Wednesday, Aug 7: 10:30 AM - 12:20 PM
5147
Contributed Papers
Oregon Convention Center
Room: CC-E144
Main Sponsor
Biopharmaceutical Section
Presentations
In clinical trials, patient-reported outcomes (PRO), encompassing multiple scales, have been widely used in measuring patients' quality of life, especially in oncology trials. The generalized pairwise comparison (GPC) combines the primary endpoint (e.g., overall survival) and PRO as the composite endpoint to assess the treatment effect. The prioritized GPC method requires a well-defined hierarchical order for each component within the composite endpoint. However, establishing a clear ranking order for each PRO scale is challenging. Non-prioritized GPC methods treat all components equally, thereby diminishing the importance of the primary endpoint. In response, we propose a semi-prioritized method that avoids the need for a strict order of PRO scales while simultaneously preserving the importance of the primary endpoint. We redefined the scoring algorithm, utilizing all endpoints observed in a trial to estimate the composite treatment effect, with particular emphasis on the primary endpoint. We conducted comprehensive simulation studies to assess the limitations of existing methods and applied all tests to data from an oncology trial.
Keywords
Generalized Pairwise Comparison
Composite Endpoint
Patient-Reported Outcomes
Oncology Trials
The study of time-varying covariates (TVCs) gains attention in both statistical and medical fields. An example of a TVC is the receipt of hematopoietic cell transplantation (HCT) after CAR-T infusion, as patients may receive HCT after infusion, or not at all. The standard Cox model and Kaplan-Meier (KM) curve (Naïve method) may introduce "immortal time bias" since they assume TVC status known at baseline. Landmark analysis and time-dependent (TD) Cox model is two alternatives, but visualization of survival curves remains challenging. A novel visualization, Smith-Zee, based on TD Cox model, addresses this issue by mimicking new patients with TVC status change at different times, which overcomes drawbacks of the Naïve and Landmark methods. In this study, we developed a novel R Shiny tool called TVCurveTM to address these challenges and TVCurveTM incorporates various models: Naïve Cox, landmark Cox, and the TD Cox, along with multiple survival curves such as Naïve KM, Landmark KM, and Smith-Zee. Our tool TVCurve breaks collaboration barriers since it does not require data sharing between institutions but ensures standardized analyses across diverse datasets.
Keywords
R Shiny
Time Varying Covariates
Time-dependent Cox model
Survival Curves
Visualization
Current Precision Medicine (CPM) matches cancer therapies to consensus molecular characteristics at one or more timepoints. However, it is well-known that cancers contain extensive subclonal heterogeneity and that their subclonal compositions evolve dynamically in response to therapy. Mathematical modeling of this subclonal evolution has the potential to optimize the timing and sequencing of therapies in an even more effective and personalized manner than CPM. Clinical trial designs that test Evolutionary Guided Precision Medicine (EGPM) strategies that may prevent or delay relapse thereby improving outcomes, are needed. We evaluated Dynamic Precision Medicine (DPM), an EGPM, vs CPM in a stratified randomized design with two strata based on whether the patient was predicted to benefit from DPM, using an evolutionary classifier. We present this new proof-of-concept clinical trial design for this purpose and perform computer simulations which show high power, control of false positive rates, and robust performance in the face of anticipated challenges to clinical translation. The design is distinct from biomarker-driven designs of CPM, and can provide a robust evaluation of EGPM.
Keywords
Precision Medicine
Clinical Trial Design
Tumour Evolution
Oncology
In oncology clinical trials, estimates of OS is often confounded because some patients in the control group switch to the experimental treatment. Several switching adjustment methods have been developed to adjust for treatment switching, such as RPSFTM and two-stage method. However, many clinical trials now have patients in the control group switching to different treatment regimens other than the experimental treatments, and most of the existing methods still assume all switchers switch to the same treatment thus cannot handle multilevel switching. Stratified RPSFTM has been proposed to adjust for multilevel switching, however, the method does not change RPSFTM's assumption that treatment effect is the same for all participants regardless of when treatment is received. Two-stage method adjusts for switching that occurs after a specific disease-related time-point, which is a more practical assumption than RPSFTM. Thus, we extended the two-stage method to stratified two-stage model to adjust for multilevel treatment switching and outperforms other methods when the treatment effect is strong and when there is ineligible difference between the switching treatments.
