Improved Decision-Making Confidence Using Item-Based Pharmacometric Model: Illustration with a Phase II Placebo-Controlled Trial

Abstract

This study aimed to illustrate how a new methodology to assess clinical trial outcome measures using a longitudinal item response theory–based model (IRM) could serve as an alternative to mixed model repeated measures (MMRM). Data from the EXACT (Exacerbation of chronic pulmonary disease tool) which is used to capture frequency, severity, and duration of exacerbations in COPD were analyzed using an IRM. The IRM included a graded response model characterizing item parameters and functions describing symptom-time course. Total scores were simulated (month 12) using uncertainty in parameter estimates. The 50th (2.5th, 97.5th) percentiles of the resulting simulated differences in average total score (drug minus placebo) represented the estimated drug effect (95%CI), which was compared with published MMRM results. Furthermore, differences in sample size, sensitivity, specificity, and type I and II errors between approaches were explored. Patients received either oral danirixin 75 mg twice daily (n = 45) or placebo (n = 48) on top of standard of care over 52 weeks. A step function best described the COPD symptoms-time course in both trial arms. The IRM improved precision of the estimated drug effect compared to MMRM, resulting in a sample size of 2.5 times larger for the MMRM analysis to achieve the IRM precision. The IRM showed a higher probability of a positive predictive value (34%) than MMRM (22%). An item model–based analysis data gave more precise estimates of drug effect than MMRM analysis for the same endpoint in this one case study.

Improved Confidence in a Confirmatory Stage by Application of Item-Based Pharmacometrics Model: Illustration with a Phase III Active Comparator-Controlled Trial in COPD Patients

“…A median relative sample size over the 4-week intervals of 4.0 (FF/UMEC/VI) and 4.9-fold (BUD/FOR) larger would have been required for the MMRM analysis to achieve the IRT precision. These values are comparable with a 3.5-fold smaller study size with IRT compared to MMRM analysis using RS-Total data from a Phase II trial ( 24 ). This is particularly important in cases where a large number of patients cannot be recruited for pivotal trials (e.g.…”

“…Using data from a Phase III study, a longitudinal IRT improved the precision in the efficacy endpoint compared to MMRM. The use of NLME analysis based on item-level data (IRT) was recently proposed as an alternative to MMRM for evaluation of efficacy in the analysis of data from a Phase II study, where IRT improved the precision of the estimated drug effect considerably in comparison to MMRM ( 24 ). The benefits of using an IRT over other statistical methods such as least-square mean analysis have already been demonstrated, highlighting its higher power to detect a drug effect ( 13 , 25 ).…”

“…Furthermore, a relative sample size (N; obtained using Eq. 4) of 4.00 (FF/UMEC/VI) and 4.72 (BUD/FOR) times larger would be required in the MMRM analysis to achieve the precision obtained with the IRT analysis at the end of the study (week [21][22][23][24]. Sample size requirements at each week interval are shown in Fig.…”

Item response theory in early phase clinical trials: Utilization of a reference model to analyze the Montgomery‐Åsberg Depression Rating Scale

“…The benefit of IRT‐based analysis versus total score analysis of clinical trial data has already been demonstrated by studies using both empirical and simulation data to show that estimation of the treatment effect is less biased when applying IRT 6,7 . IRT has also increasingly been applied in the field of nonlinear mixed effects (NLME) modeling, where longitudinal models describing disease progression and treatment effect are now developed based on the change in ψ over time instead of the total score 3,8–14 . Many of these studies have shown that the statistical power to determine treatment effect using an IRT approach is higher when compared to using the total score data in NLME modeling 9,10,13 .…”

“… 6 , 7 IRT has also increasingly been applied in the field of nonlinear mixed effects (NLME) modeling, where longitudinal models describing disease progression and treatment effect are now developed based on the change in ψ over time instead of the total score. 3 , 8 , 9 , 10 , 11 , 12 , 13 , 14 Many of these studies have shown that the statistical power to determine treatment effect using an IRT approach is higher when compared to using the total score data in NLME modeling. 9 , 10 , 13 These studies either used hypothetical questionnaires for simulations or were based on data from questionnaires used in clinical practice, such as the State Trait Anxiety Index Dutch Y‐version (STAI‐DY), the Exacerbation of Chronic Pulmonary Disease Tool (EXACT), the Unified Parkinsons Disease Rating Scale (UPDRS), and Hamilton depression rating scale (HAMD‐17).…”