225 research outputs found
sj-pdf-1-smm-10.1177_09622802221122423 - Supplemental material for Optimal sampling allocation for outcome-dependent designs in cluster-correlated data settings
No full textSupplemental material, sj-pdf-1-smm-10.1177_09622802221122423 for Optimal sampling allocation for outcome-dependent designs in cluster-correlated data settings by Claudia Rivera-Rodriguez, Sebastien Haneuse and Sara Sauer in Statistical Methods in Medical Research</p
Supplemental material for Time-to-event analysis when the event is defined on a finite time interval
No full textSupplemental Material for Time-to-event analysis when the event is defined on a finite time interval by Catherine Lee, Stephanie J Lee and Sebastien Haneuse in Statistical Methods in Medical Research</p
Supplemental material for Augmented pseudo-likelihood estimation for two-phase studies
No full textSupplemental Material for Augmented pseudo-likelihood estimation for two-phase studies by Claudia Rivera-Rodriguez, Sebastien Haneuse, Molin Wang and Donna Spiegelman in Statistical Methods in Medical Research</p
SAGE_SM – Supplemental material for Quantifying and reducing statistical uncertainty in sample-based health program costing studies in low- and middle-income countries
No full textSupplemental material, SAGE_SM for Quantifying and reducing statistical uncertainty in sample-based health program costing studies in low- and middle-income countries by Claudia L Rivera-Rodriguez, Stephen Resch and Sebastien Haneuse in SAGE Open Medicine</p
Modern epidemiology/ Timothy L. Lash, Tyler J. VanderWeele, Sebastien Haneuse, Kenneth J. Rothman.
No full textKenneth J. Rothman's name appears first in the previous edition.Includes bibliographical references and index.The thoroughly revised and updated Third Edition of Dr. Rothman's acclaimed Modern Epidemiology reflects the conceptual development of this evolving science and the engagement of epidemiologists with an increasing range of current public health concerns.1 online resource
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Semi-Parametric Methods for Missing Data and Causal Inference
No full textIn this dissertation, we propose methodology to account for missing data as well as a strategy to account for outcome heterogeneity.
Missing data occurs frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not at random (MNAR) if, conditional on the observed variables, the missing data mechanism still depends on the unobserved outcome. In such settings, identification is generally not possible without imposing additional assumptions. Identification is sometimes possible, however, if an exogeneous instrumental variable (IV) is observed for all subjects such that it satisfies the exclusion restriction that the IV affects the missingness process without directly influencing the outcome. In chapter 1, we provide necessary and sufficient conditions for nonparametric identification of the full data distribution under MNAR with the aid of an IV. In addition, we give sufficient identification conditions that are more straightforward to verify in practice. For inference, we focus on estimation of a population outcome mean, for which we develop a suite of semiparametric estimators that extend methods previously developed for data missing at random. Specifically, we propose inverse probability weighted estimation, outcome regression based estimation and doubly robust estimation of the mean of an outcome subject to MNAR. For illustration, the methods are used to account for selection bias induced by HIV testing refusal in the evaluation of HIV seroprevalence in Mochudi, Botswana, using interviewer characteristics such as gender, age and years of experience as IVs.
The development of coherent missing data models to account for nonmonotone missing at random (MAR) data by inverse probability weighting (IPW) remains to date largely unresolved. As a consequence, IPW has essentially been restricted for use only in monotone MAR settings. In chapter 2, we propose a class of models for nonmonotone missing data mechanisms that spans the MAR model, while allowing the underlying full data law to remain unrestricted. For parametric specifications within the proposed class, we introduce an unconstrained maximum likelihood estimator for estimating the missing data probabilities which is easily implemented using existing software. To circumvent potential convergence issues with this procedure, we also introduce a constrained Bayesian approach to estimate the missing data process which is guaranteed to yield inferences that respect all model restrictions. The efficiency of standard IPW estimation is improved by incorporating information from incomplete cases through an augmented estimating equation which is optimal within a large class of estimating equations. We investigate the finite-sample properties of the proposed estimators in extensive simulations and illustrate the new methodology in an application evaluating key correlates of preterm delivery for infants born to HIV infected mothers in Botswana, Africa.
