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Group sequential methods and sample size savings in biomarker-disease association studies
R Aplenc1, H Zhao, T R Rebbeck
1Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104-6021, USA. raplenc@cceb.med.upenn.edu
Genetics
|March 29, 2003
Summary
Group sequential methods (GSM) can terminate molecular epidemiology studies early, saving biosamples and costs. This statistical approach efficiently identifies non-associated genotypes, like GSTM1 in prostate cancer, reducing unnecessary genotyping.
Area of Science:
- Molecular epidemiology
- Statistical genetics
- Cancer research
Background:
- Molecular epidemiological studies are resource-intensive, utilizing valuable biosamples and incurring significant costs.
- Early termination methods can potentially conserve biosamples and reduce overall study expenses.
Purpose of the Study:
- To evaluate the application of group sequential methods (GSM) for early termination in molecular epidemiology.
- To assess the potential for biosample and cost savings using GSM in genotype-association studies.
Main Methods:
- Simulation studies were conducted using case-control data for GST genotypes and prostate cancer.
- Group sequential boundaries (GSB) were defined using EAST-2000 software.
- Evaluated study termination based on interim analyses suggesting the null hypothesis was unlikely to be rejected.
Main Results:
- Early termination occurred in over 90% of simulated studies for GSTM1 genotyping, which showed no association with prostate cancer.
- An average of 36.4% of biosamples were saved from unnecessary genotyping.
- Inappropriate termination for GSTT1, showing a positive association, occurred in only 6.6% of simulations.
Conclusions:
- Group sequential methods (GSM) offer significant potential for biosample and cost savings in molecular epidemiology.
- GSM can efficiently terminate studies with no significant genotype-disease associations, optimizing resource allocation.
- The application of GSM is effective in managing resources for genetic association studies.