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2006-

Subgroup Analysis: Large, complex data sets are becoming more commonplace and people want to know which subgroups are responding differently to one another and why. The overall sample is often quite large, but subgroups may be very small and there are often many questions. Genetic data is being collected on clinical trials. Which patients will respond better to a drug and which will have more severe side effects? Disease, drug, or side effects can result from different mechanisms. Identification of subgroups of people where there is a common mechanism is useful for diagnosis and prescribing of treatment. Large educational surveys involve groups with different demographics, different educational resources and subject to different educational practices. What groups are different; how are differences related to resources and practices? What really works and why? Is the finding the result of chance? There is a need for effective statistical methods for finding subgroups that are responding differently. There is a need to be able to identify complex patterns of response and not be fooled by false positive results that come about from multiple testing. Our idea is to bring together statisticians and subject experts to develop and explore statistical strategies to address the subgroup problem. The benefit will be creditable statistical methods that are likely to produce results that will replicate in future studies.

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