Multi-state modeling and simulation of patient trajectories after allogeneic hematopoietic stem cell transplantation (allo-HSCT) to inform drug development
David James, Jennifer Ng, Jiawei Wei, and Marc Vandemeulebroecke
Novartis
Objectives: To characterize patient trajectories through states of disease after allo-HSCT, quantifying the transition rates into various event states and identifying patient characteristics associated with differential transition rates. This modeling and simulation activity was conducted to investigate drug development scenarios for the prevention of Graft-versus-Host-Disease (GvHD) after allo-HSCT.
Methods: Multi-state models were built on data from the Center for International Blood and Marrow Transplant Research (CIBMTR [4]), a prime data source on stem cell transplantation in the US. Events of interest included acute GvHD (aGvHD), chronic GvHD (cGvHD), relapse of the underlying disease, and death. Six time-continuous, finite-state Markovian models of increasing complexity were built on a sub-set of patients matching the specific target indication. The transition probability matrix was estimated using the Aalen-Johansen estimator [1]. Ten candidate baseline covariates were considered (age, sex, donor type, etc.). Selection of a final model was based on stepwise covariate selection, goodness-of-fit diagnostics, and clinical relevance. In a second step, trial scenarios were simulated based on the final model and assuming various putative drug effects on top of the background transition hazards to quantify 4 composite endpoints of interest. Computations were conducted using the R language [2,3,5-7].
Results: A final 5-state, 10-transition model was selected, and it included 5 baseline covariates affecting 5 transition rates. State probabilities were estimated for target patients, e.g., at 12 months, acute myeloid leukemia recipients of matched related donor allo-HSCT are estimated to have transition probabilities 0.024, 0.039, 0.100 and 0.227, from the initial state to aGvHD, cGvHD, relapse and death, respectively. Simulations from this model allowed us to compare the operating characteristics of a future clinical trial, assuming that the investigational drug reduces selected transition rates to a specified extent, and to compare the trial’s power among 4 composite endpoints with various sample sizes.
Conclusion: Multi-state models provide a rich framework for exploring complex progressive conditions such as the patient journey after allo-HSCT. They can help characterize a background disease pattern, and drug development strategies can then be informed by simulations in which this background pattern is varied.
References:
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[2] Carstensen B, Plummer M, Laara E, Hills M (2016). Epi: A Package for Statistical Analysis in Epidemiology. R package version 2.0. http://CRAN.R-project.org/package=Epi.
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[4] Center for International Blood and Marrow Transplant Research. CIBMTR [Internet] 2016. [accessed 10 February 2017]. URL https://www.cibmtr.org/pages/index.aspx
[5] De Wreede LC, Fiocco M, Putter H (2010). The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Computer methods and programs in biomedicine, 99(3), pp. 261-274.
[6] R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
[7] Therneau TM (2015). Survival: Survival Analysis. http://CRAN.R-project.org/package=survival.
