Bridging the Gap: Using Translational Statistics and Novel Endpoints to Accelerate Medical Research

Typical analyses of clinical trials involve intervention comparisons for each efficacy and safety outcome. Outcome-specific effects are estimated and marginal effects are potentially combined in benefit:risk analyses. It is widely believed that such analyses provide comprehensive information regarding the intervention effects on patients. However such approaches do not incorporate associations between outcomes of interest, suffer from competing risk challenges when interpreting outcome-specific results, do not recognize the cumulative nature of multiple outcomes on individual patients, and since efficacy and safety analyses are often conducted using different analysis populations, the population to which such benefit:risk analyses apply, is unclear.

We can address these limitations through patient-centricity by correcting our arithmetic and "using outcomes to analyze patients rather than patients than analyze outcomes". However to obtain the most informative answers for clinical practice, we prioritize: robustness through the avoidance of reliance upon modeling assumptions for validity, objectivity by avoiding subjective beliefs, the theory for error control consistent with the evidentiary standard for confirmatory evidence, clearly defined estimands and populations from which to estimate parameters, best practices for composite endpoints including integrated analyses of components, best practices for benefit:risk / multi-endpoint analyses to aid comprehensive assessment including analyses based on the absolute (vs. relative) risk scale consistent with providing a common scale for interpretation of multiple outcomes simultaneously, recognition of dimensions of treatment contrast including rank-based and grade-based analyses, best practices for ordinal patient-centric outcomes including cumulative analyses, intuitive interpretation, and sound technical fundamentals e.g., appropriate handling of ties for rank-based analyses utilizing pair-wise comparisons.

The Desirability Of Outcome Ranking (DOOR) is a paradigm for the design, analysis, and interpretation of clinical trials and other research studies based on patient-centric benefit-risk evaluation, developed to address these issues and advance clinical trial science. The DOOR methodology allows us to more effectively evaluate and select treatment strategies by providing a more informative way to compare the patient-centric risks and benefits of intervention alternatives. Due to its complexities and increasing application, careful and comprehensive analyses are critical. In this talk, we provide a recommended comprehensive statistical analysis plan for research studies implementing DOOR, and describe and illustrate its elements using examples. We also discuss issues in the design of clinical trials using the DOOR methodology. 

As a new twist on Q-TWiST methodology (Gelber et al, 1989), we proposed treatment-free survival as a novel outcome measure motivated by the development of immunotherapy-based treatments for advanced cancers. Overall survival is the gold-standard endpoint. Immunotherapy has prolonged survival in multiple advanced cancer settings, some in which durable cancer control without continual treatment has been possible; severe and persistent side effects are also possible. We aimed to elucidate how the prolonged overall survival time is spent, on and off treatment, with and without toxicity of the treatment. Treatment-free survival is defined as part of an integrated partitioned overall survival analysis, visualized by the area between Kaplan-Meier curves for two traditional time-to-event endpoints, time to first-line treatment cessation and time to second-line treatment initiation or death; and estimated by the difference in restricted mean survival times of the two endpoints. The integrated analysis introduces further partitioning of mean TFS and times on treatment into times with and without toxicity.