival of HIV in the USA.

In the same way, new variant CJD was also first suspected from an unusual series of deaths of young people in the UK, from an apparent dementia-like illness, a disease normally associated with the elderly. Case-series studies often point to a need for further investigations, as was the case in each one of these quoted examples.

Cross-section studies

A cross-section study aims to take a ‘snapshot’ of some situation at some particular point in time,1 but notably data on one or more variables from each subject in the study is collected only once.

An example from practice

The following extract is from a cross-section study carried out in 1993 on 2542 rural Chinese subjects, into the relationship between body mass index2 and cardiovascular disease, in a rural Chinese population (1st paragraph in text below) (Hu et al. 2000). The population of this region of China was about 6 million, and the 2542 individuals included in the sample were selected using a two-stage sampling process, as the 2nd paragraph explains. Each subject was then interviewed and the necessary measurements were taken (3rd paragraph).

A total of 2 542 subjects aged 20–70 years from a rural area of Anqing, China, participated in a cross-sectional survey, and 1 610 provided blood samples in 1993. Mean BMI (kg/m2) was 20.7 for men and 20.9 for women. . .

1In practice this ‘point’ in time may in fact be a short-ish period of time.

2Body mass index, used to measure obesity, is equal to a person’s weight (kg) divided by their height squared

(m)2. A bmi of between 20 to 25 is considered ‘normal’, 25 to 30 indicates a degree of obesity. Higher scores indicate greater levels of obesity.

. . .These participants were selected from 20 townships in four counties based on a two-stage sampling approach. The sampling unit is a village in the first stage and a nuclear family in the second stage, based on the following criteria: 1) both parents are alive; and 2) there are at least two children in the family. We limited the analysis to 2 542 participants aged 20 years or older from 776 families. . .

. . .Trained interviewers administered questionnaires to gather information on each participant’s date of birth, occupation, education level, current cigarette smoking, and alcohol use. . . measurements, including height and weight, were taken using standard protocols, with subjects not wearing shoes or outer-wear. BMI was calculated as weight (kg)/height (m2). Blood pressure measurements were obtained by trained nurses after subjects had been seated for 10 minutes by using a mercury manometer and appropriately sized cuffs, according to standard protocols.

Note that there is no intervention by the researchers into any aspect of the subjects’ care or treatment – the observers only take measurements, ask some questions or study records. The results from the above study showed that subjects in the sample with higher body mass index values were also likely to have higher blood pressures. The researchers might reasonably claim that this link would also exist in the province’s population of 6 million – that’s their inference – but the truth of this would depend on how representative the sample was of the whole Anqing population. Whether or not the finding could be extended to the rest of the diverse Chinese population is more questionable. To sum up, cross-section studies:

Take only one measurement from each subject at one moment in, or during one period of, time. Data from one or more than one variable may be collected.

Can be used to investigate a link between two or more variables, but not the direction of any causal relationship. The Anqing study does not reveal whether a higher body mass index leads to higher blood pressures (more strain on the heart, for example), or whether higher blood pressures lead to higher body mass index (maybe higher blood pressures increase appetite), it simply establishes some sort of association.

Are not particularly helpful if the condition being investigated is rare. If, for example, only 0.1 per cent of a population has some particular disease, then a very large sample would be needed to provide any reliable results. Too small a sample might lead you to conclude that nobody in the population had the disease!

Can be more limited in scope and aim only to describe some existing state of affairs, such as the prevalence of some condition – for example, the percentage of 16+ UK individuals who have taken ecstasy. Only one variable is measured – use of ecstasy, yes or no. Since this is the only variable measured, no link with any other variable can be explored.

That aim to uncover attitudes, opinions or behaviours, are often referred to as surveys. For example, the views of clinical staff towards having patients’ relatives in Emergency Department trauma rooms.

Exercise 6.4 Give two examples of the application of the cross-section design in a clinical setting.

From here to eternity – cohort studies

The main objective of a cohort study is to identify risk factors causing a particular outcome, for example death, or lung cancer, or stroke, or low-birthweight babies and so on. The principle structure of a cohort study (also known as a follow-up, prospective, or longitudinal study) is as follows:

A group of individuals is selected at random from the general population, for example all women living in Manchester. . .

. . .or from a particular population, for example all call-centre workers. . .

. . .or via a clinical setting, for example women diagnosed with breast cancer.

