Meaningful within-patient change for clinical outcome assessments: model-based approach versus cumulative distribution functions.

Abstract

Objectives: The FDA recommends the use of anchor-based methods and empirical cumulative distribution function (eCDF) curves to establish a meaningful within-patient change (MWPC) for a clinical outcome assessment (COA). In practice, the estimates obtained from model-based methods and eCDF curves may not closely align, although an anchor is used with both. To help interpret their results, we investigated and compared these approaches.

Methods: Both repeated measures model (RMM) and eCDF approaches were used to estimate an MWPC on a target COA. We used both real-life (ClinicalTrials.gov: NCT02697773) and simulated data sets that included 688 patients with up to six visits per patient, target COA (range 0 to 10), and an anchor measure on patient global assessment of osteoarthritis from 1 (very good) to 5 (very poor). Ninety-five percent confidence intervals for the MWPC were calculated by the bootstrap method.

Results: The distribution of the COA score changes affected the degree of concordance between RMM and eCDF estimates. The COA score changes from simulated normally distributed data led to greater concordance between the two approaches than did COA score changes from the actual clinical data. The confidence intervals of MWPC estimate based on eCDF methods were much wider than that by RMM methods, and the point estimate of eCDF methods varied noticeably across visits.

Conclusions: Our data explored the differences of model-based methods over eCDF approaches, finding that the former integrates more information across a diverse range of COA and anchor scores and provides more precise estimates for the MWPC.