When a risk factor affects certain categories of a multinomial outcome but not others, outcome heterogeneity is said to be present. A standard epidemiologic approach for modeling risk factors of a categorical outcome typically entails fitting a polytomous logistic regression via maximum likelihood estimation. In chapter 3, we show that standard polytomous regression is ill-equipped to detect outcome heterogeneity, and will generally understate the degree to which such heterogeneity may be present. Specifically, nonsaturated polytomous regression will often a priori rule out the possibility of outcome heterogeneity from its parameter space. As a remedy, we propose to model each category of the outcome as a separate binary regression. For full efficiency, we propose to estimate the collection of regression parameters jointly by a constrained Bayesian approach which ensures that one remains within the multinomial model. The approach is straightforward to implement in standard software for Bayesian estimation.Biostatisticsmissing data; causal inference; semi-parametric theory; statistics; biostatistic
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Semi-supervised and Representation Learning for Improved Classification and Stratification in EHR Data
No full textThe rapid digitization of healthcare has given rise to vast repositories of electronic health record (EHR) data, offering unprecedented opportunities for data-driven advancements in disease prediction, patient stratification, and clinical decision-making. However, the high dimensionality, sparsity, and heterogeneity of EHR data present unique statistical and computational challenges. Moreover, the scarcity of high-quality labels—due to the cost and complexity of manual annotation—further complicates supervised modeling efforts. This dissertation addresses these challenges through a unified framework of semi-supervised learning and representation learning for improved classification and stratification in EHR data, with applications to phenotyping, disability prediction, and patient subgroup discovery.
The overarching goal of this work is to develop scalable, robust, and interpretable methods that leverage both labeled and unlabeled EHR data, improve generalizability across populations, and uncover clinically meaningful structure in complex disease settings. The dissertation is composed of three interrelated papers, each tackling a key methodological bottleneck in modern EHR-based machine learning: (1) evaluating model performance under distributional shift, (2) learning rich patient representations in the presence of limited labels, and (3) stratifying heterogeneous patient populations using outcome-informed embeddings.
In Chapter 1, we consider the problem of evaluating the performance of binary classifiers when labeled data are unavailable in a target population. This setting is common in clinical phenotyping tasks, where models are trained using limited chart-reviewed labels in one cohort and then applied to other cohorts with potentially different covariate distributions. We propose STEAM Semi-supervised Transfer lEarning of Accuracy Measures), a doubly robust estimation procedure for receiver operating characteristic (ROC) parameters under covariate shift. STEAM combines calibrated density ratio weighting with robust outcome imputation, using both unlabeled source and target data to improve efficiency while protecting against model misspecification. Through theoretical guarantees and empirical results, we demonstrate that STEAM enables accurate performance assessment in unlabeled target populations, with applications to phenotyping models in rheumatoid arthritis on temporally evolving EHR cohort.
Building on the challenge of label scarcity, Chapter 2 shifts focus to semi-supervised representation learning for predictive modeling. We propose SCORE (Semi-supervised Clustering thrOugh REp-
resentation learning), a generative embedding framework that models the joint distribution of high-dimensional EHR features using a multivariate Poisson-LogNormal distribution, with pretrained code embeddings capturing semantic relationships between clinical concepts. SCORE integrates limited labeled data via a hybrid Expectation-Maximization and Gaussian Variational Approximation algorithm, enabling efficient and theoretically sound inference in large-scale, partially labeled cohorts. We show that SCORE produces informative and transferable patient embeddings, improving prediction of disability status in multiple sclerosis (MS) and outperforming conventional supervised and unsupervised methods.
