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based on person years of exposure. So if there is differential exposure between treatment

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groups, the computation of this estimate adjusts for that difference. With low event rates

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and small differences in exposure, this estimate will not differ from a risk ratio computed

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as the ratio of incidences. For Saxagliptin, we thought this estimate was important for the

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short-term data where dropouts were generally significantly greater in the placebo group

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than the Saxagliptin group, and not as important for the short-term plus long-term period,

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although we will show estimates from both of the time periods.

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computed stratifying on study. I will explain what each of these offers to the

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interpretation of the CV results. For the risk difference, studies with no events are

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included in the analysis. This contrasts with methods for computing odds ratios where

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studies with no events are only included if a continuity correction is used. With the risk

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difference, we are able to assess the influence of the no event trials on the interpretation

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of risk when we look at this estimate against an odds ratio.

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set by the guidance was the odds ratio based on an exact test. Our summaries all contain

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these estimates. The confidence interval for this estimate tends to be conservative

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compared to intervals computed by other methods and therefore I think an attractive

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approach for assessing CV risk.

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The incidence rate ratio, that is the second one listed here, is computed

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Now the estimate we use for assessing risk in the context of the boundary

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Lastly, we thought it was important to analyze the results with more than one statistical

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method. From our experience with Avandia and other drugs, we knew that the method

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used could impact the results particularly with rare events and with multiple studies with

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no events.

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Now here are the estimates we considered. All these estimates are

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