738 research outputs found
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
There is fast-growing literature on estimating heterogeneous treatment
effects via random forests in observational studies. However, there are few
approaches available for right-censored survival data. In clinical trials,
right-censored survival data are frequently encountered. Quantifying the causal
relationship between a treatment and the survival outcome is of great interest.
Random forests provide a robust, nonparametric approach to statistical
estimation. In addition, recent developments allow forest-based methods to
quantify the uncertainty of the estimated heterogeneous treatment effects. We
propose causal survival forests that directly target on estimating the
treatment effect from an observational study. We establish consistency and
asymptotic normality of the proposed estimators and provide an estimator of the
asymptotic variance that enables valid confidence intervals of the estimated
treatment effect. The performance of our approach is demonstrated via extensive
simulations and data from an HIV study
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
A method for estimating the conditional average treatment effect under
condition of censored time-to-event data called BENK (the Beran Estimator with
Neural Kernels) is proposed. The main idea behind the method is to apply the
Beran estimator for estimating the survival functions of controls and
treatments. Instead of typical kernel functions in the Beran estimator, it is
proposed to implement kernels in the form of neural networks of a specific form
called the neural kernels. The conditional average treatment effect is
estimated by using the survival functions as outcomes of the control and
treatment neural networks which consists of a set of neural kernels with shared
parameters. The neural kernels are more flexible and can accurately model a
complex location structure of feature vectors. Various numerical simulation
experiments illustrate BENK and compare it with the well-known T-learner,
S-learner and X-learner for several types of the control and treatment outcome
functions based on the Cox models, the random survival forest and the
Nadaraya-Watson regression with Gaussian kernels. The code of proposed
algorithms implementing BENK is available in https://github.com/Stasychbr/BENK
A nonparametric framework for treatment effect modifier discovery in high dimensions
Heterogeneous treatment effects are driven by treatment effect modifiers,
pre-treatment covariates that modify the effect of a treatment on an outcome.
Current approaches for uncovering these variables are limited to
low-dimensional data, data with weakly correlated covariates, or data generated
according to parametric processes. We resolve these issues by developing a
framework for defining model-agnostic treatment effect modifier variable
importance parameters applicable to high-dimensional data with arbitrary
correlation structure, deriving one-step, estimating equation and targeted
maximum likelihood estimators of these parameters, and establishing these
estimators' asymptotic properties. This framework is showcased by defining
variable importance parameters for data-generating processes with continuous,
binary, and time-to-event outcomes with binary treatments, and deriving
accompanying multiply-robust and asymptotically linear estimators. Simulation
experiments demonstrate that these estimators' asymptotic guarantees are
approximately achieved in realistic sample sizes for observational and
randomized studies alike. This framework is applied to gene expression data
collected for a clinical trial assessing the effect of a monoclonal antibody
therapy on disease-free survival in breast cancer patients. Genes predicted to
have the greatest potential for treatment effect modification have previously
been linked to breast cancer. An open-source R package implementing this
methodology, unihtee, is made available on GitHub at
https://github.com/insightsengineering/unihtee
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring
We propose a reinforcement learning method for estimating an optimal dynamic
treatment regime for survival outcomes with dependent censoring. The estimator
allows the treatment decision times to be dependent on the failure time and
conditionally independent of censoring, supports a flexible number of treatment
arms and treatment stages, and can maximize either the mean survival time or
the survival probability at a certain time point. The estimator is constructed
using generalized random survival forests, and its consistency is shown using
empirical process theory. Simulations and leukemia data analysis results
suggest that the new estimator brings higher expected outcomes than existing
methods in various settings. An R package dtrSurv is available on CRAN
Estimating Trustworthy and Safe Optimal Treatment Regimes
Recent statistical and reinforcement learning methods have significantly
advanced patient care strategies. However, these approaches face substantial
challenges in high-stakes contexts, including missing data, inherent
stochasticity, and the critical requirements for interpretability and patient
safety. Our work operationalizes a safe and interpretable framework to identify
optimal treatment regimes. This approach involves matching patients with
similar medical and pharmacological characteristics, allowing us to construct
an optimal policy via interpolation. We perform a comprehensive simulation
study to demonstrate the framework's ability to identify optimal policies even
in complex settings. Ultimately, we operationalize our approach to study
regimes for treating seizures in critically ill patients. Our findings strongly
support personalized treatment strategies based on a patient's medical history
and pharmacological features. Notably, we identify that reducing medication
doses for patients with mild and brief seizure episodes while adopting
aggressive treatment for patients in intensive care unit experiencing intense
seizures leads to more favorable outcomes
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