Fernando Alarid-Escudero, PhD

Fernando Alarid-Escudero, PhD

Health Decision Scientist

Talks and presentations

The DARTH initiative: Promoting the Use of Open-Source Software in Medical Decision Making

January 01, 2018

Oral presentation, Erasmus University, Rotterdam, The Netherlands

Open-source software (OSS) has gained significant popularity across many academic disciplines such as statistics and engineering. There have been many developments and exhibitions of the use of OSS in health decision sciences (HDS), but certainly not enough. Some of the most popular models used in HDS are being implemented in either domain-specific or proprietary software. There are many advantages to this approach, but there are also significant drawbacks, namely high entry costs, lack of transparency and a fragmented modeling approach. The Decision Analysis in R for Technologies in Health (DARTH) initiative is a multi-institutional, multi-university collaborative effort comprised of scholars who have a passion for transparent and open-source solutions to decision analysis in HDS. This meeting will highlight the benefits of OSS, the research advancements and educational initiatives currently available in decision analysis using OSS and will foster discussion around the many exciting ways in which OSS can be used to advance the field of HDS.

Comparison of calibration methods for natural history simulation models

October 23, 2016

Conference proceedings talk, 38th Annual North American Meeting of the Society for Medical Decision Making, Vancouver, Canada

Abstract

Purpose

Disease natural history models often contain parameters that are unknown or unobservable for different reasons (e.g., ethical or financial). Calibration is the process of estimating these parameters by matching model outputs to observed clinical or epidemiological data. Our objective is to compare four different calibration methods on how they perform recovering the true parameters.

Calibration of piecewise Markov models using a Bayesian change-point analysis through an iterative convex optimization algorithm

September 07, 2015

Conference proceedings talk, ISPOR 5th Latin America Conference, Santiago, Chile

Abstract

Purpose

Relative survival, as reported by the Surveillance, Epidemiology, and End Results (SEER) Program, represents cancer survival in the absence of other causes of death. Often, cancer Markov models have a distant metastasis state, a state not directly observed in SEER, from which cancer deaths are presumed to occur. The aim of this research is to use a novel approach to calibrate the transition probabilities to and from an unobserved state of a Markov model to fit a relative survival curve.

Calibration of piecewise Markov models using a Bayesian change-point analysis through an iterative convex optimization algorithm

July 29, 2015

Conference proceedings talk, INFORMS Healthcare Conference, Nashville, TN

Abstract

Purpose

Relative survival, as reported by the Surveillance, Epidemiology, and End Results (SEER) Program, represents cancer survival in the absence of other causes of death. Often, cancer Markov models have a distant metastasis state, a state not directly observed in SEER, from which cancer deaths are presumed to occur. The aim of this research is to use a novel approach to calibrate the transition probabilities to and from an unobserved state of a Markov model to fit a relative survival curve.