Doubly robust inference for targeted minimum loss-based estimation in randomized trials with missing outcome data.

@article{Daz2017DoublyRI,
  title={Doubly robust inference for targeted minimum loss-based estimation in randomized trials with missing outcome data.},
  author={Iv{\'a}n D{\'i}az and Mark J. van der Laan},
  journal={Statistics in medicine},
  year={2017},
  volume={36 24},
  pages={
          3807-3819
        }
}
  • Iván Díaz, Mark J. van der Laan
  • Published 2017
  • Mathematics, Medicine
  • Statistics in medicine
  • Missing outcome data is a crucial threat to the validity of treatment effect estimates from randomized trials. The outcome distributions of participants with missing and observed data are often different, which increases bias. Causal inference methods may aid in reducing the bias and improving efficiency by incorporating baseline variables into the analysis. In particular, doubly robust estimators incorporate 2 nuisance parameters: the outcome regression and the missingness mechanism (ie, the… CONTINUE READING

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