||1R01CA272806-01A1 Interpret this number
||University Of Tx Md Anderson Can Ctr
||Optimizing Treatment Decision By Accounting for Longitudinal Biomarker Trajectories and Competing Risks of Each Individual
The goal of this proposal is to develop statistical methods for evaluating treatment strategies at different
time points and identifying optimal treatment strategies on the basis of patients' longitudinal biomarker
measurements. It is motivated by our research on identifying the best timing for patients with chronic myeloid
leukemia (CML) to receive a stem cell transplant (SCT). SCT can cure leukemia, but it is associated with life-
threatening risks. For this reason, most patients start with other less-aggressive treatment options that are
much safer but cannot cure the disease. Thus the decision-making about optimal timing of SCT depends on a
patient's disease progression. However, it is infeasible to conduct a randomized controlled trial to weigh the
risks and benefits of SCT at various times. To optimize this decision-making process, sophisticated and
comprehensive statistical models are needed to provide an accurate estimation of the benefits and risks (and
their trade-offs) over time for patients under different SCT timing options. However, these have not yet been
developed, due to the challenges elaborated below.
First, the question of an optimal decision on SCT cannot be answered by a single statistical model, it
requires assembling information from a series of models and analyses. Second, there most likely is not a
uniform solution for this question, as the optimal timing of SCT depends on each individual's disease
progression status. Consequently, physicians must use patients' longitudinal biomarker trajectories to monitor
their health status and make treatment decision in a dynamic fashion. Third, the treatment decision for each
individual must account for their competing risks, including death by treatment-related complications and other
causes (e.g., heart diseases and diabetes). Finally, it is impossible to implement optimal decision-making
without an easy-to-use software. The following specific aims are proposed to solve these problems.
Aim 1: Use functional component principal component analysis (FPCA) techniques to fully capture the
dominant patterns from patients' longitudinal biomarker trajectories, and use them as predictors of
patients’ risk of disease progression.
Aim 2: Estimate dynamic competing risks based on baseline covariates and longitudinal biomarker
trajectories using multi-state models.
Aim 3: Use analytic and microsimulation approaches to estimate and compare the mean survival times
under different SCT timing options.
Aim 4: Conduct validation studies, develop software, and broaden application.
Three CML studies will be used to cross-validate each other regarding the optimal timing of SCT. Software
programs with user-friendly interfaces will be made publicly available. The proposed statistical and software
programs will be adapted and applied to a study of kidney disease to test their broad application.