Finally, Chapter 3 addresses the critical task of patient stratification in heterogeneous diseases. We focus on Alzheimer’s disease (AD), where progression and prognosis vary substantially with age. We propose SOLAR (age-Specific Outcome-guided representation Learning for pAtient clusteRing), a novel clustering framework that incorporates time-to-event outcomes and explicitly models age-group structure using a multitask learning paradigm. SOLAR jointly learns low-dimensional patient representations across age groups, encouraging shared structure while allowing age-specific flexibility. By integrating survival information and modeling age-related heterogeneity, SOLAR identifies clinically meaningful AD subtypes with distinct prognostic profiles, improving both interpretability and clinical utility over existing age-unaware or outcome-agnostic methods.
Together, these three works present a cohesive framework for semi-supervised and representation learning in EHR analysis. The methods developed here contribute new strategies for evaluating, predicting, and stratifying patient outcomes in data-scarce, high-dimensional clinical settings. In doing so, they aim to advance the broader goals of personalized medicine and evidence-based healthcare by making machine learning more robust, scalable, and clinically relevant.Biostatistic
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Statistical Methods for Missing Data in Electronic Health Records-based Research
No full textBecause conducting large-scale, long-term randomized studies is prohibitively expensive and time-consuming, researchers have turned to observational studies using electronic health records (EHR) for answers. EHR include rich data on large populations over long periods of time and are available at relatively low cost. However, data are not collected for research purposes, and secondary analyses of EHR are subject to various challenges and biases. Specifically, the potential for selection bias is high when analyses are restricted to patients with complete data. Approaching selection bias as a missing data problem, one could apply standard methods, such as inverse probability weighting (IPW) and multiple imputation (MI), to adjust for selection. However, these methods fail to address the complex nature of EHR data, particularly the interplay of numerous decisions by patients, physicians, and insurers that collectively determine whether complete data is observed.
One recently proposed method for addressing this issue involves breaking down the complex process that governs whether or not a patient has complete data into a series of more manageable sub-mechanisms. This method involves characterizing the data provenance, or the process by which data originates and appears in the EHR. If a clinician is interested in measuring BMI among patients 24 months after undergoing bariatric surgery, it might be the case that for a patient to have complete data in this context, they must: (1) be actively enrolled in their health plan at 24 months after surgery, (2) have a clinical encounter at 24 months, and (3) have their BMI measured at the encounter. Statistical models can then be built for 'selection' (i.e., being in the positive state) at each of the three sub-mechanisms. A framework for estimation and inference within this context has been developed in which IPW is used to adjust for selection at every sub-mechanism. This research proposal expands upon the existing framework by introducing ‘blended analysis’ strategies that give researchers the flexibility to apply MI and IPW simultaneously to control for selection bias. It has been previously demonstrated that there can be gains in efficiency when MI and IPW are used simultaneously. For a given missingness sub-mechanism in the modularized specification of the data provenance, rather than using IPW to adjust for selection of patients with complete data for a specific covariate, a researcher might consider imputing missing values of that covariate instead.
In the first chapter, we introduce a robust variance estimation method when combining IPW with MI, and apply this strategy to an EHR-based study of bariatric surgery, weight loss, and chronic kidney disease. In the second chapter, we introduce the blended analysis framework, establishing estimation procedures under this framework. Throughout, we apply these methods to the DURABLE (DURAtion of Bariatric Long Term Effects) study, a large, ongoing, NIH-funded, multi-center retrospective cohort study investigating the health outcomes of patients who undergo bariatric surgery. While it is widely accepted that Roux-en-Y gastric bypass surgery (RYGB) leads to greater weight loss than vertical sleeve gastrectomy (VSG), there are concerns that the risks of RYGB are greater, especially among patients with chronic kidney disease at baseline. Using EHR, we examine whether the weight loss advantage of RYGB compared to VSG persists among subjects with chronic kidney disease.
