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In most clinical oncology trials, time-to-first-event analyses are used for efficacy assessment, which do often not capture the entire disease process. Instead, the focus may be on more complex time-to-event endpoints, as e.g. the course of disease after the first event or endpoints occurring multiply post randomization. We propose "relapse-free and immunosuppression-free survival" (RIFS) as an innovative and clinically relevant outcome measure for assessing treatment success after hematopoietic stem cell transplantation (SCT). To capture the time-dynamic relationship of multiple episodes of immunosuppressive therapy during follow-up, relapse, and non-relapse mortality, a multistate model is developed. The statistical complexity is that the probability of RIFS is non-monotonic over time; thus, standard time-to-first-event methodology is inappropriate for formal treatment comparisons. Instead, a generalization of the Kaplan-Meier method is used for probability estimation, and simulation-based resampling is suggested as a strategy for statistical inference. We reanalyze data from a recently published phase III trial in 201 leukemia patients after SCT. The study aimed at evaluating long-term treatment success of standard GvHD prophylaxis plus a pre-transplant anti-human-T-lymphocyte immunoglobulin compared to standard prophylaxis alone. Results suggest that treatment increases the long-term probability of RIFS by approximately 30% during the entire follow-up period, which complements the original findings. This article highlights the importance of complex endpoints in oncology. They give deeper insight into the treatment and disease process over time. Multistate models combined with resampling are highlighted as a promising tool to evaluate treatment success beyond standard endpoints. Example code is provided in the Supplementary Materials.
PMID: 31927103 [PubMed - as supplied by publisher]