endpoints form bounds on the estimated treatment effect and would be used in place of point estimates. Accompanying these bounds would be a 95 percent confidence region. This procedure can be viewed as accounting for both sampling variability and variability due to model uncertainty (i.e., uncertainty about the sensitivity parameter value): see Molenberghs and Kenward (2007) for more detailed discussion and recommendations for computing a 95 percent confidence region.

A second possibility is to carry out inference under MAR and determine the set of sensitivity parameter values that would lead to overturning the conclusion from MAR. Results can be viewed as equivocal if the inference about treatment effects could be overturned for values of the sensitivity parameter that are plausible.

The third possibility is to derive a summary inference that averages over values of the sensitivity parameters in some principled fashion. This approach could be viewed as appropriate in settings in which reliable prior information about the sensitivity parameter value is known in advance.

Regardless of the specific approach taken to decision making, the key issue is weighting the results, either formally or informally, from both the primary analysis and each alternative analysis by assessing the reasonableness of the assumptions made in conjunction with each analysis. The analyses should be given little weight when the associated assumptions are viewed as being extreme and should be given substantial weight when the associated assumptions are viewed as being comparably plausible to those for the primary analysis. Therefore, in situations in which there are alternative analyses as part of the sensitivity analysis that support contrary inferences to that of the primary analysis, if the associated assumptions are viewed as being fairly extreme, it would be reasonable to continue to support the inference from the primary analysis.


Recommendation 15: Sensitivity analyses should be part of the primary reporting of findings from clinical trials. Examining sensitivity to the assumptions about the missing data mechanism should be a mandatory component of reporting.

We note that there are some often-used models for the analysis of missing data in clinical trials for which the form of a sensitivity analysis has not been fully developed in the literature. Although we have provided principles for the broad development of sensitivity analyses, we have not been prescriptive for many individual models. It is important that additional research be carried out so that methods to carry out sensitivity analyses for all of the standard models are available.

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