The use of computer adaptive tests in outcome assessments following upper limb trauma: a systematic review

Aims

Outcome measures quantifying aspects of health in a precise, efficient, and user-friendly manner are in demand. Computer adaptive tests (CATs) may overcome the limitations of established fixed scales and be more adept at measuring outcomes in trauma. The primary objective of this review was to gain a comprehensive understanding of the psychometric properties of CATs compared with fixed-length scales in the assessment of outcome in patients who have suffered trauma of the upper limb. Study designs, outcome measures and methodological quality are defined, along with trends in investigation.

Materials and Methods

A search of multiple electronic databases was undertaken on 1 January 2017 with terms related to "CATs", "orthopaedics", "trauma", and "anatomical regions". Studies involving adults suffering trauma to the upper limb, and undergoing any intervention, were eligible. Those involving the measurement of outcome with any CATs were included. Identification, screening, and eligibility were undertaken, followed by the extraction of data and quality assessment using the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) criteria. The review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria and reg istered (PROSPERO: CRD42016053886).

Results

A total of 31 studies reported trauma conditions alone, or in combination with non-traumatic conditions using CATs. Most were cross-sectional with varying level of evidence, number of patients, type of study, range of conditions and methodological quality. CATs correlated well with fixed scales and had minimal or no floor-ceiling effects. They required significantly fewer questions and/or less time for completion. Patient-Reported Outcomes Measurement Information System (PROMIS) CATs were the most frequently used, and the use of CATs is increasing.

Conclusion

Early studies show valid and reliable outcome measurement with CATs performing as well as, if not better than, established fixed scales. Superior properties such as floor-ceiling effects and ease of use support their use in the assessment of outcome after trauma. As CATs are being increasingly used in patient outcomes research, further psychometric evaluation, especially involving longitudinal studies and groups of patients with specific injuries are required to inform clinical practice using these contemporary measures. Cite this article: Bone Joint J 2018;100-B:693-702.

The Accuracy of Computerized Adaptive Testing in Heterogeneous Populations: A Mixture Item-Response Theory Analysis

BACKGROUND

Computerized adaptive testing (CAT) utilizes latent variable measurement model parameters that are typically assumed to be equivalently applicable to all people. Biased latent variable scores may be obtained in samples that are heterogeneous with respect to a specified measurement model. We examined the implications of sample heterogeneity with respect to CAT-predicted patient-reported outcomes (PRO) scores for the measurement of pain.

METHODS

A latent variable mixture modeling (LVMM) analysis was conducted using data collected from a heterogeneous sample of people in British Columbia, Canada, who were administered the 36 pain domain items of the CAT-5D-QOL. The fitted LVMM was then used to produce data for a simulation analysis. We evaluated bias by comparing the referent PRO scores of the LVMM with PRO scores predicted by a "conventional" CAT (ignoring heterogeneity) and a LVMM-based "mixture" CAT (accommodating heterogeneity).

RESULTS

The LVMM analysis indicated support for three latent classes with class proportions of 0.25, 0.30 and 0.45, which suggests that the sample was heterogeneous. The simulation analyses revealed differences between the referent PRO scores and the PRO scores produced by the "conventional" CAT. The "mixture" CAT produced PRO scores that were nearly equivalent to the referent scores.

CONCLUSION

Bias in PRO scores based on latent variable models may result when population heterogeneity is ignored. Improved accuracy could be obtained by using CATs that are parameterized using LVMM.