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, and are expected to be representative of the larger target population. Only results obtained from studied populations that accurately reflect the characteristics of the target-population can be extrapolated and assumed valid for the larger target-population
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
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
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 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 under investigation 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 CTU↧ can support you with experienced staff regarding this topic
Basel, Departement Klinische Forschung, CTU, dkf.unibas.ch
Lugano, Clinical Trials Unit, CTU-EOC, www.ctueoc.ch
Bern, Clinical Trials Unit, CTU, www.ctu.unibe.ch
Geneva, Clinical Research Center, CRC, crc.hug.ch
Lausanne, Clinical Research Center, CRC, www.chuv.ch
St. Gallen, Clinical Trials Unit, CTU, www.kssg.ch
Zürich, Clinical Trials Center, CTC, www.usz.ch
References
ICH Topic E9 statistical Principles for Clinical Trials – see in particular
- 2.2.1 Population
ICH Topic E8(R1) on general considerations for clinical studies - see in particular
- 5.5 Methods to reduce bias