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 reproductive and women’s health, HIV/AIDS and sleep research. Research projects include modeling of changes in sleep and body composition through the menopausal transition, and modeling of labor progression.

Dr. Ratcliffe is co-PI 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.

Statistical Methodology Publications

Biostatistics Ph.D. Students

  • Angelo F. Elmi. "Curve registration in functional data analysis with informatively censored event-times." Graduated 2009.
  • ChengCheng Liu. "Joint modeling of non-gaussian longitudinal outcomes and time to event data." Graduated 2011.
  • Arwin Thomasson. "Joint longitudinal-survival model with possible cure: An analysis of patient outcomes on the liver transplant waiting list." Graduated 2012.