ADVANCED TOPICS IN DECISION ANALYTIC MODELING

Format Requirements: The course will include a combination of didactic lectures, discussions and hands-on exercises. This course is designed for those with basic understanding of decision analytic modeling (e.g. comfortable with introductory modeling courses offered at SMDM) and interested in building relatively complex models. Basic understanding of calculas and statistics is helpful but is not required. It is recommended that participants bring a computer.

Course Description and Objectives Guidelines

This course will cover advanced topics in the following three modeling approaches: cohort-based models, patient-level models, and population-based models. We will provide several examples illustrating the mistakes and bias, and practical solutions to avoid such mistakes. Participants will acquire the following skills from this short course.                                 

1.     Discrete-time Markov model (DTMM)

The choice of DTMM with commonly used cycle lengths (e.g., a year) can lead to biased outcomes.

a)    Half-cycle correction: Understand biases in modeling outcomes (costs, life-years, etc.) by ignoring half-cycle correction in DTMM and learn different methods for half-cycle correction to reduce bias.

b)    Competing risk in Markov models:Understand issues with changing cycle lengths in Markov models without accounting for competing risk, and learn methods to incorporate competing risks to avoid bias in results.

2.     Continuous-time Markov model (CTTM)

Since in most biological and healthcare systems state transitions can occur at any time, use of DTMM instead of CTMM can result in biased outcomes. Using a series of examples, we illustrate the formulation and numerical simulation of DTMM and CTMM, and examine the differences between the two approaches.

3.     Cohort- versus individual-level models

Understand biases with cohort-based approach because of non-linear relationship between the model inputs and outcomes, and how individual-level models (Markov simulation models, discrete event simulations, agent-based models), can incorporate heterogeneity in data.

4.     Static versus dynamic models

Understand differences between "static" (e.g. Markov) versus dynamic population models and learn about incorporating indirect effects into static models of infectious diseases using analytical formulae.

5.     Probabilistic sensitivity analysis (PSA)

Learn different sampling techniques (e.g. Latin Hypercube) to efficiently perform PSA in individual-level models, which can be computationally challenging and impractical with a commonly used Monte Carlo sampling.