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Gary L. Kamer Statistician OSB/DBS. 2 Statistical Issues at Time of PMA Review Clinical Study Design Excess All-cause Late Mortality (31 to 365 days)

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Presentation on theme: "Gary L. Kamer Statistician OSB/DBS. 2 Statistical Issues at Time of PMA Review Clinical Study Design Excess All-cause Late Mortality (31 to 365 days)"— Presentation transcript:

1 Gary L. Kamer Statistician OSB/DBS

2 2 Statistical Issues at Time of PMA Review Clinical Study Design Excess All-cause Late Mortality (31 to 365 days)

3 3 Clinical Study Design Non-randomized Study Study Requires Treatment and Control Patients to be Anatomically Different Treatment Arm Comparability Issues

4 Results Indicated Excess All- cause Late Mortality for Treatment Patients Standard-risk Graft Patients 6 of 199 (3.0%) Control Patients 1 of 80 (1.3%) p = 0.3553 OR = 2.456 (0.29, 114.4) Difference = 1.77% (-5.85%, 9.27%)

5 5 Mortality Concerns All-cause Late Mortality endpoint showing a numerically greater mortality rate for the standard-risk graft patients in a study not powered for mortality Numerically high mortality rates for roll-in and high-risk graft patients

6 6 Possible Solutions to Study Design and Excess Mortality Issues Patient by patient clinical evaluation of mortality risk factors Covariate analysis including logistic regression Propensity score analysis

7 7 Propensity Score Analysis A propensity score is the conditional probability of a patient receiving the active treatment rather than the control, given a collection of observed covariates. The purpose of a propensity score analysis is to attempt to simultaneously balance many covariates in the two treatment groups, thus trying to reduce bias.

8 8 Propensity Score Analysis (cont) The propensity score model was built using backward stepwise regression. The resulting model utilizes ten variables and a constant term. 270 patients are then grouped into five quintiles (54 per quintile) based on propensity score.

9 9 Propensity Score Analysis (cont) For all-cause late mortality, the propensity score analysis has resulted in an increase in p-value from 0.3553 to 0.709. This suggests that the standard-risk graft patients may have been sicker than the control patients at baseline. However, the analysis suffers from the lack of mortality information on 37 patients and the use of appropriate statistical procedures to include these patients in the analysis.

10 10 Summary Given the need for conducting a less than optimally designed clinical study, the use of propensity scores has provided study results adjusted for measured covariates.

11 11 Summary (cont) Neither clinical evaluation, standard covariate analysis nor propensity score analysis is capable of adjusting for non-measured covariates or the existence of an anatomical difference between treatment groups; a properly- designed randomized study would have been necessary to accomplish this.

12 12 Summary (cont) The propensity score analysis should be considered along with the patient by patient clinical evaluation and the covariate analysis in assessing the safety and effectiveness of this AAA graft.

13 13 Summary (cont) These three analyses should be evaluated in terms of their providing similar results, their limitations and their comprehensiveness. The low statistical power to detect differences in mortality rates between study arms should also be considered.


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