Keywords
Treatment switching
Two-stage method
Multi-level switching
Randomized Clinical Trial
RPSFTM
Net benefit and win ratio are gaining interest in oncology and cardiovascular research for assessing multifaceted clinical outcomes. They compare multiple outcomes hierarchically by using all possible pairs from treatment and control groups. For time-to-event outcomes suffering from censoring, the inverse probability of censoring weighting (IPCW) is available. However, Dong's (2021) original IPCW formulation does not distinguish (a) "ties" in uncensored prioritized outcomes and (b) "uninformative" comparison due to censoring when considering lower-priority outcomes. The resulting censoring-dependent bias has been overlooked because of the lack of clear estimands in pairwise comparisons. In this talk, we introduce explicitly defined estimands for net benefit/win ratio with censored outcomes in terms of the "probabilities that would be observed if we had removed censoring". We show the bias by the series of simulations by varying dependencies in censoring, outcome correlations, and treatment effects for separate outcomes. We propose the modified IPCW estimator that reduces the bias but sacrifices efficiency by excluding uninformative censored pairs.
Keywords
generalized pairwise comparisons
inverse probability of censoring weighting
net benefit
prioritized outcomes
Time-to-event endpoints like progression-free survival and overall survival in oncology randomized trials sometimes show differential censoring patterns between study arms which can be indicative of informative censoring, depending on censoring reasons. Informative censoring can bias treatment effect estimates but few simulation studies characterized the magnitude of its impact, particularly in the context of delayed treatment effects. We use copula methods to model dependent censoring data and assess the impact of informative censoring. To improve the understanding of copula models in this context, we propose a new measure of the strength of informative censoring, the probability of events being informatively censored. To guide the choice of copula, we further propose a visual tool for examining the underlying correlation pattern. We conduct simulation studies to assess the impact of informative censoring on estimation bias for hazard ratios, as well as on empirical power of unweighted, weighted log-rank tests, and the MaxCombo test.
Keywords
Informative Censoring
Copula
Randomized clinical trials
Generalized log-rank tests
Co-Author(s)
Yujie Zhao, Merck & Co., Inc.
Gregory Chen, Merck & Co., Inc.
Margarita DONICA, Merck & Co., Inc.
Larry León, Merck & Co., Inc.
Ludovic Trinquart, Tufts Medical Center
Sabrina Wan, Merck & Co., Inc.
First Author
Jingyi Lin, Merck & Co., Inc
Presenting Author
Jingyi Lin, Merck & Co., Inc
In solid tumor therapy development, with early phase clinical efficacy objective response rate (ORR), deriving predictive models to median progression free survival (mPFS) and median overall survival (mOS) is important as it could optimize late phase trial design. In literature, there are only some ORR/mPFS/mOS association research with limited number of included trials. In this paper, we attempt to predict mPFS and mOS by ORR and optimize late phase trial design of PD1/PDL1 combination therapy. To include adequate eligible clinical trials, we derive comprehensive quantitative clinical trial landscape database by combining information from various sources. A tree-based machine learning regression model is derived to borrow strength and account for ORR/mPFS (ORR/mOS) relationship heterogeneity to ensure the predictive model is robust and has clear structure for interpretation. Over 1000 times cross validation, predictive MSE of proposed model is competitive to random forest, extreme gradient boosting, and superior to common additive and interaction regression models. Example application of proposed model on PD1/PDL1 combination therapy late phase trial POS evaluation is illustrated.
Keywords
PD1 / PDL1 combination therapy development
Predict late phase clinical endpoint by early phase clinical endpoint
Quantitative clinical trial landscape data base
Tree based machine learning regression model
Borrow strength and account for heterogeneity
Clear structure for clinical interpretation and robustness