The group is followed forward over a period of time,3 and the subjects monitored on their exposure to suspected risk factors, or to different clinical interventions.

At the end of the study, a comparison is made between groups with and without the outcome of interest (say cardio-vascular disease), in terms of their exposure over the course of the study to a suspected risk factor (e.g. smoking, lack of exercise, diet, etc.).

A reasoned conclusion is drawn about the relationship between the outcome of interest and the suspected risk factor or intervention.

A well-known prospective cohort study was that conducted by Doll and Hill into a possible connection between mortality and cigarette smoking. They recruited about 60 per cent of the doctors in the UK, determined their age and smoking status (among other things), and then followed them up over the ensuing years, recording deaths as they arose. Very quickly the data began to show significantly higher mortality among doctors who smoked.

In some cohort studies, the data may be collected from existing historical records, and subjects followed from some time starting in the past, as the following example demonstrates.

An example from practice

An investigation of the relationship between weight in infancy and the prevalence of coronary heart disease (CHD) in adult life used a sample of 290 men born between 1911 and 1930, and living in Hertfordshire, whose birthweights and weights at one year were on record. In 1994

3Note that ‘forward’ doesn’t necessarily mean from today, although prospective cohort studies do follow subjects forward from the time the study is initiated.

various measurements were made on the 290 men, including the presence or not of CHD (Fall et al. 1995). So ‘forward’ here means from each birth year between 1911 and 1930, up to 1944.

The researchers found that 42 men had CHD, a prevalence of 14 per cent, (42/290) × 100. But weight at birth was not influential on adult CHD. However, men who weighed 18 lbs (8.2kg) or less, at one year, had almost twice the risk of CHD as men who weighed more than 18 lbs. This of course is only the sample evidence. Whether this finding applies to the population of all men born in Hertfordshire during this period, or today, or indeed in the UK, depends on how representative this sample is of either of these populations.

Table 6.1 shows this cohort study expressed as a contingency table (see Chapter 2). The subjects are grouped according to their exposure or non-exposure to the risk factor (in this case weighing 18 lbs or less at one year is taken to be the risk factor), and these groups form the columns of the table. The rows identify the presence or otherwise of the outcome, CHD. Clearly this design does suggest (but certainly does not prove) a cause and effect – low weight at one year seems to lead to coronary heart disease in adult life. Cohort studies suffer a number of drawbacks, among which are the following:

Selection of appropriate subjects may cause difficulties. If subjects are chosen using a contact sample, for example attendees at a clinic, then the outcomes for these individuals may be different from those in the general population.

If the condition is rare in the population, i.e. has low prevalence, it may require a very large cohort to capture enough cases to make the exercise worthwhile.

The subjects will have to be followed-up for a long time, possibly many years, before any worthwhile results are obtained. This can be expensive as well as frustrating, and not good if a quick answer is needed. Moreover, this long time-period allows for considerable losses, as subjects drop out for a variety of reasons - they move away, they die from other non-related causes, and so on.

Over a long period a significant proportion of the subjects may change their habits, quit smoking, for example, or take up regular exercise. However, this problem can be monitored with frequent checks of the state of the cohort.

Table 6.1 The cohort study of weight at one year and its effect on the presence of coronary heart disease (CHD) in adult life, expressed in the form of a contingency table

Finally, note again that the selection of the groups in the cohort contingency table is based on whether individuals have or have not been exposed to the risk factor, for example weighing 18 lbs or less at one year (or smoking, or exposure to asbestos, or whatever).

Back to the future – case-control studies

A number of the limitations of the cohort design are addressed by the case-control design, although it is itself far from perfect, as you will see. In a cohort study, a group of subjects is followed up to see if they develop an outcome (a condition) of interest. In contrast, in a case-control study the groups are selected on the basis of having or not having the outcome or condition. The objective is the same in both types of study – can the outcome of interest be related to the candidate risk factor? The structure of a case-control study (also known as a longitudinal or retrospective study) is as follows:

Two groups of subjects are selected on the basis of whether they have or do not have some condition of interest (for example, sudden infant death, or stroke, or depression, etc.).

One group, the cases, will have the condition of interest.

The other group, the controls, will not have the condition, but will be as similar to the cases as possible in all other ways.

Individuals in both groups are then questioned about past exposure to possible risk factors.

A reasoned conclusion is then drawn about the relationship between the condition in question and exposure to the suspected risk factor.