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. 2018 Oct;28(4):1985-2003.
doi: 10.5705/ss.202016.0308.

A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials

Ian R White  1   2 , James Carpenter  2   3 , Nicholas J Horton  4
Affiliations

A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials

Ian R White et al. Stat Sin. 2018 Oct.

Abstract

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.

Keywords: Intention-to-treat analysis; Longitudinal data analysis; Mean score; Missing data; Randomised trials; Sensitivity analysis.

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Figures

Figure 1
Figure 1
QUATRO trial: sensitivity analysis for the estimated intervention effect on the MCS (with 95% confidence interval) over a range of departures from MAR.
Figure 2
Figure 2
QUATRO data: effective sample size in sensitivity analysis.
See this image and copyright information in PMC

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