Getting less of what you want: reductions in statistical power and increased bias when categorizing medication adherence data

@article{Tueller2016GettingLO,
  title={Getting less of what you want: reductions in statistical power and increased bias when categorizing medication adherence data},
  author={Stephen J Tueller and Pascal R. Deboeck and Richard A. Van Dorn},
  journal={Journal of Behavioral Medicine},
  year={2016},
  volume={39},
  pages={969-980}
}
Medication adherence is thought to be the principal clinical predictor of positive clinical outcomes, not only for serious mental illnesses such as schizophrenia, bipolar disorder, or depression, but also for physical conditions such as diabetes. Consequently, research on medication often looks not only at medication condition (e.g., placebo, standard medication, investigative medication), but also at adherence in taking those medications within each medication condition. The percentage (or… CONTINUE READING