13

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

14

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

15

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

16

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

17

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

18

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

19

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

6

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

7

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

8

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

9

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

10

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

11

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

21

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

22

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

23

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

24

approach for assessing CV risk.

12

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

20

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

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1

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

2

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

3

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

4

no events.

5

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

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