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

## TL;DR

This paper introduces a mean score method for conducting sensitivity analysis in randomized trials to assess the impact of departures from the missing at random assumption, enhancing the robustness of outcome analyses.

## Contribution

It proposes a new, interpretable, and efficient mean score approach for sensitivity analysis under pattern mixture models in randomized trials.

## Key findings

- Method is fast, non-stochastic, and aligns with standard methods under certain parameters.
- Sensitivity parameters are easy to interpret and elicitable from experts.
- Illustrated with real data from a mental health trial.

## 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.