Reproducing Expert Judgement with Shortened Surveys

Thomas M. Menino Convention & Exhibition Center 

Patient-reported outcome measures allow us to measure constructs that are often not directly observable. The items are summarized through a simple unweighted summed score or through more advanced methods, including factor analysis, and item response theory. Shortening questionnaires reduces respondent burden, cost, and can increase data quality. However, shortened forms may have lower precision in latent trait estimation, but can they be used for accurate prediction of a diagnosis or expert judgment? We use a Markov Chain Monte Carlo algorithm to find shortened forms that maintain diagnostic accuracy. We demonstrate its efficiency through simulation studies, and apply it to a screener for alcohol use disorder.

Patient Reported Outcome Measures

Shortening Surveys

Markov Chain Monte Carlo 

ENAR