Research methods for primary health care
3.3 Quantitative research
Implementation in practice Quantitative (hypothesis
-testing) research, e.g.
randomised trials Exploratory (hypothesis-generating)
qualitative research
Explanatory qualitative
research
Evaluative qualitative research Figure 3.2 The place of qualitative research in mixed-method clinical trials.
and locally – including guideline development, organisational changes such as an extended out of hours telephone service, inclusion of brief advice on practice leaflets and education programmes for high-risk patients and their relatives. What the qualitative study on why patients delay seeking help when they have chest pain did not (and could not) answer was the quantitative ques- tion ‘What is the chance that a person with chest pain will delay seeking help for more than four hours?’ This, of course, requires a quantitative study (see Section 3.3).
In qualitative research, serious bias (i.e. distortion of findings or their in- terpretation) can occur when researchers do not critically examine their own perspective and the influence that they themselves might have had on the re- sults. An example of this is the doctor or nurse researcher who asks a sample of patients what they think about conventional and alternative forms of hor- mone replacement therapy, but who does not sufficiently consider that their own position as an authority figure in conventional medicine might prejudice the interviewees’ responses.
Qualitative research fits well within the interpretive approach to knowledge, in which the main goal is a search for meaning and understanding, but it is also used in research studies that take a more logico-scientific perspective, where the goal is establishing causality (see Table 2.2, page 47), as shown in the example of ‘explanatory’ research in Figure 3.2.
hence can be answered by numerical measurements. Quantitative research is the cornerstone of epidemiology (see Section 8.1) – one of the key under- pinning disciplines of primary care. As a rule of thumb, all epidemiological research addresses one of five types of question, and each of these questions has a preferred research design.
rPrevalence questions take the general format ‘What proportion of the popu- lation suffers from disease X?’ The prevalence of diabetes in the UK is about 2% – in other words, 2 in every 100 people are known to have the disease.
The research method of choice for answering this sort of question is a simple counting exercise – or, to use its formal scientific name, a cross-sectional sur- vey. Section 7.1 presents data on family structure that are based on the most ubiquitous cross-sectional surveys of all – the General Household Survey of England and Wales (‘the Census’), which all citizens are asked to complete ev- ery 10 years. Section 9.1 describes how data from the same survey were used to generate the Index of Multiple Deprivation – an aggregate estimate of disad- vantage. Note also that questionnaire studies, covered in Section 3.4, are also a form of cross-sectional survey – but instead of measuring what proportion of people have a disease, the questionnaire survey measures what proportion holds a particular attitude or opinion. The cross-sectional survey design is shown in Figure 3.3.
rIncidence questions take the general format ‘What is the chance that a person will develop disease X in time period T?’ Whereas prevalence expresses the total number of cases per unit of population, incidence measures the number of new cases over a given time period (usually per year). The incidence of multiple sclerosis in the UK is 4 per 100,000 – in other words, (on average) in a population
Estimates the proportion of people in a population who have a disease (prevalence study) or who hold a particular opinion (questionnaire survey)
Figure 3.3 Basic design for a cross-sectional survey.
Exposed cohort (disease free but exposed to possible
harmful agent)
Proportion of exposed people
who develop the disease
Predefined time interval
Non-exposed cohort (disease free and not exposed to this
agent)
Proportion of non- exposed people
who develop the disease Figure 3.4 Basic design for a longitudinal cohort study.
of 100,000, 4 people will develop the condition over the next 12 months. The preferred research method for incidence questions is a careful follow-up of a population for a given period of time (i.e. a longitudinal survey), using validated diagnostic methods and criteria to pick up new cases. Section 8.3 describes a longitudinal survey that followed women up to find the outcome of different screening tests for Down syndrome. The longitudinal cohort design is shown in Figure 3.4.
rPrognosis questions take the general format ‘What proportion of people with disease X will develop outcome Y over time period T?’ For example, if a young woman develops breast cancer, her first question to the doctor (or perhaps the breast cancer support nurse) might be ‘What is my chance of survival?’.
The doctor or nurse cannot tell her her individual survival time (which is why patients have usually misunderstood their clinician when they say ‘I’ve been given five years to live’). But epidemiology allows us to give patients’ infor- mation on prognosis such as ‘If 100 people with the same disease as you were left untreated, 50 would still be alive in five years’. Indeed, cancer prognosis is generally expressed in terms of 5-year survival (5YS), except in poor prognosis tumours when it is expressed in terms of 1-year survival (1YS). The research method of choice for prognosis questions is again a longitudinal survey – but this time our focus is not on the whole population (any of whom might develop a new disease) but on a group of individuals who already have a par- ticular disease at a particular stage in its natural history – a group known as an inception cohort. Prognosis studies inform (indeed, are the basis of) the clinical prediction rules described in Section 5.2.
