| dc.description.abstract |
<p>Modern health data science applications leverage abundant molecular and electronic
health data, providing opportunities for machine learning to build statistical models
to support clinical practice. Time-to-event analysis, also called survival analysis,
stands as one of the most representative examples of such statistical models. Models
for predicting the time of a future event are crucial for risk assessment, across
a diverse range of applications, i.e., drug development, risk profiling, and clinical
trials, and such data are also relevant in fields like manufacturing (e.g., for equipment
monitoring). Existing time-to-event (survival) models have focused primarily on preserving
the pairwise ordering of estimated event times (i.e., relative risk). </p><p>In this
dissertation, we propose neural time-to-event models that account for calibration
and uncertainty, while predicting accurate absolute event times. Specifically, we
introduce an adversarial nonparametric model for estimating matched time-to-event
distributions for probabilistically concentrated and accurate predictions. We consider
replacing the discriminator of the adversarial nonparametric model with a survival-function
matching estimator that accounts for model calibration. The proposed estimator can
be used as a means of estimating and comparing conditional survival distributions
while accounting for the predictive uncertainty of probabilistic models.</p><p>Moreover,
we introduce a theoretically grounded unified counterfactual inference framework for
survival analysis, which adjusts for bias from two sources, namely, confounding (from
covariates influencing both the treatment assignment and the outcome) and censoring
(informative or non-informative). To account for censoring biases, a proposed flexible
and nonparametric probabilistic model is leveraged for event times. Then, we formulate
a model-free nonparametric hazard ratio metric for comparing treatment effects or
leveraging prior randomized real-world experiments in longitudinal studies. Further,
the proposed model-free hazard-ratio estimator can be used to identify or stratify
heterogeneous treatment effects. For stratifying risk profiles, we formulate an interpretable
time-to-event driven clustering method for observations (patients) via a Bayesian
nonparametric stick-breaking representation of the Dirichlet Process. </p><p>Finally,
through experiments on real-world datasets, consistent improvements in predictive
performance and interpretability are demonstrated relative to existing state-of-the-art
survival analysis models.</p>
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