Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis.
Journal:
Journal of evaluation in clinical practice
PMID:
29888469
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single treatment unit's outcome is studied over time, and the intervention is expected to "interrupt" the level and/or trend of the outcome, subsequent to its introduction. The internal validity of this analysis is strengthened considerably if the treated unit is contrasted with a comparable control group. In this paper, we introduce a novel machine learning approach using optimal discriminant analysis (ODA) to evaluate treatment effects in multiple-group ITSA.