Monday, Aug 4: 8:30 AM - 10:20 AM
4043
Contributed Papers
Music City Center
Room: CC-209C
Main Sponsor
Biopharmaceutical Section
Presentations
Characterizing the dose-response relationship from early phase studies is important in making dose decisions for phase 3 studies. While there is rich research in the dose-response model for mono-therapies, the dose-response model has not been studied for the combination treatment. We propose a flexible and efficient estimation process by expressing the combination response as a function of the mean responses from individual mono-therapies. We present two primary families of combination dose response models on top of the additive and multiplicative models.
Closed-form solutions for parameter estimation are provided for certain models, while more complex models may require numerical methods. We illustrate that using a two-step procedure enables robust combination dose response estimation. The variance-covariance structure of these estimates can be derived via the Delta method or bootstrap to assess model variability. This framework is particularly valuable in scenarios where mono-therapy data is already available, allowing for a structured approach to modeling combination therapies.
Keywords
Combination dose-response
Drug interaction models
Clinical statistics
External control arms (ECAs) are increasingly used in clinical trials for rare diseases or when ethical concerns prevent the use of a placebo. A significant challenge in including an ECA in oncology trials for pre-treated patients is to determine the proper time zero (index date) such that they are comparable with the clinical trial patients. Because the number of prior lines of therapy and patient characteristics heavily influence the assessment of clinical benefits, a naïve comparison between external controls and the treated cohort in the current trial may lead to biased estimates of the causal treatment effect. We propose an Emax frailty model to simulate the correlated recurrent event times for multiple lines of therapy. We conduct extensive simulation studies to assess the impact on the bias of treatment effect, Type 1 error, and power of the Wald test from Cox models. Various methods of selecting time zero for a starting treatment line are considered, including random selection, all lines with and without variance adjustment, and matching treatment lines using propensity scores. We also assess the impact of adjusting for both measured and unmeasured confounders.
Keywords
External control
Index date
Emax model
frailty
Propensity score
In drug development, dose optimization is crucial and challenging due to the inherent variability and exploratory nature in early phase trials. It requires careful evaluation of dose-response and toxicity to ensure that the treatment is effective and safe while maintaining an acceptable level of tolerability. The FDA's Project Optimus Initiative highlights the importance of refined dose optimization strategies. To address this challenge, we propose the Dose Optimization Design using Patient-Reported Outcomes via Bayesian Additive Regression Trees (DOD-PRO-BART). This innovative approach integrates patient-reported outcomes with clinician-reported toxicity and efficacy data, enabling a more personalized and patient-centered method in both dose escalation and dose randomization. DOD-PRO-BART provides a comprehensive assessment of treatment effects, improving our understanding of patient responses to different dosage levels. Our simulation study illustrates that the proposed method can substantially improve the optimal dose selection by integrating patient-reported data along with clinician-reported toxicity and efficacy data.
Keywords
Dose optimization
Dose randomization
Patient-reported outcomes
Bayesian additive regression trees
The 2023 American Heart Association Guidelines have identified combination therapies as an important knowledge gap and an area of future research likely to offer the best chance of success for delayed cerebral ischemia (DCI) in patients with an aneurysmal spontaneous subarachnoid hemorrhage (aSAH). In this talk, we present an optimized Bayesian adaptive design to identify the best combination of Cilostazol and Human Albumin using a two-dimensional normal dynamic linear model. This design is shown to be smaller, stronger, faster, and benefit more trial participants than fixed and adaptive designs that use an independent model. Further, the two-dimensional approach avoids the difficulty of prespecifying the order of combination therapies required in a one-dimensional normal dynamic linear model.
Keywords
response adaptive randomization
smoothing ratio
phase II trial
In oncology drug development, the quality and interpretability of time-to-event endpoints, progression-free survival (PFS) and overall survival (OS), have long been recognized as significant challenges. These challenges arise from multiple factors, including issues related to data collection, analysis methodology for handling intercurrent events (ICEs) and missing data, trial integrity concerns related to study conduct, and complexities in interpreting multiple endpoints. For example, data challenges include imbalanced schedules of radiographic assessments, informative censoring due to Blinded Independent Central Review (BICR), incomplete or absent survival sweeps, a lack of clarity in defining lost-to-follow-up and withdrawal of consent, and inadequate collection of intercurrent event data. Adopting a prospective estimand framework helps mitigating risks associated with data collection, analysis methodology and interpretability. In this work, the DahShu Innovative Design Scientific Working Group (IDSWG) Oncology team systematically investigates the complexities of PFS and OS and presents practical recommendations according to E9(R1).
Keywords
Progression Free Survival
Overall Survival
Estimand
Oncology clinical trials
E9(R1)
Co-Author(s)
Revathi Ananthakrishnan, Bristol-Myers Squibb
Gu Mi, Sanofi
Laura Fernandes, COTA Healthcare Inc.
David Leung, Daiichi Sankyo
Caleb Lee, Daiichi Sankyo
Natalie Ren, iTeos Therapeutics
Sohail Chaudhry, Nektar Therapeutics
Hui Yang, Takeda
Helen Zhou, GSK
Haijun Ma, Exelixis
Cassie Dong
First Author
Philip He, Daiichi Sankyo Inc.
Presenting Author
Philip He, Daiichi Sankyo Inc.
It is well known that imbalanced disease assessment schedule (DAS) between arms could introduce bias in Progression-free survival (PFS) analysis. Understanding the magnitude of such potential bias on key study parameters would be greatly helpful to trial design and analysis, although have not been well-studied.
In this paper, we simulate comprehensive settings that are commonly seen in oncology clinical trials. We consider the scenarios with median PFS (mPFS) on the control arm ranging from 1.5 months to 24 months, and treatment cycles occurring every 3 weeks, 4 weeks or 6 weeks, and evaluate the impact of various disease assessment schedules on the following parameters: mPFS estimate, hazard ratio (HR) estimate, type 1 error and power. We use a real study to further illustrate how the factors including patients' deviations from the scheduled visits can impact the estimates.
In general, type 1 error is inflated and hazard ratio (treatment / control) is under-estimated when subjects in treatment arm are assessed less frequently, compared to control arm. The magnitude of the impact depends on the true median PFS, the frequency of the assessments, and how far apart the assessm
Keywords
Progression-free Survival,
Disease Assessment Schedule
Clinical Trial Design
In clinical trials setting, stratified randomization is frequently employed to mitigate imbalances between treatment groups related to specific factors. Such stratification variables are applied in the analyses to enhance analytical efficiency. For time-to-event endpoints, stratified Cox model is often used for analysis, which assumes a uniform treatment effect across all strata. However, the presence of predictive factors in stratified randomization and analysis, a situation that is not uncommon in real-world settings, can violate this assumption. To evaluate the impact of incorporating predictive factors in stratified analysis, the power of stratified analyses and unstratified analyses are compared. Furthermore, different methods of strata combination for stratified analysis in the context of including predictive factors are also explored and compared to address scenarios where certain strata exhibit very small event counts.
Keywords
Stratification
Stratified analysis
Predictive factors
Time-to-event