Power analysis (English): Difference between revisions
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== Why perform a sample size calculation? == | == Why perform a sample size calculation? == | ||
The main reasons for perfomring a sample size calculation are ethical. If the number of subjects tested in a study is too small to detect the effect being investigated, | The main reasons for perfomring a sample size calculation are ethical. If the number of subjects tested in a study is too small to detect the effect being investigated, the subjects will be subjected to the risks of participating in the study in vain. | ||
vain. | |||
The study will easily result in a false negative conclusion. On the other hand, testing too many subjects may also lead to undesirable situations. If an intervention turns out to | The study will easily result in a false negative conclusion. On the other hand, testing too many subjects may also lead to undesirable situations. If an intervention turns out to | ||
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Finally, the logistic planning of a study benefits from a sample size calculation. | Finally, the logistic planning of a study benefits from a sample size calculation. | ||
== Referenties == | == Referenties == | ||
Revision as of 15:56, 29 January 2020
This text is an edited version of the AMC sample size calculation manual [1]. It provides a practical guide into sample size calculations used in clinical research. After reading the manual, a researcher will know:
- why power analysis is used to plan and evaluate medical research
- what power and statistical significance mean
- what information is needed for a sample size calculation
- where to find the information needed
- how to perform a simple sample size calculation
- how to write down a power calculation.
In addition, the manual contains two practical examples of sample size calculations.
Why perform a sample size calculation?
The main reasons for perfomring a sample size calculation are ethical. If the number of subjects tested in a study is too small to detect the effect being investigated, the subjects will be subjected to the risks of participating in the study in vain.
The study will easily result in a false negative conclusion. On the other hand, testing too many subjects may also lead to undesirable situations. If an intervention turns out to be effective, too many subjects have missed out on this intervention. If the intervention is not effective, too many have been exposed to this ineffective intervention. For these reasons a trial should always consider what number of subjects would be appropriate to answer the study question. Sample size calculations prior to a study can help focus on the number of subjects that is needed and sufficient for a study. Moreover, a sample size calculation helps one to focus on a clinically relevant effect, instead of the erroneous strategy of testing as many subjects as needed to reach statistical significance of an irrelevant effect.
The CONSORT statement (guideline for reporting clinical trials) states that a researcher should calculate study size on beforehand and should report this calculation in the methods section of the resulting scientific paper. The AMC Medical Ethics Board (MEC) and the Animal Experiments Committee (DEC) also ask for a power calculations in the approval process. The same holds for most study grant applications (e.g., ZonMW).
Finally, the logistic planning of a study benefits from a sample size calculation.
Referenties
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van Geloven N, Dijkgraaf M, Tanck M, Reitsma J. AMC biostatistics manual - Sample size calculation. 2009. Amsterdam: Academic Medical Center.