Sarah J. Ratcliffe, Ph.D.

Dr. Ratcliffe's statistical methodological research focuses on functional data analysis (models for complex curves), and the analysis of longitudinal data with informative dropout, particularly joint models for longitudinal and survival data. Her collaborative areas of interest include neonatology, reproductive and women’s health, and HIV/AIDS. Research projects include modeling of changes during labor, and modeling RFM data.

Dr. Ratcliffe is MPI of an NIH funded research grant on Semi-parametric joint models for longitudinal and time to event data.


  • Matlab programs from: Ratcliffe SJ, Guo G and Ten Have TR. Joint modelling of longitudinal and survival data via a common frailty. Biometrics, 60(4):892-899, 2004
  • Matlab programs from: Guo W, Ratcliffe SJ and Ten Have TR. A Random pattern-mixture model for longitudinal data with dropouts. Journal of the American Statistical Association, 99:929-937, 2004.
  • Matlab programs from: Ratcliffe SJ and Shults J. GEEQBOX: A Matlab toolbox for generalized estimating equations and quasi-least squares. Journal of Statistical Software, 25(14):1-14, 2008.


A complete list of published work is available in MyBibliography, Google Scholar, and Research Gate

Statistical Publications


Biostatistics Ph.D. Students

Epidemiology Ph.D. Students

  • Sydney E.S. Brown. "The epidemiology of readmissions to the intensive care unit and its use as a quality indicator." Graduated 2013. Dissertation available from ProQuest
  • Michael Harhay. Research focuses on design and analysis issues encountered in intensive care unit-based clinical trials