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Sensitivity analysis for randomized trials with missing outcome data


  • Ian White

    () (MRC Biostatistics Unit, Cambridge University)


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".

Suggested Citation

  • Ian White, 2011. "Sensitivity analysis for randomized trials with missing outcome data," United Kingdom Stata Users' Group Meetings 2011 03, Stata Users Group.
  • Handle: RePEc:boc:usug11:03

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