@article{Cro:340129,
      recid = {340129},
      author = {Cro, Suzie and Morris, Tim P. and Kenward, Michael G. and  Carpenter, James R.},
      title = {Reference-based sensitivity analysis via multiple  imputation for longitudinal trials with protocol deviation},
      journal = {Stata Journal},
      address = {2016-04},
      number = {199-2024-511},
      year = {2016},
      abstract = {Randomized controlled trials provide essential evidence  for the evaluation of new and existing medical treatments.  Unfortunately, the statistical analysis is often  complicated by the occurrence of protocol deviations, which  mean we cannot always measure the intended outcomes for  individuals who deviate, resulting in a missing-data  problem. In such settings, however one approaches the  analysis, an untestable assumption about the distribution  of the unobserved data must be made. To understand how far  the results depend on these assumptions, the primary  analysis should be supplemented by a range of sensitivity  analyses, which explore how the conclusions vary over a  range of different credible assumptions for the missing  data. In this article, we describe a new command, mimix,  that can be used to perform reference-based sensitivity  analyses for randomized controlled trials with longitudinal  quantitative outcome data, using the approach proposed by  Carpenter, Roger, and Kenward (2013, Journal of  Biopharmaceutical Statistics 23: 1352–1371). Under this  approach, we make qualitative assumptions about how  individuals’ missing outcomes relate to those observed in  relevant groups in the trial, based on plausible clinical  scenarios. Statistical analysis then proceeds using the  method of multiple imputation.},
      url = {http://ageconsearch.umn.edu/record/340129},
      doi = {https://doi.org/10.22004/ag.econ.340129},
}