Matching-adjusted indirect comparisons: Application to time-to-event data.

The Matching-Adjusted Indirect Comparison method (MAIC) is a recent methodology that allows to perform indirect comparisons between two drugs assessed in two different studies, where individual patients data are available in only one of the two studies, the data of the other one being available in an aggregate format only. In this work, we have assessed the properties of the MAIC method and compared, through simulations, several ways of practical implementation of the method. We conclude that it is more efficient to match the treatment arms separately (match the two drugs to compare on one hand, and the control arms on the other hand) and use the Lasso technique to select the covariates for the matching step is better than matching a maximal set of covariates.

Design optimization for dose-finding trials: a review.

Dose selection is one of the most difficult and crucial decisions to make during drug development. As a consequence, the dose-finding trial is a major milestone in the drug development plan and should be properly designed. This article will review the most recent methodologies for optimizing the design of dose-finding studies: all of them are based on the modeling of the dose-response curve, which is now the gold standard approach for analyzing dose-finding studies instead of the traditional ANOVA/multiple testing approach. We will address the optimization of both fixed and adaptive designs and briefly outline new methodologies currently under investigation, based on utility functions.

Randomized Phase III Study of Amcenestrant Plus Palbociclib Versus Letrozole Plus Palbociclib in Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer: Primary Results From AMEERA-5.

PURPOSE: AMEERA-5 investigated amcenestrant (oral selective estrogen receptor [ER] degrader) plus palbociclib versus letrozole plus palbociclib as first-line treatment for ER-positive/human epidermal growth factor receptor 2-negative (ER+/HER2-) advanced/metastatic breast cancer (aBC). MATERIALS AND METHODS: In AMEERA-5 (ClinicalTrials.gov identifier: NCT04478266), a double-blind, double-dummy, international phase III trial, adult pre-/post-menopausal women and men without previous systemic therapy for ER+/HER2- aBC were randomly assigned 1:1 to amcenestrant 200 mg once daily + standard palbociclib dosage (125 mg once daily, 21 days on/7 days off) or letrozole 2.5 mg once daily + standard palbociclib dosage, stratified by de novo metastatic disease, postmenopausal women, and visceral metastasis. The primary end point was progression-free survival (PFS), compared using a stratified log-rank test with one-sided type I error rate of 2.5%. Secondary end points included overall survival (key secondary), pharmacokinetics, and safety. RESULTS: Between October 14, 2020, and December 2, 2021, 1,068 patients were randomly assigned to amcenestrant + palbociclib (N = 534) or letrozole + palbociclib (N = 534). At the interim analysis (median follow-up 8.4 months), the stratified hazard ratio for PFS was 1.209 (95% CI, 0.939 to 1.557; one-sided P value = .9304); therefore, the study was stopped for futility. The 6-month PFS rate was 82.7% (95% CI, 79.0 to 85.8) with amcenestrant + palbociclib versus 86.9% (95% CI, 83.5 to 89.6) with letrozole + palbociclib. In the amcenestrant + palbociclib versus letrozole + palbociclib groups, treatment-emergent adverse events (any grade) occurred in 85.6% versus 85.4% of patients and grade ≥3 events in 46.3% versus 60.8%, respectively. CONCLUSION: The AMEERA-5 study was discontinued on the basis of the recommendation of the data monitoring committee at the interim futility analysis. No new safety signals were identified.

Development and validation of a predictive model to guide the use of plerixafor in pediatric population.

Plerixafor, a CXCR4 receptor antagonist, reduces the binding and chemotaxis of hematopoietic stem cells to the bone marrow stroma, resulting in predictable peak of cluster of differentiation 34+ (CD34+) cells in the peripheral blood (PB) approximately 10 h after its administration. We developed a model that could predict the CD34+ harvest volume on the first day of apheresis (AP-CD34+) based on PB-CD34+ counts immediately prior to commencing apheresis in pediatric population. In all, data from 45 pediatric patients from the MOZAIC study who received either granulocyte colony-stimulating factor (G-CSF) alone or G-CSF plus plerixafor were included. The modeling of the data exhibited a strong and highly predictive linear relationship between the counts of PB-CD34+ cells on the first day of apheresis and AP-CD34+ cells collected on the same day. It is predicted that there are approximately 13 new collected CD34+ cells for 100 new circulating CD34+ cells before apheresis. Our predictive algorithm can be used to quantify the minimal count of PB-CD34+ cells that enables to collect at least 2 × 106 or 5 × 106 AP-CD34+ cells/kg with sufficient assurance (probability = 0.90) and can guide the use of plerixafor in patients at higher perceived risk for mobilization failure. Trial registration of MOZAIC study: ClinicalTrials.gov, NCT01288573; EudraCT, 2010-019340-40.

Utility-Based Dose Selection for Phase II Dose-Finding Studies.

BACKGROUND AND OBJECTIVES: Dose selection is a key feature of clinical development. Poor dose selection has been recognized as a major driver of development failure in late phase. It usually involves both efficacy and safety criteria. The objective of this paper is to develop and implement a novel fully Bayesian statistical framework to optimize the dose selection process by maximizing the expected utility in phase III. METHODS: The success probability is characterized by means of a utility function with two components, one for efficacy and one for safety. Each component refers to a dose-response model. Moreover, a sequential design (with futility and efficacy rules at the interim analysis) is compared to a fixed design in order to allow one to hasten the decision to perform the late phase study. Operating characteristics of this approach are extensively assessed by simulations under a wide range of dose-response scenarios. RESULTS AND CONCLUSIONS: Simulation results illustrate the difficulty of simultaneously estimating two complex dose-response models with enough accuracy to properly rank doses using an utility function combining the two. The probability of making the good decision increases with the sample size. For some scenarios, the sequential design has good properties: with a quite large probability of study termination at interim analysis, it enables to reduce the sample size while maintaining the properties of the fixed design.