Poster Session II: P19-P23

The integrity of randomization is critical for the success of a clinical trial. Therefore, routinely performing randomization checks is important. These checks must be done by an unblinded statistical representative to ensure the appropriate blinding is preserved. For fixed randomization designs, changes in the randomization parameters are not expected (same treatments, ratio, stratification, etc.), throughout the study. Standard randomization checks consist of source randomization scheme compare, sufficient remaining records, randomizations occurring as expected, stratification (anomalies), and balance (overall and within any applicable subgroups). These checks are usually done at pre-defined timepoints or milestones (e.g., at ~10%, 50%, 75% subjects randomized).

Complex Innovative Designs such as Master Protocols, often expect randomization adaptations and changes to the randomization parameters (e.g., introducing new treatments, closing treatments, ratio allocation adjustments, introducing new subgroups) across the study. Most of the same randomization checks apply to these designs as the fixed designs, but due to their adaptive nature, there are key differences. Specifically, the reviews need to account for the different adaptions occurring, and the randomization needs to be reviewed separately for each adaptation setting period. The occurrences of these reviews are more driven by events (e.g., after introducing a new treatment, closing a treatment, ratio adjustment) versus relying solely on milestones. These reviews may also be needed to make data driven decisions for adaptations (e.g., when to adjust ratios, close treatments).

This poster intends establish the importance of randomization monitoring, outline recommended randomization review checks, and compare review approaches for fixed versus complex innovative designs. 

Cluster randomization trials with survival endpoint are predominantly used in drug development and clinical care research when drug treatments or interventions are delivered at a group level. Unlike conventional cluster randomization design, stratified cluster randomization design is generally considered more effective in reducing the impact of imbalanced baseline prognostic factors and varying cluster sizes between groups when these stratification factors are adopted in the design. Failure to account for stratification and cluster size variability may lead to underpowered analysis and inaccurate sample size estimation. Apart from the sample size estimation in unstratified cluster randomization trials, there are no development of explicit sample size formulas for survival endpoint when a stratified cluster randomization design is employed. In this article, we present closed-form sample size formulas based on stratified and clustered log-rank statistics for stratified cluster randomization trials with survival endpoint. It provides an integrated solution for sample size estimation that account for cluster size variation, baseline hazard heterogeneity, and the estimated intracluster correlation coefficient based on the preliminary data. Simulation studies show that the proposed formulas provide the appropriate sample size for achieving desired statistical power under various parameter configurations. A real example of a stratified cluster randomization trial in the population with stable coronary heart disease is presented to illustrate our method. 

For topical, dermatological drug products, an in-vitro option to determine bioequivalence (BE) between test and reference products is recommended. In particular, In Vitro Permeation Test (IVPT) data analysis uses a reference-scaled approach for two primary endpoints, cumulative amount (AMT) and maximum flux (Jmax), which takes the within subject variability into consideration.
In December 2022, FDA released a new guidance, specific to IVPT studies (www.fdanews.com/10-21-22-InVitroPermeationTestStudiesdraftguidance.pdf), that presents in detail the statistical analysis method applicable to both balanced and unbalanced IVPT data. We are currently working on expanding the IVPT statistical analysis method to data coming from other types of studies, such as in-vivo dermal Open-Flow Microperfusion (dOFM) and microdialysis. Based on existing open questions from drug development submissions over the studies mentioned above, new research is presented on the following topics on the IVPT study data:
1. The development of objective criteria (i.e., statistical tests) to determine and characterize outliers
2. The optimized use of an adaptive design with sample size re-estimation that leverages data from both the pilot and the pivotal IVPT studies
3. The adaptation of the bioequivalence (BE) assessment to a model-based approach using a mixed effects model that accounts for both the skin donors and their corresponding replicate skin sections.
We aim to address these questions by both simulated and real data. The performance of the introduced tests in terms of statistical power will be discussed. 

The hazard ratio has long been considered to be the gold standard in estimating treatment effects for survival data. However, the hazard ratio relies on the proportional hazards assumption, which has been shown to not always hold, and is especially suspect for cancer immunotherapies. In these cases, the hazard ratio is not interpretable and is unable to meaningfully describe the magnitude of a treatment effect. As an alternative, we propose an adaptation of the Mann-Whitney Wilcoxon statistic called the Mitigated Fraction (MF) for evaluating survival data. The MF has been circulating in the literature under various names (Siev, 2005), and the Wilcoxon statistic has been proposed for use in survival analysis before under the name of Gehan's U (1965). We differ from Gehan in that we use the version of MF for censored data proposed by Zhang, et al. (2020), which corrects for censoring through the use of the Kaplan-Meier estimator. This results in the MF being a non-parametric estimator of the risk difference between two treatment groups which can also be regarded as a generalized metric, since it does not rely on the proportional hazards assumption, but still has a 1:1 relation to the hazard ratio when the assumption holds. To support our proposal, we provide an explanation of the formulation of the MF for survival data, some of its properties, and a power analysis on simulated data that shows that the MF's ability to detect treatment effects is comparable to that of the restricted mean survival time and the difference in median survival. 

Immunotherapy is a cutting-edge treatment that utilizes the patient's immune system to combat cancer. However, current clinical trial designs for immunotherapy rely on intricate parametric models that are challenging to validate in practice and interpret clinically, hindering the translation of statistical design into practical trial implementation and producing undesirable outcomes. To address these issues, we propose the UFO design, a curve-free phase I/II clinical trial design that incorporates the distinct characteristics of immunotherapy to identify the optimal biological dose (OBD) that maximizes the risk-benefit trade-off for patients. The UFO design dynamically determines the dose escalation and de-escalation by simultaneously considering immune response, toxicity, and efficacy, allocating patients to the dose with the highest desirability in the risk-benefit tradeoff. Unlike most existing designs, the UFO design does not rely on any parametric model assumptions and produces favorable results under any clinically significant dose-response curves. Additionally, we have extended the UFO design to handle delayed outcomes. Our comprehensive simulation studies demonstrate that the UFO design produces favorable operating characteristics.