CHAPTER 1
Fundamental concepts

This chapter provides a summary of the main types of trials and their key design features. A checklist for critical appraisal of trial reports is on page 199, and a glossary of common abbreviations is on page 201.

1.1 What is a clinical trial?

The two distinct study designs used in health research are observational and experimental. Observational studies (usually cross‐sectional, retrospective case‐control or prospective cohort) do not intentionally involve intervening in the way individuals live their lives or how they are treated.1 However, clinical trials (experimental) are specifically designed to intervene, and then evaluate health‐related outcomes, with the following objectives:

• To diagnose or detect a disease
• To prevent a disease or early death (prolong life)
• To treat or cure an existing disorder, including reducing or managing symptoms
• To change behaviour or lifestyle habits, including reducing risk factors.

Countries (low, middle and high income areas) can successfully conduct clinical trials that reflect local health issues. The fundamental features of design and analysis are similar but the conduct and delivery will vary (especially how interventions are administered and how follow‐up and outcome data are collected).

An intervention could be a single therapy involving a substance that is injected, infused, swallowed, inhaled or absorbed through the skin; medical device; surgical procedure; radiotherapy; behavioural or psychological therapy; something to improve health service delivery or promote health education; or an alternative or complementary therapy.

A new generation of biological and targeted therapies (small molecules, monoclonal antibodies, immunotherapies and genetic and cell therapies) has revolutionised the treatment of several disorders, in which the choice of a therapy is influenced by the presence (or absence) of a biomarker, genetic abnormality or imaging marker. There are also vaccines that can be used for disease prevention or to reduce the risk of disease progression.

A combination of interventions can be referred to as a regimen, such as chemotherapy and surgery in treating cancer.

Any drug or micronutrient that is examined in a clinical trial with the specific purpose of treating, preventing or diagnosing disease is usually referred to as an Investigational Medicinal Product (IMP) or Investigational New Drug (IND).# Most clinical trial regulations cover studies using an IMP and several medical devices.

Figure 1.1 Overall view of trial design, types of results and interpretation. The acronym PICO (Participants/Population, Intervention, Control and Outcome) focuses on the four key design elements that must always be clearly defined (examples of trials in later chapters use the PICO list). Translational research (bio‐ and imaging markers) can also be examined.

New drugs and some medical devices usually require a licence or marketing authorisation for human use from a national regulatory authority. They can then be made available to the target population after review by a health technology assessment (HTA) or payer/reimbursement organisation through a process referred to as market access.

Throughout this book, the terms intervention, treatment and therapy are used interchangeably. People who take part in a trial are referred to as participants if they are healthy individuals or patients if they are already ill with the disorder of interest.

Figure 1.1 is an overall view of trial design and types of results (covered in more detail in other chapters).

Patient and Public Involvement and Engagement (PPIE) is a key activity in which lay members (e.g. past patients, carers and members of the public) can help with trial design (e.g. agree that the new therapy is appealing), conduct (create the participant‐facing information materials) and interpret trials in a way that can be easily understood.

Artificial intelligence is also expected to be used, for example in identifying eligible participants from electronic medical records and analysing clinical data and multiple biomarkers.

Decentralised trials may be increasingly used where many or all of the processes from participant selection and eligibility, allocation of treatments (usually licensed products already in use), through to data and outcome collection are done electronically including remote assessments of participants.

1.2 Early trials

James Lind, a Scottish naval physician, is regarded as conducting the first clinical trial.2 During a sea voyage in 1747, he chose 12 sailors with similarly severe cases of scurvy and examined 6 treatments, each given to 2 sailors: cider, diluted sulphuric acid, vinegar, seawater, a mixture of foods including nutmeg and garlic, and oranges and lemons. These sailors were made to live in the same part of the ship and given the same basic diet. Lind understood the importance of standardising their living conditions to ensure that any change in their disease would unlikely be influenced by other factors. After about a week, both sailors given fruit had almost completely recovered unlike the other sailors. This dramatic effect led to the conclusion that eating fruit cured scurvy, without knowing that it was due to vitamin C.

Two important features of his trial were a comparison between two or more interventions and an attempt to ensure that the participants had similar characteristics (see confounding below). The requirement for these two features has not changed for more than 270 years, indicating how essential they are to evaluating interventions.

One key element missing from Lind’s trial was the process of randomisation. The Medical Research Council trial of streptomycin and tuberculosis in 1948 is regarded as the first to use random allocation.3

1.3 Why clinical trials are needed

Statin therapy is effective in treating coronary heart disease. However, why do some patients who have had a heart attack and been given statin therapy have a second attack, while others do not? The answer is that people vary. People have different body characteristics (e.g., weight, blood pressure and blood measurements), genetic make‐up and lifestyles (e.g., diet, exercise, and smoking and alcohol consumption habits). These lead to variability or natural variation. People respond to the same exposure or treatment in different ways. When a new intervention is evaluated, it is essential to consider whether the observed responses are consistent with natural variation (i.e. chance) or whether there really is a treatment effect. This is a principal concern of medical statistics.

1.4 Alternatives to clinical trials

Examining interventions can be done using a clinical trial and in particular a randomised controlled trial (RCT), observational study or trial with historical controls. They have fundamentally different designs. Some observational studies are used as supporting evidence for the effectiveness and safety of an intervention under the topic real‐world evidence (RWE) or real‐world data (RWD); see Chapter 9.

Although studies other than RCTs can provide useful information about an intervention, care is needed over their interpretation. Observational studies, for example, can give the same or conflicting conclusion to RCTs:

• A review of 20 observational studies indicated that giving the influenza vaccine to the elderly could halve the risk of developing respiratory and flu‐like symptoms.4 Practically the same effect was found in a large double‐blind RCT.5
• A review of 6 observational studies indicated that people with a high β‐carotene intake, by eating lots of fruit and vegetables, had a 31% reduction in the risk of cardiovascular death than those with a low intake.6 However, 4 RCTs together showed that a high intake increases the risk by 12%.6

Observational (non‐randomised) studies

Observational studies may be useful in evaluating treatments with large effects, although there may still be uncertainty over the actual size of the effec.7, 8 They can have a larger number of participants than RCTs and therefore provide more evidence on side effects, particularly uncommon ones. However, when the treatment effect is small or moderate, the potential design limitations of observational studies can make it difficult to establish whether a new intervention is truly effective. These limitations are called confounding and bias.

Figure 1.2 Example of an observational study of the flu vaccine.

Source: Adapted from9.

Several observational studies have examined the effect of the influenza vaccine in preventing flu and respiratory disease in elderly individuals. Such a study would involve taking a group of people aged over 60 years, then ascertaining whether each participant had had the influenza vaccine or not, and who subsequently developed flu or flu‐like illnesses. An example is given in Figure 1.2.9 The chance of developing flu‐like illness was lower in the vaccine group than in the unvaccinated group: 21 versus 33%. However, did the flu...