Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results

Abstract

Background.

Modelers lack a tool to systematically and clearly present complex model results, including sensitivity analyses.

Objective.

To propose linear regression metamodeling as a tool to increase transparency of decision-analytic models and better communicate their results.

Methods.

We used a simplified cancer-cure model to demonstrate our approach. The model computed the lifetime cost and benefit of three treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the resulting regression coefficients to describe various sensitivity analyses measures, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs.

Results.

The regression intercept represented the estimated base-case outcome and the other coefficients described the relative parameter uncertainty in the model. In addition, we defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic one-way or two-way sensitivity analyses, but was more accurate since it used all parameter values.

Conclusions.

Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.