TY  - JOUR
SN  - 1017-0405
UR  - https://doi.org/10.5705/ss.202016.0308
PB  - Academia Sinica, Institute of Statistical Science
ID  - discovery1554767
N2  - 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.
KW  - Intention-to-treat analysis
KW  -  longitudinal data analysis
KW  -  mean score
KW  -  missing data
KW  -  randomised trials
KW  -  sensitivity analysis.
A1  - White, IR
A1  - Carpenter, J
A1  - Horton, NJ
JF  - Statistica Sinica
VL  - 28
AV  - public
Y1  - 2018/10//
TI  - A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
IS  - 4
ER  -