Optimization of individualized dynamic treatment regimes for recurrent diseases.
TL;DR: This method utilizes all the longitudinal data collected during the multi-stage process of disease recurrences and treatments, and identifies the optimal dynamic treatment regime for each individual patient by maximizing his or her expected overall survival.
read more
Abstract: Patients with cancer or other recurrent diseases may undergo a long process of initial treatment, disease recurrences and salvage treatments. It is important to optimize the multi-stage treatment sequence in this process to maximally prolong patients’ survival. Comparing disease-free survival for each treatment stage over-penalizes disease recurrences but under-penalizes treatment-related mortalities. Moreover, treatment regimes used in practice are dynamic, i.e., the choice of next treatment depends on a patient’s responses to previous therapies. In this article, using accelerated failure time models, we develop a method to optimize such dynamic treatment regimes (DTRs). This method utilizes all the longitudinal data collected during the multi-stage process of disease recurrences and treatments, and identifies the optimal DTR for each individual patient by maximizing his/her expected overall survival. The application of this method is illustrated using data from a study of acute myeloid leukemia. The optimal treatment strategies for different patient subgroups are identified.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Doubly Robust Learning for Estimating Individualized Treatment with Censored Data
TL;DR: Nonparametric methods for estimating an optimal individualized treatment rule in the presence of censored data are developed and a doubly robust estimator is proposed which requires correct specification of either the censoring model or survival model but not both.
Differences in predictions of ODE models of tumor growth: a cautionary example
TL;DR: There is a 12- fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.
Interactive Q-learning for Quantiles.
TL;DR: This work derives estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings and illustrates the approach with data from a sequentially randomized trial where the primary outcome is remission of depression symptoms.
Estimating Optimal Dynamic Treatment Regimes With Survival Outcomes
TL;DR: A doubly robust, easy to implement method for estimating optimal DTRs with survival endpoints subject to right-censoring which requires solving a series of weighted generalized estimating equations.
42
Robustifying trial-derived optimal treatment rules for a target population
TL;DR: This work uses data from a single trial study to propose a two-stage procedure to derive a robust and parsimonious rule to maximize the benefit in the target population.
References
•Journal Article
The Dantzig selector: Statistical estimation when P is much larger than n
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: Is it possible to estimate β reliably based on the noisy data y?
2K
Commentary on Andersen and Gill's "Cox's Regression Model for Counting Processes: A Large Sample Study"
Steven G. Self,Ross L. Prentice +1 more
TL;DR: In this article, Andersen and Gill (hereafter AG) present a stimulating development of asymptotic distribution theory for the Cox regression model with time-dependent covariates, which involves such conditions as $\sigma$-algebra right continuity and predictable, locally bounded, covariate processes.
1.3K
Optimal dynamic treatment regimes
TL;DR: In this paper, the authors use experimental or observational data to estimate decision regimes that result in a maximal mean response, and make smooth parametric assumptions only on quantities that are directly relevant to the goal of estimating the optimal rules.
An experimental design for the development of adaptive treatment strategies
TL;DR: This paper advocates the use of sequential multiple assignment randomized trials in the development of adaptive treatment strategies and both a simple ad hoc method for ascertaining sample sizes and simple analysis methods are provided.