What is it? Why is it important?

The sample size consists of a subset of individuals selected from a larger population, and who they are meant to represent.

 

The statistical power of a study is directly related to the sample size. In other words, a larger sample size will more accurately represent the population under investigation. Thus, the sample size has a direct effect on the precision and accuracy of study results, including the ability to detect a real effect (e.g. the ability of an interventional drug to lower blood pressure in participant with high blood pressure).  

 

When planning a study, it is important to justify the defined sample size of the study. The study sample size is calculated by the study statistician.

 

What do I need to do?

As a SP-INV, based on the aim of your study explain how the sample size and its subsequent collected data are expected to provide valuable scientific information.

 

Aspects to consider include:

  • The outcome/endpoint of your study (e.g. cholesterol concentration)
  • The method you use for the sample size estimation
  • The statistical framework, which can either be:
    • Hypothesis testing (e.g. you want to test the difference in cholesterol concentration between two patient groups). Define the null and alternative hypothesis
    • Precision-based (e.g. you want to estimate the mean in cholesterol concentration of a certain group of patients). Define the type I error and power
  • The assumption/previous knowledge which were used as a basis for the sample size calculation (e.g. we expect a mean value of 5.2 mmol/L and a standard deviation of 1.5)

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) Guideline – see in particular:

  • Essential records table: documentation statistical consideration
  • 3.11.4.5.4 Monitoring of clinical trials
  • 3.16.2 b Statistical programming
  • Appendix B. The protocol – B.10 Statistical considerations

ICH Topic E9 – see in particular

  • 3.5 Sample Size
  • 4.4 Sample size adjustment

ICH Topic E8(R1) General considerations for clinical studies - see in particular

  • 5.1 Study population
  • 5.6 Statistical analysis
Abbreviations
  • CTU – Clinical Trials Unit
  • ICH – International Council for Harmonisation
  • ICH GCP – International Council for Harmonisation Good Clinical Practice
  • SP-INV – Sponsor Investigator
Development ↦ Statistic Methodology ↦ Sample Size ↦ Justification
Study
Basic

Provides some background knowledge and basic definitions

Basic Monitoring
Concept

Starts with a study idea

Ends after having assessed and evaluated study feasibility

Concept Statistic Methodology
Concept Drug or Device
Development

Starts with confidence that the study is feasible

Ends after having received ethics and regulatory approval

Development Drug or Device
Set-Up

Starts with ethics and regulatory approval

Ends after successful study initiation

Set-Up Ethics and Laws
Set-Up Statistic Methodology
Set-Up Quality and Risk
Set-Up Drug or Device
Conduct

Starts with participant recruitment

Ends after the last participant has completed the last study visit

Conduct Statistic Methodology
Conduct Drug or Device
Completion

Starts with last study visit completed

Ends after study publication and archiving

Completion Drug or Device
Current Path (click to copy): Development ↦ Statistic Methodology ↦ Sample Size ↦ Justification