Performance of longitudinal item response theory models in shortened or partial assessments

Abstract

This work evaluates the performance of longitudinal item response (IR) theory models in shortened assessments using an existing model for part II and III of the MDS-UPDRS score. Based on the item information content, the assessment was reduced by removal of items in multiple increments and the models’ ability to recover the item characteristics of the remaining items at each level was evaluated. This evaluation was done for both simulated and real data. The metric of comparison in both cases was the item information function. For real data, the impact of shortening on the estimated disease progression and drug effect was also studied. In the simulated data setting, the item characteristics did not differ between the full and the shortened assessments down to the lowest level of information remaining; indicating a considerable independence between items. In contrast when reducing the assessment in a real data setting, a substantial change in item information was observed for some of the items. Disease progression and drug effect estimates also decreased in the reduced assessments. These changes indicate a shift in the measured construct of the shortened assessment and warrant caution when comparing results from a partial assessment with results from the full assessment.

Application of longitudinal item response theory models to modeling Parkinson’s disease progression

“…The application of longitudinal IRT models to the study of neurological diseases has increased recently. To our knowledge, there are 13 prior papers of relevance to our work using longitudinal IRT models: eight dealing with PD 10 , 11 , 12 , 13 , 14 , 15 , 28 , 29 and the others dealing with other neurological or psychiatric conditions. 27 , 30 , 31 , 32 , 33 All of them have made significant contributions to the field.…”

“…Similarly, Gottipati et al 15 analyzed all three parts of MDS‐UPDRS using separate IRT models with the latent variable representing patient‐reported responses, sided responses, and non‐sided responses. Last, Arrington et al 14 applied a unidimensional longitudinal IRT model to motor‐related items in MDS‐UPDRS parts 2 and 3 and assessed the impact of reducing the number of items. Outside of PD, Ueckert et al 30 used IRT to study Alzheimer’s disease, and Novakovic et al 32 used the technique in multiple sclerosis and concluded that IRT modeling is an effective tool to study disease progression with item‐level data.…”

“…Given the different nature of each part, issues around combining and interpretation arise and it has been recommended to analyze each part as a separate but correlated domain 3 . Previous IRT investigations involving PD are contributory to the field, but have the following limitations: (1) used cross‐sectional IRT models on longitudinal data 11,12 ; (2) adopted a two‐step sequential parameter estimation process that does not estimate all parameters simultaneously 13 ; and (3) assumed unidimensionality 14 or modeled the disease progression separately to each of the latent variables but without regard to their internal relationship 12,15 . In comparison, multidimensional longitudinal IRT models with multiple latent variables offer the unique opportunity of investigating different progression patterns and treatment responses in more than one related, but nevertheless distinct, domains of diseases 16,17 .…”

Novel Approach to Movement Disorder Society–Unified Parkinson's Disease Rating Scale Monitoring in Clinical Trials: Longitudinal Item Response Theory Models

“…Item response theory (IRT) models can be used to analyze data from rating scales such as the MDS‐UPDRS 3 , 4 , 5 , 6 in a way that addresses these highly pertinent clinical and statistical issues. By examining the relationship of items and scales to the underlying latent trait of PD severity, captured conceptually by the term theta , IRT approaches can identify curvilinearity among potential subdomains.…”

“…1 There is a low rate of change of MDS-UPDRS scores in early PD that, coupled with high variance, leads to large sample size requirements to test efficacy specifically in disease modification trials. 2 Item response theory (IRT) models can be used to analyze data from rating scales such as the MDS-UPDRS [3][4][5][6] in a way that addresses these highly pertinent clinical and statistical issues. By examining the relationship of items and scales to the underlying latent trait of PD severity, captured conceptually by the term theta, IRT approaches can identify curvilinearity among potential subdomains.…”