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Simulation program for power and sample size determination in logistic analysis of single nucleotide polymorphisms
Tomomi Yamada1, Naoko Kinukawa, Tsuyoshi Nakamura
1Department of Medical Information Science, Kyushu University Hospital, Fukuoka, Japan. t-yamada@doc.medic.mie-u.ac.jp
Computer Methods and Programs in Biomedicine
|May 1, 2009
Summary
This study developed a simulation program to calculate sample sizes for clinical studies investigating genetic-disease associations. The program accounts for misclassification errors, crucial for accurate statistical power calculations in genetic research.
Area of Science:
- Biostatistics
- Genetic Epidemiology
- Clinical Trial Design
Background:
- Retrospective studies can identify potential genetic-disease associations.
- Accurate sample size determination is critical for confirming these associations in clinical studies.
- Misclassification of data can significantly impact statistical power.
Purpose of the Study:
- To develop a simulation program for determining clinical study sample sizes.
- To evaluate the impact of response misclassification on statistical power.
- To provide a tool for sample size calculation that accounts for misclassification.
Main Methods:
- Development of a simulation program for sample size calculation.
- Investigation of misclassification effects on statistical power using Pitman asymptotic relative efficiency.
- Estimation of exact statistical power in the presence of misclassification.
Main Results:
- A simulation program was developed to estimate sample size and statistical power.
- Misclassification, even at low rates, seriously reduces statistical power.
- A general expression for power decrease due to misclassification was derived.
Conclusions:
- Response misclassification must be considered during sample size determination for clinical studies.
- The developed simulation program can be accessed online.
- Accurate sample size calculations are essential for reliable genetic-disease association studies.
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