Estimand in Oncology
Chair: Jingjing Schneider (BeiGene)
Vice Chair: Qing Xu (FDA)
Speaker: Songzi Li (BeiGene)
Title: Statistical Considerations for Adjusting Overall Survival in Randomized Trials with Treatment Switching
Abstract: Treatment switching is commonly allowed in randomized clinical trials on novel interventions driven by ethical considerations. When control group patients switch to experimental arm and benefit from the experimental treatment, statistical inference on overall survival based on data according to the arms to which patients were randomized will be biased. The question of clinical interest “what is the overall survival benefit of treatment” cannot be addressed adequately without proper adjustment. In this session, Dr. Songzi Li will provide a theoretical overview of well-known statistical methods rank preserving structure failure time model, inverse probability of censoring weights and two stage. Each adjustment model results in an estimand for a specific question of clinical interest.
Speaker: Jonathan Siegel (Bayer)
Title: Estimands and Censoring in Oncology Time-to-Event Trials – Answering the Right Question
Abstract: This conceptually oriented talk focuses on formulating clinically meaningful and operationally feasible questions that can be addressed in a study context, and discusses the meaning of and alternatives to censoring practices traditional in oncology time-to-event clinical trials. The estimand framework (ICH E9 [R1]) calls for precisely defining the treatment effect of interest to align with the clinical question of interest and requires predefining the handling of intercurrent events that occur after treatment initiation and either preclude the observation of an event of interest or impact the interpretation of the treatment effect. In some clinical contexts, it is not feasible to systematically follow patients to an event of interest. Pervasive and systematic treatment switching can be an example of this type of problem. We discuss what traditional censoring means in such contexts and alternative strategies available to address it. Our paper introduces terminology to distinguish types of censoring and to explain when alternatives to a treatment policy strategy might be appropriate. We provide recommendations for trial design, stressing the need for close alignment of the clinical question of interest, study design, and the practical implications of study execution.
Ref: Siegel JM, Grinsted, L, Liu, F, Weber, HJ, Englert, S, Casey M. Framing time-to-event estimands and censoring mechanisms in oncology in light of the estimands framework. Accepted with revisions, Pharmaceutical Statistics.
Speaker: Qing Xu (FDA)
Title: Causal Inference on Evaluation of Subsequent Therapy in Oncology Studies: Simulations and Clinical Trial Examples
Abstract: Typical oncology practice often includes not only an initial
front-line treatment but also subsequent treatments. For example,
acute lymphoblastic leukemia patients receive hematopoietic stem
cell transplantation as a subsequent therapy or a kidney
transplantation may be given to patients being treated with
dialysis. In clinical trials, subsequent therapy is a nuisance
covariate that may cause complications in the interpretation of the
experimental therapy because it is usually non-random and may
correlates with the patient response to the front-line treatment,
therefore affects the outcome of the primary endpoint. Conventional
analyses using ITT analysis that ignore subsequent treatments may
be misleading, because they are an evaluation of both the
front-line treatment effects and the subsequent treatments. The
international harmonization of technical requirements for
registration of pharmaceutical for human use (ICH) guideline
suggests that it is not advisable to adjust the main analyses for
covariates measured after randomization because they may be
affected by the treatment. This raised a challenge in analyzing the
data that include post-randomization treatment, or subsequent
therapy. We explore the impact of time-varying subsequent therapy
on the statistical power and treatment effects in survival
analysis. Alternative methods other than covariate adjustment for
analyzing the impact of subsequent therapy in time-to-event
endpoints will be discussed. Both simulations and real case studies
will be used to evaluate the pros and cons of different statistical
approaches. The study demonstrated the importance of accounting for
time-varying subsequent therapy for obtaining unbiased
interpretation of data.
