David A. Stephens
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View article: Incorporating Partial Adherence for Estimation of Dynamic Treatment Regimes
Incorporating Partial Adherence for Estimation of Dynamic Treatment Regimes Open
Dynamic Treatment Regimes (DTRs) provide a systematic framework for optimizing sequential decision-making in chronic disease management, where therapies must adapt to patients' evolving clinical profiles. Inverse probability weighting (IPW…
View article: Individualized treatment regimens under correlated data with multiple outcomes
Individualized treatment regimens under correlated data with multiple outcomes Open
Precision medicine involves developing individualized treatment regimes (ITRs) which allow for treatment decisions to be tailored to patient characteristics. Naturally, the identification of the optimal regime, that is, the rule which maxi…
View article: Multivariate regression with missing response data for modelling regional DNA methylation QTLs
Multivariate regression with missing response data for modelling regional DNA methylation QTLs Open
Identifying genetic regulators of DNA methylation (mQTLs) with multivariate models enhances statistical power, but is challenged by missing data from bisulfite sequencing. Standard imputation-based methods can introduce bias, limiting reli…
View article: A Bayesian non‐stationary heteroskedastic time series model for multivariate critical care data
A Bayesian non‐stationary heteroskedastic time series model for multivariate critical care data Open
We propose a multivariate GARCH model for non‐stationary health time series by modifying the observation‐level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedast…
View article: The time-dependent Poisson-gamma model in practice: Recruitment forecasting in HIV trials
The time-dependent Poisson-gamma model in practice: Recruitment forecasting in HIV trials Open
Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The tim…
View article: Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model
Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model Open
DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing t…
View article: Computational Considerations for the Linear Model of Coregionalization
Computational Considerations for the Linear Model of Coregionalization Open
In the last two decades, the linear model of coregionalization (LMC) has been widely used to model multivariate spatial processes. However, it can be a challenging task to conduct likelihood-based inference for such models because of the c…
View article: The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020–2023
The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020–2023 Open
Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for …
View article: A time‐dependent Poisson‐Gamma model for recruitment forecasting in multicenter studies
A time‐dependent Poisson‐Gamma model for recruitment forecasting in multicenter studies Open
Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson‐Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson …
View article: Generalized Random Forests using Fixed-Point Trees
Generalized Random Forests using Fixed-Point Trees Open
We propose a computationally efficient alternative to generalized random forests (GRFs) for estimating heterogeneous effects in large dimensions. While GRFs rely on a gradient-based splitting criterion, which in large dimensions is computa…
View article: The impact of directly observed therapy on the efficacy of Tuberculosis treatment: A Bayesian multilevel approach
The impact of directly observed therapy on the efficacy of Tuberculosis treatment: A Bayesian multilevel approach Open
We propose and discuss a Bayesian procedure to estimate the average treatment effect (ATE) for multilevel observations in the presence of confounding. We focus on situations where the confounders may be latent (e.g., spatial latent effects…
View article: Bayesian inference for optimal dynamic treatment regimes in practice
Bayesian inference for optimal dynamic treatment regimes in practice Open
In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby…
View article: A Bayesian Non-Stationary Heteroskedastic Time Series Model for Multivariate Critical Care Data
A Bayesian Non-Stationary Heteroskedastic Time Series Model for Multivariate Critical Care Data Open
We propose a multivariate GARCH model for non-stationary health time series by modifying the variance of the observations of the standard state space model. The proposed model provides an intuitive way of dealing with heteroskedastic data …
View article: The Role of Exchangeability in Causal Inference
The Role of Exchangeability in Causal Inference Open
Though the notion of exchangeability has been discussed in the causal\ninference literature under various guises, it has rarely taken its original\nmeaning as a symmetry property of probability distributions. As this property\nis a standar…
View article: A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies
A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies Open
Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson …
View article: Causal Inference Under Mis-Specification: Adjustment Based on the Propensity Score (with Discussion)
