Multi-dimensional Models for Outcomes Measurement
Patient-reported outcomes (PRO) data have contributed enormously to health services
research. However, they are complex to conceptualize and use, and investigators have
expressed the need for a flexible set of analytic and scoring methods that can account for
the multi-dimensionality of PRO data as well as provide interpretable scores that are
useful for various research and policy applications.
Existing methods have a limited ability to capture the multi-dimensional
characteristics of PRO data, which include a range of broad domains, such as health-related
quality of life (HRQOL, such as physical functioning and social role participation) and
symptoms (fatigue, pain, depression). Each domain is considered multi-dimensional in that
it includes a variety of heterogeneous indicators.
Currently, both the classical and modern measurement approaches commonly used to
analyze and score PRO data assume that one dominant factor underlies each domain and
accounts for most of the variation in scores. This is called the assumption of
unidimensionality. If more than one factor exists, the domain must be divided into
sub-domains to apply these methods. Under this restrictive set of assumptions, efforts to
summarize these data into broader constructs suffer from the lack of clear statistical and
analytical decision-rules. Measures are often simply added
together to create a combined score, or, alternatively, patient or expert judgment is used
to weight these factors within or across domains.
More complex psychometric modeling methods exist that take advantage of the
correlations among PRO domains to improve the measurement of patients at the sub-domain
level. Other new models, such as the bi-factor model, can account for the hierarchical
nature of the data, thereby improving the estimation of summary scores of various HRQOL
and symptom domains in cancer and other diseases.
The Outcomes Research Branch funded Dr. Robert Gibbons (Director, Center for Health Statistics and Professor of
Biostatistics and Psychiatry at the University of Illinois-Chicago) to assess the value of
using these innovative psychometric models to improve PRO measurement. These
models create a profile of scores as well as an overall summary score, an improvement over
standard approaches used in PRO measurement.
The results of this study can be found in The Added Value of Multidimensional IRT