Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence.

It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.

Indirect Treatment Comparison of Daratumumab, Pomalidomide, and Dexamethasone Versus Standard of Care in Patients with Difficult-to-Treat Relapsed/Refractory Multiple Myeloma.

INTRODUCTION: The phase 3 APOLLO study demonstrated significantly better progression-free survival (PFS) and clinical responses with daratumumab, pomalidomide, and dexamethasone (D-Pd) versus pomalidomide and dexamethasone (Pd) in patients with relapsed/refractory multiple myeloma (RRMM). On the basis of these results and those from the phase 1b EQUULEUS trial, D-Pd was approved in this patient population. In the absence of head-to-head data comparing D-Pd with further standard of care (SOC) therapies, indirect treatment comparisons (ITCs) can provide important information to help optimize treatment selection. The objective of this study was to indirectly compare PFS improvement with D-Pd versus daratumumab, bortezomib, and dexamethasone (D-Vd) and D-Pd versus bortezomib and dexamethasone (Vd) in patients with RRMM. METHODS: Patient-level data were from APOLLO, EQUULEUS, and CASTOR. Three methods of adjusting imbalances in baseline characteristics including stabilized inverse probability of treatment weighting (sIPTW), cardinality matching (CM), and propensity score matching (PSM) were initially considered. CM offers mathematically guaranteed largest matched sample meeting pre-specified maximum standardized mean difference criteria for matching covariates. sIPTW and PSM were based on propensity scores derived from logistic regression. Feasibility assessment of the PSM method returned too low effective sample size to support a meaningful comparison. CM was chosen as the base case and sIPTW as a sensitivity analysis. RESULTS: After harmonized eligibility criteria were applied, 253, 104, and 122 patients from the D-Pd, D-Vd, and Vd cohorts, respectively, were included in the ITC analyses. Some imbalances in baseline characteristics were identified between D-Pd and D-Vd/Vd cohorts that remained after adjustment. PFS hazard ratios showed significant improvement for D-Pd over D-Vd and Vd for CM and sIPTW analyses. CONCLUSIONS: Results showed consistent PFS benefit for D-Pd versus D-Vd and Vd regardless of the adjustment technique used. These findings support the use of D-Pd versus D-Vd or Vd in patients with difficult-to-treat RRMM. TRIAL REGISTRATION: NCT03180736; NCT02136134, NCT01998971.