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W 12: Tipping Point Sensitivity Analysis in Continuous Asthma Quality of Life Questionnaire Endpoint





Poster Presenter

      Tulin Shekar

      • Associate Principle Statistician
      • Merck & Co., Inc.
        United States

Objectives

In this paper, sensitivity analysis will be performed using tipping point approach; based on recent FDA requests in current respiratory trials. Analysis will present randomized clinical trial questionnaire data.

Method

Sensitivity analysis using the tipping-point approach will be used to assess the robustness of the primary analysis approach. The Variant 3 of the tipping point as described in Ratitch et al. (2013) will be applied.

Results

Clinical Trial data illustrates sensitivity analysis in multiple imputations under the MNAR assumption by searching for a tipping point that reverses the study conclusion. The Variant 3 of the tipping point as described in Ratitch et al. (2013) will be applied. In that approach, missing data are first imputed for all visits under the MAR assumption, and then the worsening/shift is applied. This is repeated until the result is no longer statistically significant. The results of the sensitivity analysis indicate that the tipping point approach is not intended for the primary analysis method and is only used for the sensitivity analysis and the results are robust as compared to primary analysis. Details of numerical results will be provided in the poster.

Conclusion

One method, tipping point approach, has gained the popularity recently as an approach for performing the sensitivity analysis under the missing at not random (MNAR) assumption. In other words, the tipping point approach is like a progressive stress-testing to assess how severe departures from missing at random (MAR) must be in order to overturn conclusions from the primary analysis. The value of tipping point may be compared to the clinical meaningful difference or the estimated treatment difference from the primary analysis. This may provide a sense for us to interpret the robustness of the analysis results against the handling of missing data.