  Most analyses of randomised trials with incomplete outcomes make untestable
assumptions and should therefore be subjected to sensitivity analyses. However,
methods for sensitivity analyses are not widely used. We propose a mean score
approach for exploring global sensitivity to departures from missing at random
or other assumptions about incomplete outcome data in a randomised trial. We
assume a single outcome analysed under a generalised linear model. One or more
sensitivity parameters, specified by the user, measure the degree of departure
from missing at random in a pattern mixture model. Advantages of our method are
that its sensitivity parameters are relatively easy to interpret and so can be
elicited from subject matter experts; it is fast and non-stochastic; and its
point estimate, standard error and confidence interval agree perfectly with
standard methods when particular values of the sensitivity parameters make
those standard methods appropriate. We illustrate the method using data from a
mental health trial.