In general, IPW and MI-based methods fail to produce consistent estimates when data are MNAR; that is, when the probability that a given covariate is not measured depends on the value of the covariate itself, or on other factors that are only partially observed in EHR. Further, the assumption researchers must make as to whether data is or is not MNAR is statistically untestable. Rigorous sensitivity analyses are therefore needed to measure the extent to which estimators yielded by our methods are impacted by unobserved data. This is the focus of the third chapter
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Generalizability Methods for Estimating Causal Population Effects
No full textStudies are often performed in samples that do not resemble the target populations relevant for policy, treatment, or other decisions. Much of the causal inference literature has focused on addressing internal validity bias; however, both internal and external validity are necessary for unbiased estimates in a target population. The generalizability methods presented in this thesis allow for inference on the population of interest rather than the one in the study.
Chapter 1 presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations. The chapter concludes with practical guidance for researchers and practitioners.
Chapter 2 presents an innovative class of estimators, conditional cross-design synthesis (CCDS), for combining randomized and observational data to eliminate their respective external and internal validity biases. CCDS uses the region of covariate overlap between data types to remove potential unmeasured confounding bias in the observational data in order to extend inference beyond the support of the randomized data to the full target population. We derive outcome regression, propensity weighting, and double robust approaches under the CCDS framework. We illustrate the methods to estimate the causal effect of health insurance plans on cost among New York City Medicaid enrollees.
Chapter 3 introduces novel approaches for generalizing from an evaluation study of a voluntary intervention to estimate population average treatment effects for future treated individuals, which can accommodate nonparametric outcome regression approaches such as Bayesian Additive Regression Trees and Bayesian Causal Forests. The generalizability approach incorporates uncertainty regarding target population treated group membership into the posterior credible intervals to better-reflect the uncertainty of scaling up a voluntary intervention. In a simulation based on real data, we estimate impacts of a national scale-up of a voluntary health policy model and highlight the benefit of using flexible regression approaches for generalizability
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Bayesian Causal Inference With Intermediates
No full textCausal inference from observational data can be complicated for a number of reasons, including complex functional forms for covariates, partially missing or wholly unmeasured confounders, and truncating events which obscure effects on the outcome of interest. In these instances, it can be useful to look at that intermediate variables to disentangle the causal effect of a treatment or exposure on the primary outcome of interest. Moreover, the intermediates can themselves become outcomes of interest as they become targets of public health intervention or metrics for quality of care.
This dissertation explores the use of intermediate variables in two settings. In Chapter 1, we introduce a data-driven sensitivity analysis method. This Bayesian data fusion (BDF) procedure synthesizes information across multiple data sources to correct for confounding by a variable which is unmeasured in the main data set. We demonstrate this method for unmeasured exposure-induced mediator-outcome confounding in the context of Black-White racial disparities in colorectal cancer.
In Chapters 2 and 3, we turn to the problem of understanding hospital readmissions among late-stage pancreatic cancer patients. Readmissions are a common proxy indicator for quality of care, but they can be truncated by death in a problem referred to as semicompeting risks. Chapter 2 lays out a general causal framework for semicompeting risks that is rooted in principal stratification. We motivate two new causal estimands: the time-varying survivor average causal effect (TV-SACE) and the restricted mean survivor average causal effect (RM-SACE). We also introduce a Bayesian estimation procedure which accommodates individual-level latent frailties, and we demonstrate its application in an evaluation of home support among newly diagnosed pancreatic cancer patients.
Chapter 3 proposes a nonparametric estimation procedure for the TV-SACE and RM-SACE based on Bayesian Additive Regression Trees (BART), which allows for treatment effect heterogeneity with embedded interaction terms in the branches of the trees. With this newfound flexibility, we revisit the data analysis of Chapter 2 to understand how the changing composition of latent principal strata drives population-level effects and how heterogeneity informs individualized recommendations.
Chapter 4 concludes with a discussion of unifying themes and future research directions.Biostatistic
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