Graphical sensitivity analysis with different methods of imputation for a trial with probable non-ignorable missing data

Abstract

Graphical sensitivity analyses have recently been recommended for clinical trials with non-ignorable missing outcome. We demonstrate an adaptation of this methodology for a continuous outcome of a trial of three cognitive-behavioural therapies for mild depression in primary care, in which one arm had unexpectedly high levels of missing data. Fixed-value and multiple imputations from a normal distribution (assuming either varying mean and fixed standard deviation, or fixed mean and varying standard deviation) were used to obtain contour plots of the contrast estimates with their?P-values superimposed, their confidence intervals, and the root mean square errors. Imputation was based either on the outcome value alone, or on change from baseline. The plots showed fixed-value imputation to be more sensitive than imputing from a normal distribution, but the normally distributed imputations were subject to sampling noise. The contours of the sensitivity plots were close to linear in appearance, with the slope approximately equal to the ratio of the proportions of subjects with missing data in each trial arm.

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