Basic↦Statistic Methodology↦Research Question↦The Population
What is it? Why is it important?
Two terms are used when describing a study population:
- The target-population (i.e. sometimes referred to as “population”) represents a group of individuals of interest to the study (e.g. persons with lung cancer, aged between 18 and 50 years, diagnosed in 2020, and treated in Switzerland)
- The studied-population: it is often not feasible to include all individual from a target-population in a study. The studied-population is therefore a sample from the larger target-population, and from whom study data is being collected
Individuals from the studied-population are identified as part of the larger target-population, as they fulfil the required inclusion and exclusion criteria.
As individuals from the studied-population are expected to be representative of the larger target population, results obtained from the studied population are assumed to be generalizable for the larger target-population.
WARNING NOTE: The ability to get a representative selection of individuals from the target-population is not always straightforward. Selection-biases might cause a studied-population to not accurately represent the target-population and hence, lead to biased or misleading results.
More
Study recruitment must be non-discriminatory. Objective reasons for excluding or including a disproportionate group of people must be provided to the Ethics Committee (EC) (e.g. sex, age, race, genetic profiling).
What do I need to do?
As a SP-INV:
- Define your target-population (e.g. non-smoking adults diagnosed with lung cancer living in Switzerland)
- Specify the inclusion/exclusion criteria of your study:
- Inclusion: age range 18 – 50, with lung cancer diagnosed in 2020
- Exclusion: any smoking within 5 years prior to diagnosis, living outside of Switzerland
- Define and discuss with a statistician an appropriate sampling strategy with the aim to minimize potential selection bias
More
Example of a selection bias causing misleading results
- Study: the effectiveness of a new medication for treating the winter flu in adult patients.
- Sampling strategy: to include all adult patients residing within a single neighbourhood.
- Problem: the population in that particular neighbourhood may have unique characteristics affecting health outcome (e.g. good socioeconomic background, excellent access to healthcare resources). Consequently, the studied-population may respond better to treatment than the target-population would have responded.
Example of a sampling strategy: random selection
A study assesses the prevalence of a particular disease in a city with a target-population of 100,000 people. A random selection strategy:
- Assigns each person in the target-population with a unique number
- A generator randomly selects a sample of 1,000 individuals from the target-population
These 1,000 randomly selected individuals will be the studied-population and contacted for study participation
Where can I get help?
Your local Research Support Centre↧ can assist you with experienced staff regarding this topic
Basel, Departement Klinische Forschung (DKF), dkf.unibas.ch
Lugano, Clinical Trials Unit (CTU-EOC), ctueoc.ch
Bern, Department of Clinical Research (DCR), dcr.unibe.ch
Geneva, Clinical Research Center (CRC), crc.hug.ch
Lausanne, Clinical Research Center (CRC), chuv.ch
St. Gallen, Clinical Trials Unit (CTU), h-och.ch
Zürich, Clinical Trials Center (CTC), usz.ch
References
ICH GCP E6 (R3) – see in particular:
- Principles of ICH GCP Nr. 1.4 Participant selection should be representative
ICH Topic E9 statistical Principles for Clinical Trials – see in particular
- 2.2.1 Population
ICH Topic E8(R1) General considerations for clinical studies - see in particular
- 4.3.3 Special populations
- 5.5 Methods to reduce bias
Swiss Law
ClinO – see in particular article
- Art. 4a Inclusion of relevant groups of persons
ClinO-MD – see in particular
- Art. 3 paragraph 1 letter a Applicable provisions
HRO – see in particular
Art. 2c Applicable provisions