On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out.

@article{Demirtas2003OnTP,
  title={On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out.},
  author={Hakan Demirtas and Joseph L. Schafer},
  journal={Statistics in medicine},
  year={2003},
  volume={22 16},
  pages={2553-75}
}
Random-coefficient pattern-mixture models (RCPMMs) have been proposed for longitudinal data when drop-out is thought to be non-ignorable. An RCPMM is a random-effects model with summaries of drop-out time included among the regressors. The basis of every RCPMM is extrapolation. We review RCPMMs, describe various extrapolation strategies, and show how analyses may be simplified through multiple imputation. Using simulated and real data, we show that alternative RCPMMs that fit equally well may… CONTINUE READING

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