JMIR Public Health Surveill
August 2024
Background: Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts.
View Article and Find Full Text PDFBMC Med Res Methodol
August 2023
Background: The Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse sources such as randomized controlled trials that make use of real-world data, observational studies, and other study designs. This paper illustrates a principled approach to assessing the validity and interpretability of RWE.
View Article and Find Full Text PDFPurpose: The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments.
Methods: We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies.
Common tasks encountered in epidemiology, including disease incidence estimation and causal inference, rely on predictive modelling. Constructing a predictive model can be thought of as learning a prediction function (a function that takes as input covariate data and outputs a predicted value). Many strategies for learning prediction functions from data (learners) are available, from parametric regressions to machine learning algorithms.
View Article and Find Full Text PDFIn this work we introduce the personalized online super learner (POSL), an online personalizable ensemble machine learning algorithm for streaming data. POSL optimizes predictions with respect to baseline covariates, so personalization can vary from completely individualized, that is, optimization with respect to subject ID, to many individuals, that is, optimization with respect to common baseline covariates. As an online algorithm, POSL learns in real time.
View Article and Find Full Text PDFWe investigated the initial outbreak rates and subsequent social distancing behaviour over the initial phase of the COVID-19 pandemic across 29 Combined Statistical Areas (CSAs) of the United States. We used the Numerus Model Builder Data and Simulation Analysis (NMB-DASA) web application to fit the exponential phase of a SCLAIV+D (Susceptible, Contact, Latent, Asymptomatic infectious, symptomatic Infectious, Vaccinated, Dead) disease classes model to outbreaks, thereby allowing us to obtain an estimate of the basic reproductive number R for each CSA. Values of R ranged from 1.
View Article and Find Full Text PDFSufficient evidence supports a relationship between certain myeloid neoplasms and exposure to benzene or formaldehyde. DNA methylation could underlie benzene- and formaldehyde-induced health outcomes, but data in exposed human populations are limited. We conducted two cross-sectional epigenome-wide association studies (EWAS), one in workers exposed to benzene and another in workers exposed to formaldehyde.
View Article and Find Full Text PDFMachine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness.
View Article and Find Full Text PDFInverse probability weighting (IPW) and targeted maximum likelihood estimation (TMLE) are methodologies that can adjust for confounding and selection bias and are often used for causal inference. Both estimators rely on the positivity assumption that within strata of confounders there is a positive probability of receiving treatment at all levels under consideration. Practical applications of IPW require finite inverse probability (IP) weights.
View Article and Find Full Text PDFSeveral recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross-validation (cross-validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted.
View Article and Find Full Text PDFEpigenetic aging biomarkers are associated with increased morbidity and mortality. We evaluated if occupational exposure to three established chemical carcinogens is associated with acceleration of epigenetic aging. We studied workers in China occupationally exposed to benzene, trichloroethylene (TCE) or formaldehyde by measuring personal air exposures prior to blood collection.
View Article and Find Full Text PDFAm J Physiol Endocrinol Metab
May 2020
The global prevalence of type 2 diabetes (T2D) has doubled since 1980. Human epidemiological studies support arsenic exposure as a risk factor for T2D, although the precise mechanism is unclear. We hypothesized that chronic arsenic ingestion alters glucose homeostasis by impairing adaptive thermogenesis, i.
View Article and Find Full Text PDFHuman exposure to trichloroethylene (TCE) is linked to kidney cancer, autoimmune diseases, and probably non-Hodgkin lymphoma. Additionally, TCE exposed mice and cell cultures show altered DNA methylation. To evaluate associations between TCE exposure and DNA methylation in humans, we conducted an epigenome-wide association study (EWAS) in TCE exposed workers using the HumanMethylation450 BeadChip.
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