• 2017 – Ph.D., Health Services Research, Policy & Administration, University of Minnesota.
  • 2009 – Master in Economics, CIDE.
  • 2006 – Biomedical Engineering, Metropolitan Autonomous University, Iztapalapa

Academic appointments:

  • Professor-researcher at the Drug Policy Program (PPD) at the Center for Research and Teaching in Economics (CIDE) Central Region in Aguascalientes, Mexico.
  • Member of the Cancer Surveillance and Intervention Modeling Network (CISNET).
  • Researcher affiliated with CISIDAT.
  • Level 1 in the National System of Researchers (SNI)
  • Founding partner of the Decision Analysis in R for Technologies in Health working group (DARTH; http://darthworkgroup.com/)

Research interests:

  • Application and development of epidemiological, computational, economic, statistical, and decision analysis models for health problems and public health policies.
  • Development of novel methods to quantify the value of future research.

Featured projects:

  • Impact evaluation of different detection and treatment policies for colorectal, cervical, and gastric cancers, and infectious disease agents as precursors of cancers such as human papillomavirus (HPV) and Helicobacter pylori (H. pylori) on health and quality life of the population.
  • Cost-effectiveness analysis of different HIV / AIDS prevention interventions in Central America.
  • Methodological work: co-developed a new efficient method to perform information value analysis (VOI) using a Gaussian approximation approach. He defined the problem of non-identifiability in the calibration of simulation models and its implications for decision-making in health. He implemented new Bayesian calibration methodologies to quantify and propagate the uncertainty of a colorectal cancer natural history model’s parameters to assess its impact on health policy analysis. He has also developed open-source software to quantify future research value and estimate the price based on the value of healthcare technologies. He developed an open-source framework for building cost-effectiveness analyzes based on decision models. He quantified the extrapolation bias by using clinical trial parameters used to extrapolate the effectiveness of health technologies beyond the evaluation period and proposed a new method to eliminate this bias.


  • Alarid-Escudero F∗, Kuntz KM. Potential bias associated with modeling the effectiveness of health- care interventions in reducing mortality using an overall hazard ratio. PharmacoEconomics, 2019 (Forthcoming). The code implementing these methods can be found in the R package dshr (https://github.com/feralaes/dshr).
  • Attanasio L, Alarid-Escudero F, Kozhimannil KB. Midwife-led care and obstetrician-led care for low-risk pregnancies: A cost comparison Birth, 2019 (Online First).
  • Alarid-Escudero F∗, Krijkamp E, Pechlivanoglou P, Jalal H, Kao SY, Yang A, Enns EA. A need for change! A coding framework for improving transparency in decision modeling. PharmacoEconomics, 2019 (Online First). The coding template is implemented in the R package darthpack (https://github.com/DARTH-git/darthpack).
  • Alarid-Escudero F∗, Enns EA, Kuntz KM, Michaud TL, Jalal H. “Time Traveling Is Just Too Dangerous” But Some Methods Are Worth Revisiting: The Advantages of Expected Loss Curves Over Cost-Effectiveness Acceptability Curves and Frontier. Value in Health, 2019; 22(5):611-618. Use these methods with the R package dampack (https://github.com/DARTH-git/dampack).
  • Sawaya G, Sanstead E, Alarid-Escudero F, Smith-McCune K, Gregorich SE, Silverberg M, Leyden W, Huchko MJ, Kuppermann M, Kulasingam S Estimated Quality of Life and Economic Outcomes Associated With 12 Cervical Cancer Screening Strategies: A Cost-effectiveness Analysis. JAMA Internal Medicine, 2019;179(7):867-878.
  • Kunst N, Alarid-Escudero F, Paltiel D, Wang SY. A value of information analysis of research on the 21-gene assay for breast cancer management. Value in Health, 2019;22(10):1102-1110.
  • Jutkowitz E, Alarid-Escudero F∗, Kuntz KM, Jalal H. The Curve of Optimal Sample Size (COSS): a Graphical Representation of the Optimal Sample Size from a Value of Information Analysis. PharmacoEconomics, 2019;37(7):871-877. Download code at https://github.com/feralaes/COSS.
  • Sathianathen NJ, Alarid-Escudero F, Kuntz KM, Lawrentschuk NL, Bolton DM, Murphy DG, Kim SP, Konety BR. A Cost-effectiveness Analysis of Systemic Therapy for Metastatic Hormone- sensitive Prostate Cancer. European Urology Oncology, 2019 ;2(6):649-755.
  • Sathianathen NJ, Konety BR, Alarid-Escudero F, Lawrentschuk NL, Bolton DM, Murphy DG, Weight CJ, Kuntz KM. Cost-effectiveness Analysis of Active Surveillance Strategies for Men with Low-risk Prostate Cancer. European Urology, 2019;75(6):910-917.
  • Alarid-Escudero F∗, MacLehose RF, Peralta Y, Kuntz KM, Enns EA. Non-identifiability in model calibration and implications to medical decision making. Medical Decision Making, 2018;38(7):810- 21.
  • Easterly CA, Alarid-Escudero F∗, Enns EA, Kulasingam S. Revisiting Assumptions about Age- Based Mixing Representations in Mathematical Models of Sexually Transmitted Infections. Vaccine, 2018;36(37):5572-5579. Download code at https://zenodo.org/record/1322780#.Xcn4Yy2ZPOQ
  • Alarid-Escudero F∗, Enns EA, MacLehose R, Parsonnet J, Torres J, Kuntz KM. Force of infection of H. pylori in Mexico: Evidence from a national survey using a hierarchical Bayesian model. Epidemiology and Infection, 2018;146(8):961-9.
  • Sathianathen NJ, Kuntz KM, Alarid-Escudero F, Lawrentschuk NL, Bolton DM, Murphy DG, Weight CJ, Konety BR. Incorporating biomarkers into the primary prostate biopsy setting: a cost- effectiveness analysis. The Journal of Urology, 2018;200(6):1215-1220.
  • Krijkamp EM, Alarid-Escudero F, Enns EA, Jalal H, Hunink MGM, Pechlivanoglou P. Microsimulation modeling for health decision sciences using R: A tutorial. Medical Decision Making, 2018;38(3):400- 422. Download code at https://github.com/DARTH-git/Microsimulation-tutorial.
  • Jalal H, Alarid-Escudero F. A Gaussian Approximation Approach for Value of Information Analysis. Medical Decision Making, 2018;38(2):174-188. Download code at https://github.com/feralaes/VOI-Gaussian-Approximation.