Missing data sensitivity analysis for binary outcomes derived from underlying continuous variables

Music City Center 

Multiple imputation is a common approach for analysis in the presence of missing data in clinical trials. For continuous outcomes with missing data, a δ-adjusted approach on the imputed outcomes is commonly used to assess the tipping-points at which the observed results become insignificant. For binary outcomes, the primary analysis can be performed naturally by deriving the binary outcome from the (imputed) underlying continuous variable(s). Then, for each imputation, the δ-adjusted sensitivity analysis can be performed using the following steps: impute the underlying continuous variables and estimate the probability for the binary outcome for each subject based on the primary analysis model; employ an additive δ adjustment on the logit scale; the missing binary outcome is simulated from a Bernoulli distribution with the δ-adjusted probability; the "complete" data after imputation are analyzed using the same statistical model as the primary analysis; repeat above steps M times and the results from the M imputations are then combined using traditional methods. The tipping points are then generated and summarized. We apply this method to data from a real clinical trial.

Missing data

Binary outcome

Sensitivity Analysis

Multiple Imputation

Tipping points 

Biopharmaceutical Section