Causal Inference Under Mis-Specification: Adjustment Based on the Propensity Score (with Discussion) Open
We study Bayesian approaches to causal inference via propensity score regression. Much of Bayesian methodology relies on parametric and distributional assumptions, with presumed correct specification, whereas the extant propensity score me…
View article: Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records
Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records Open
Summary Non‐parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a…
View article: Targeting functional parameters with semiparametric Bayesian inference
Targeting functional parameters with semiparametric Bayesian inference Open
Typical Bayesian inference requires parameter identification via likelihood parameterization, which has invited criticism for being less flexible than the Frequentist framework and subject to misspecification. Though misspecification may b…
View article: Causal inference: critical developments, past and future
Causal inference: critical developments, past and future Open
Causality is a subject of philosophical debate and a central scientific issue with a long history. In the statistical domain, the study of cause and effect based on the notion of `fairness' in comparisons dates back several hundred years, …
View article: Bayesian Analysis of Sigmoidal Gaussian Cox Processes via Data Augmentation
Bayesian Analysis of Sigmoidal Gaussian Cox Processes via Data Augmentation Open
Many models for point process data are defined through a thinning procedure where locations of a base process (often Poisson) are either kept (observed) or discarded (thinned). In this paper, we go back to the fundamentals of the distribut…
View article: Semiparametric Bayesian inference for optimal dynamic treatment regimes via dynamic marginal structural models
Semiparametric Bayesian inference for optimal dynamic treatment regimes via dynamic marginal structural models Open
Summary Considerable statistical work done on dynamic treatment regimes (DTRs) is in the frequentist paradigm, but Bayesian methods may have much to offer in this setting as they allow for the appropriate representation and propagation of …
View article: Causal inference under mis-specification: adjustment based on the propensity score
Causal inference under mis-specification: adjustment based on the propensity score Open
We study Bayesian approaches to causal inference via propensity score regression. Much of the Bayesian literature on propensity score methods have relied on approaches that cannot be viewed as fully Bayesian in the context of conventional …
View article: Bayesian clustering for continuous‐time hidden Markov models
Bayesian clustering for continuous‐time hidden Markov models Open
We develop clustering procedures for longitudinal trajectories based on a continuous‐time hidden Markov model (CTHMM) and a generalized linear observation model. Specifically, in this article we carry out finite and infinite mixture model‐…
View article: Table of Contents
Table of Contents Open
View article: The Role of Phylogenetics in Unravelling Patterns of HIV Transmission towards Epidemic Control: The Quebec Experience (2002–2020)
The Role of Phylogenetics in Unravelling Patterns of HIV Transmission towards Epidemic Control: The Quebec Experience (2002–2020) Open
Phylogenetics has been advanced as a structural framework to infer evolving trends in the regional spread of HIV-1 and guide public health interventions. In Quebec, molecular network analyses tracked HIV transmission dynamics from 2002–202…
View article: Bayesian inference for continuous-time hidden Markov models with an unknown number of states
Bayesian inference for continuous-time hidden Markov models with an unknown number of states Open
View article: Bayesian Sample Size Calculations for SMART Studies
Bayesian Sample Size Calculations for SMART Studies Open
In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have been growing in popularity as they offer a more individualized approach, and sequential m…
View article: Estimation of Optimal Dynamic Treatment Regimes using Gaussian Process Emulation
Estimation of Optimal Dynamic Treatment Regimes using Gaussian Process Emulation Open
In precision medicine, identifying optimal sequences of decision rules, termed dynamic treatment regimes (DTRs), is an important undertaking. One approach investigators may take to infer about optimal DTRs is via Bayesian dynamic Marginal …
View article: Estimation of Optimal Dynamic Treatment Regimes via Gaussian Process Emulation: A Technical Report
Estimation of Optimal Dynamic Treatment Regimes via Gaussian Process Emulation: A Technical Report Open
Causal inference of treatment effects is a challenging undertaking in it of itself; inference for sequential treatments leads to even more hurdles. In precision medicine, one additional ambitious goal may be to infer about effects of dynam…
View article: Stochastic Reweighted Gradient Descent
Stochastic Reweighted Gradient Descent Open
Despite the strong theoretical guarantees that variance-reduced finite-sum optimization algorithms enjoy, their applicability remains limited to cases where the memory overhead they introduce (SAG/SAGA), or the periodic full gradient compu…