rHarm questions take the general format ‘What proportion of people exposed to risk factor R will develop unwanted outcome O?’ Risk factor R might be a drug, a vaccine, an environmental pollutant (including cigarettes), a be- haviour choice (e.g. riding a motorcycle), a surgical operation – indeed any- thing that might lead to an adverse outcome. The research method of choice for harm questions is often a longitudinal cohort study – of which post-marketing surveillance (i.e. keeping careful records of all patients prescribed a particular drug within 3 years of its release onto the market) is a good example. Thus, for example, patients in the USA taking the new parathyroid hormone ana- logue teriparatide, recently licensed for the treatment of severe osteoporosis, are routinely placed on a register and their doctors sent regular questionnaires to monitor any health problems. So far, not a single one of the 350,000 patients on this register has developed bone cancer (a theoretical risk from a drug that aggressively promotes bone growth).18Another useful method for exploring the link between exposure and harm is a case-control study, in which peo- ple who have developed an unwanted condition (e.g. autism) are carefully matched with people who have not, and these ‘cases’ and ‘controls’ carefully studied to compare their past exposure (or not) to the putative harmful agent.
Parents contemplating the triple measles, mumps and rubella (MMR) vaccine for their child, for example, may ask ‘What is the chance that my child will develop autism as a result of this jab?’ – to which we can now say confidently
‘No greater than their chance of developing autism if they do not have the jab’
(see Section 8.2 for a full discussion of this example). The case-control design is shown in Figure 3.5.
Proportion of cases who were exposed
in the past
Small number of cases identified with the disease
Retrospective look for past exposure
Proportion of controls who were exposed in the past
Disease-free controls, each of which is matched with a ‘case’
? ?
Figure 3.5 Basic design for a case-control study.
rTherapy questions take the general format ‘what proportion of people with disease X and treated with treatment [e.g. drug or operation] Y will develop outcome O, compared with the proportion who get outcome O on no treat- ment [or on treatment Z]?’ The comparison group is very important for therapy questions. Ninety-nine percent of children with mild sore throat who are given penicillin will be cured within 9 days, but a similar proportion will be cured on no treatment! The research method of choice for therapy questions is the randomised controlled trial, in which eligible participants are allocated ran- domly to either the intervention or the control group, so that (in theory at least) we start the trial with two groups who differ only in terms of the inter- vention being studied. Randomised controlled trials have traditionally been the province of secondary care (patients lying in their beds are more easily recruited and randomised than those in the community), but there is now a growing evidence base from high-quality randomised trials (and systematic reviews of such trials – see Section 3.8) that helps us address the bread-and- butter questions of primary health care such as whether (and in what cir- cumstances) to give antibiotics for sore throats, what wound dressing to use for leg ulcers and so on. Rather than reference specific trials as examples, I strongly encourage you to check out the Cochrane Controlled Trials Register on http://www.nelh.nhs.uk/cochrane. The randomised controlled trial design is shown in Figure 3.6.
A particular form of prevalence study is the validation of a diagnostic or screening test, in which a new (perhaps cheaper, safer or more acceptable) test is compared with a recognised gold standard. Every participant in the
Sample drawn from a population
Assessment to confirm eligibility and take baseline measurements
Randomisation
Follow-up measurements Predefined time
interval Intervention group
(treatment A)
Control group (treatment B)
Figure 3.6 Basic design for a randomised controlled trial.
Table 3.2 Format for a 2×2 matrix for validation of a diagnostic or screening test.
Result of gold standard test Disease positive Disease negative
a+c b+d
Result of screening test Test positive True positive False positive
a+b a b
c+d c d
Test negative False negative True negative Sensitivity =a/(a+c); specificity=d/(b+d); positive predictive value=a/(a+b); negative predictive value=d/(c+d) (see Section 3.3 for explanation of these terms).
study is offered both tests, and using a 2×2 matrix (Tables 3.2 and 3.3), the proportion of true and false positive results can be calculated. In the example shown in Table 3.3, the Helisal saliva test performs well but not outstandingly:
it has a sensitivity of 88% (i.e. successfully picks up this proportion of people with Helicobacter pylori); a specificity of 70% (i.e. successfully excludes this proportion of people without the condition); a positive predictive value of 75% (i.e. if the test is positive the person has this chance of actually having the condition); and a negative predictive value of 85% (i.e. if the test is negative the person has this chance of not having the condition). In Section 8.3, I will discuss some more examples of how epidemiology can be used to screen pre- symptomatic people for disease.
It is important not to conflate quantitative research with the limited range of designs and techniques used in epidemiology. Whilst these are the main ones of relevance to clinicians, there are many other types of research that use quantitative data and many other ways of collecting and analysing such data. For example, social network analysis (see Section 9.2) is essentially a quantitative technique, as is the mathematical modelling that informs much economic analysis these days, and questionnaire research (see below) spans both qualitative and quantitative fields.
Table 3.3 Validation study for ‘Helisal’ saliva test for detectingHelicobacter pyloriinfection against established gold standard.19
Result of gold standard test*
Disease positive Disease negative Result of screening test Test positive True positive False positive
‘Helisal’ saliva 120 41
17 96
Test negative False negative True negative
*Combination of three existing tests including urea breath test.