Sensitivity analysis for randomized trials with missing outcome data

Any analysis with incomplete data makes untestable assumptions about the missing data, and analysts are therefore urged to conduct sensitivity analyses. Ideally, a model is constructed containing a nonidentifiable parameter d, where d = 0 corresponds to the assumption made in the standard analysis, and the value of d is then varied in a range considered plausible in the substantive context. I have produced Stata software for performing such sensitivity analyses in randomized trials with a single outcome, when the user specifies a value or range of values of d. The analysis model is assumed to be a generalized linear model with adjustment for baseline covariates. I will describe the statistical model used to allow for the missing data, sketch the programming required to obtain a sandwich variance estimator, and describe modifications needed to make the results given when d = 0 correspond exactly to those results available by standard methods. I will illustrate the use of the software for binary and continuous outcomes, when the standard analysis assumes either missing at random or (for a binary outcome) "missing = failure".