Layla Parast

Photo of Layla Parast
Senior Statistician
Santa Monica Office


Ph.D. in biostatistics, Harvard University; M.S. in statistics, Stanford University; B.S. in mathematics, University of Texas, Austin


Layla Parast is a statistician at the RAND Corporation and co-director of the Center for Causal Inference. She is the principal investigator of an R01 from the National Institute of Diabetes and Digestive and Kidney Diseases that focuses on developing novel nonparametric statistical methods to assess the value of surrogate markers of diabetes using data from the Diabetes Prevention Program Outcomes Study. In addition, she was recently project director of the National Implementation of the Emergency Department Patient Experience of Care Discharged to Community (EDPEC DTC) Survey project (completed November 2020). This project involved the development and testing of the EDPEC DTC Survey, including cognitive testing of survey items, multiple mode experiments, the development of public materials for voluntary implementation, and successful application for the CAHPS Survey trademark (now, ED CAHPS Survey). Her statistical research focuses on robust estimation of intervention effects, developing and evaluating risk prediction procedures for long-term survival, causal inference methods, nonparametric kernel-based methods, and methods to assess surrogacy. Her substantive research at RAND is focused on evaluating patient experience, developing and testing quality measures in a variety of settings, investigating process-outcome relationships, assessing quality of care, evaluating substance use screening tools in a pediatric setting, and examining the effects of interventions aimed at reducing substance use and risky behaviors. Parast received her Ph.D. in biostatistics from Harvard University, her M.S. in statistics from Stanford University, and her B.S. in mathematics from the University of Texas at Austin.

Recent Projects

  • National Implementation of the Emergency Department Patient Experience of Care Discharged to Community Survey
  • Robust Statistical Methods to Identify Surrogate Markers in Diabetes
  • Preparation for National Implementation of the Emergency Department Patient Experience of Care Discharged to Community Survey
  • National Implementation of the CAHPS Hospice Survey
  • Robust Statistical Methods to Identify and Use Surrogate Markers in Diabetes

Selected Publications

Parast L, Cai T and Tian L, "Evaluating Surrogate Marker Information using Censored Data," Statistics in Medicine, 36(11), 2017 (forthcoming)

Parast L, Doyle B, Damberg CL, Shetty K, Ganz DA, Wenger NS, Shekelle PG, "Challenges in Assessing the Process Outcome Link in Practice," Journal of General Internal Medicine, 30(3), 2015

Parast L, Tian L, Cai T., "Landmark Estimation of Survival and Treatment Estimation in a Randomized Clinical Trial," Journal of the American Statistical Association, 109(505), 2014

Parast L, Cai T, "Landmark Risk Prediction for Breast Cancer Survival," Statistics in Medicine, 32(20), 2013

Parast L, Cheng S, Cai T., "Landmark Prediction of Long Term Survival Incorporating Short Term Event Time Information," Journal of the American Statistical Association, 107(500), 2012

Parast L, Cheng S, Cai T, "Incorporating Short-term Outcome Information to Predict Long-term Survival with Discrete Markers," Biometrical Journal, 53, 2011

Parast L, McDermott M, and Tian L, "Robust Estimation of the Proportion of Treatment Effect Explained by Surrogate Marker Information," Statistics in Medicine, 35(10), 2016

Sharek PJ, Parast LM, Leong K, Combs J, Ernst K, Sullivan J, Frankel LR, Roth SJ (2007)., "Effect of Rapid Response Team on Hospital-wide Mortality and Code Rates Outside the ICU in a Childrens Hospital," Journal of the American Medical Association, 298(19), 2007

Honors & Awards

  • RAND Health Bob Brook Scholar Award for Early-career Researchers, RAND
  • ASA Biometrics Section Travel Award, ASA
  • Laha Travel Award, Institute of Mathematical Statistics, IMS


  • An IV and an epidural machine with a pregnant woman lying on a hospital bed

    A Tale of Two Deliveries, or an Out-of-Network Problem

    Two mothers gave birth within weeks of each other, at the same hospital, using the same employer-sponsored insurance. Both had an epidural. But one received a surprise physician bill for anesthesiology, while the other didn't have to pay a dime. Why?

    Nov 4, 2015 Health Affairs Blog