Sunday, September 26, 2010

Do we really need to statistical soluation for everything?

I came back from last week's FDA/Industry Statistics Workshop with more questions than answers. While the theme for this year's workshop is on risk benefit assessment, the old regular issues such as multiplicity, missing data, meta analysis, adaptive design, subgroup analysis,...are still the hot topics. For both the new (risk benefit assessment) and the old topics, there are more questions being raised and for many, there are no clear answer to these questions.

For adaptive design and non-inferiority clinical trials, FDA issued the draft guidance early this year; however, both guidance were written more like a technical report for statistician, and unlikely to be understood by the non-statisticians. For non-inferiority design, more questions were raised about the subjectivity / objectivity in determining the non-inferiority margin. For risk-benefit assessment, perhaps, we have to rely on the medical experts in the specific therapeutic area to make their subjective judgment based on the separate marginal analyses of Benefit (efficacy) and Risk (Safety) instead of different weighted modeling approaches. Perhaps, there is no simple mathematical and statistical solution for the benefit risk assessment. I believe that the advisory committee members were making subjective judgments based on their experience in voting in favor of or against a product for benefit and risk assessment - like Jury's verdict.

it is not a good thing that as statisticians, we come up with some complicated statistical methodology which we can not explain well to the non-statisticians. Eventually, we may need to go back to the basics to follow the KISS (Keep it simple) principle. Several years ago, the complicated and bad math that nobody could really understand caused the financial crisis. A working paper, Computational complexity and informational asymmetry in financial products, Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge. sheds some light on the complex mathematical models upon which credit default obligations and other derivatives are based.

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