An analytic strategy for modeling multiple-item responses: a breast cancer symptom example

Item Response Theory (IRT) is increasingly applied in health research to combine information from multiple-item responses. IRT posits that a person's susceptibility to a symptom is driven by the interaction of the characteristics of the symptom and person. This article describes the statistical background of incorporating IRT into a multilevel framework and extends this approach to longitudinal health outcomes, where the self-report method is used to construct a multi-item scale.

A secondary analysis of data from 2 descriptive longitudinal studies is performed. The data include 21 symptoms reported across time by 350 women with breast cancer. A 3-level hierarchical linear model (HLM) was used for the analysis. Level 1 models the item responses, consisting of symptom presence or absence. Level 2 models the trajectory of each individual, representing change over time of the IRT-created latent variable symptom experience. Level 3 explains that trajectory using person-specific characteristics such as age and location of care. The purpose of the analysis is to examine if older and younger women with breast cancer differ in their symptom experience trajectory after controlling for location of care.

Fatigue and pain were the most prevalent symptoms. The symptom experience of women with breast cancer was found to improve over time. Neither age nor location of care was significantly associated with the symptom experience trajectory.

Embedding IRT into an HLM framework produces several benefits. The example provided demonstrates benefits through the creation of a latent symptom experience variable that can be used either as an outcome or as a covariate in another model, examining the latent symptom experience trajectory and its relationship with covariates at the individual level, and managing symptom nonresponse.