Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.

@article{Touloumi2001ImpactOM,
  title={Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.},
  author={Giota Touloumi and Abdel G. Babiker and Stuart J. Pocock and J. H. M. Darbyshire},
  journal={Statistics in medicine},
  year={2001},
  volume={20 24},
  pages={3715-28}
}
Many cohort studies and clinical trials are designed to compare rates of change over time in one or more disease markers in several groups. One major problem in such longitudinal studies is missing data due to patient drop-out. The bias and efficiency of six different methods to estimate rates of changes in longitudinal studies with incomplete observations were compared: generalized estimating equation estimates (GEE) proposed by Liang and Zeger (1986); unweighted average of ordinary least… CONTINUE READING

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