Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes.

@article{Prague2016AccountingFI,
  title={Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes.},
  author={M{\'e}lanie Prague and Rui Wang and Alisa J. Stephens and Eric J Tchetgen Tchetgen and Victor DeGruttola},
  journal={Biometrics},
  year={2016},
  volume={72 4},
  pages={1066-1077}
}
Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions… CONTINUE READING