An Item Response Theory-Informed Strategy to Model Total Score Data from Composite Scales.

ABSTRACT: Composite scale data is widely used in many therapeutic areas and consists of several categorical questions/items that are usually summarized into a total score (TS). Such data is discrete and bounded by nature. The gold standard to analyse composite scale data is item response theory (IRT) models. However, IRT models require item-level data while sometimes only TS is available. This work investigates models for TS. When an IRT model exists, it can be used to derive the information as well as expected mean and variability of TS at any point, which can inform TS-analyses. We propose a new method: IRT-informed functions of expected values and standard deviation in TS-analyses. The most common models for TS-analyses are continuous variable (CV) models, while bounded integer (BI) models offer

SUBMITTER: Wellhagen GJ 

PROVIDER: S-EPMC7966126 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature