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Open Access Publications from the University of California

Scholarly Works (9 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2023)

In this work, we delve into the versatility and potency of Targeted Learning (TL) as an indispensable tool for generating actionable and verifiable evidence from real-world data analyses, focusing on modern healthcare applications. We consider the utility of TL in regulatory science, personalized medicine, and federated learning. Our journey begins with an introductory overview of TL, its accompanying roadmap and machine learning component (Chapters 1–2). From there, we embark on an examination in regulatory science, specifically on how TL's reliance on realistic assumptions and dedication to the integrity of statistical inference play a crucial role in the regulatory decision-making process, making statistical analysis planning and implementation more transparent and conducive to rigorous scientific interrogation (Chapter 4). The next focus is on personalized medicine, where TL is paired with online learning to make predictions more effective by tailoring them to the individual's unique circumstances, and TL's contribution here rests on the ability to uncover meaningful relationships from data and thereby reliably guide more personalized patient care (Chapter 5). Finally, we venture into the realm of federated learning across healthcare institutions, where TL's unique integration of causal inference, statistical theory, and machine learning allows these organizations to collaboratively produce statistically sound insights without sharing sensitive data and while respecting their individual autonomy (Chapter 6).

As we explore these distinct yet interconnected applications, a common thread emerges: the power of TL in harnessing complex, real-world data to generate robust evidence. This journey illuminates the remarkable potential of TL as a catalyst for advancing healthcare. It also emphasizes TL's universality and adaptability in diverse environments, all while holding onto its core statistical principles. Our story underscores the profound impact of TL in varied settings and encourages cross-pollination of ideas, strategies, and challenges among them. Ultimately, this work paves the way for a more interconnected and evidence-driven future in healthcare, demonstrating the transformative power of TL in shaping this trajectory.

    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2022)
    Machine 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. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as "AI-QI" units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.
      Cover page: Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcareCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (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. METHODS: We applied the roadmap to a published observational study of the dose-response association between ritodrine hydrochloride and pulmonary edema among women pregnant with twins in Japan. The goal was to identify barriers to causal effect estimation beyond unmeasured confounding reported by the studys authors, and to explore potential options for overcoming the barriers that robustify results. RESULTS: Following the roadmap raised issues that led us to formulate alternative causal questions that produced more reliable, interpretable RWE. The process revealed a lack of information in the available data to identify a causal dose-response curve. However, under explicit assumptions the effect of treatment with any amount of ritodrine versus none, albeit a less ambitious parameter, can be estimated from data. CONCLUSIONS: Before RWE can be used in support of clinical and regulatory decision-making, its quality and reliability must be systematically evaluated. The TL roadmap prescribes how to carry out a thorough, transparent, and realistic assessment of RWE. We recommend this approach be a routine part of any decision-making process.
        Cover page: Evaluating and improving real-world evidence with Targeted Learning.Creative Commons 'BY' version 4.0 license
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2022)
        Sufficient 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. Using HumanMethylation450 BeadChips, we investigated differences in blood cell DNA methylation among 50 benzene-exposed subjects and 48 controls, and among 31 formaldehyde-exposed subjects and 40 controls. We performed CpG-level and regional-level analyses. In the benzene EWAS, we found genome-wide significant alterations, i.e., FWER-controlled P-values <0.05, in the mean and variance of methylation at 22 and 318 CpG sites, respectively, and in mean methylation of a large genomic region. Pathway analysis of genes corresponding to benzene-associated differential methylation sites revealed an impact on the AMPK signalling pathway. In formaldehyde-exposed subjects compared to controls, 9 CpGs in the DUSP22 gene promoter had genome-wide significant decreased methylation variability and a large region of the HOXA5 promoter with 44 CpGs was hypomethylated. Our findings suggest that DNA methylation may contribute to the pathogenesis of diseases related to benzene and formaldehyde exposure. Aberrant expression and methylation of HOXA5 previously has been shown to be clinically significant in myeloid leukaemias. The tumour suppressor gene DUSP22 is a potential biomarker of exposure to formaldehyde, and irregularities have been associated with multiple exposures and diseases.
          Cover page: Epigenome-wide association studies of occupational exposure to benzene and formaldehyde
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2022)
          We 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 R0 for each CSA. Values of R0 ranged from 1.9 to 9.4, with a mean and standard deviation of 4.5±1.8. Fixing the parameters from the exponential fit, we again used NMB-DASA to estimate a set of social distancing behaviour parameters to compute an epidemic flattening index cflatten. Finally, we applied hierarchical clustering methods using this index to divide CSA outbreaks into two clusters: those presenting a social distancing response that was either weaker or stronger. We found cflatten to be more influential in the clustering process than R0. Thus, our results suggest that the behavioural response after a short initial exponential growth phase is likely to be more determinative of the rise of an epidemic than R0 itself.
            Cover page: A comparison of COVID-19 outbreaks across US Combined Statistical Areas using new methods for estimating R<sub>0</sub> and social distancing behaviour.Creative Commons 'BY-NC-ND' version 4.0 license
            • Article
            • Peer Reviewed
            UC Berkeley Previously Published Works (2022)
            Several 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. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross-validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross-validation helps in avoiding unintentional over-fitting of nuisance parameter functionals and leads to more robust inferences.
              • Peer Reviewed
              UC San Francisco Previously Published Works (2023)
                Creative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2019)
                Human 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. Across individual CpG probes, genomic regions, and globally (i.e., the 450K methylome), we investigated differences in mean DNA methylation and differences in variability of DNA methylation between 73 control (< 0.005 ppm TCE), 30 lower exposed (< 10 ppm TCE), and 37 higher exposed ( ≥ 10 ppm TCE) subjects' white blood cells. We found that TCE exposure increased methylation variation globally (Kruskal-Wallis p-value = 3.75e-3) and in 25 CpG sites at a genome-wide significance level (Bonferroni p-value < 0.05). We identified a 609 basepair region in the TRIM68 gene promoter that exhibited hypomethylation with increased exposure to TCE (FWER = 1.20e-2). Also, genes that matched to differentially variable CpGs were enriched in the 'focal adhesion' biological pathway (p-value = 2.80e-2). All in all, human exposure to TCE was associated with epigenetic alterations in genes involved in cell-matrix adhesions and interferon subtype expression, which are important in the development of autoimmune diseases; and in genes related to cancer development. These results suggest that DNA methylation may play a role in the pathogenesis of TCE exposure-related diseases and that TCE exposure may contribute to epigenetic drift.
                  Cover page: Human exposure to trichloroethylene is associated with increased variability of blood DNA methylation that is enriched in genes and pathways related to autoimmune disease and cancer
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2022)
                  Epigenetic 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. Unexposed controls matched by age and sex were selected from nearby factories. We measured leukocyte DNA methylation (DNAm) in peripheral white blood cells using the Infinium HumanMethylation450 BeadChip to calculate five epigenetic aging clocks and DNAmTL, a biomarker associated with leukocyte telomere length and cell replication. We tested associations between exposure intensity and epigenetic age acceleration (EAA), defined as the residuals of regressing the DNAm aging biomarker on chronological age, matching factors and potential confounders. Median differences in EAA between exposure groups were tested using a permutation test with exact p-values. Epigenetic clocks were strongly correlated with age (Spearman r > 0.8) in all three occupational studies. There was a positive exposure-response relationship between benzene and the Skin-Blood Clock EAA biomarker: median EAA was -0.91 years in controls (n = 44), 0.78 years in workers exposed to <10 ppm (n = 41; mean benzene = 1.35 ppm; p = 0.034 vs. controls), and 2.10 years in workers exposed to ≥10 ppm (n = 9; mean benzene = 27.3 ppm; p = 0.019 vs. controls; ptrend = 0.0021). In the TCE study, control workers had a median Skin-Blood Clock EAA of -0.54 years (n = 71) compared to 1.63 years among workers exposed to <10 ppm of TCE (n = 27; mean TCE = 4.22 ppm; p = 0.035). We observed no evidence of EAA associations with formaldehyde exposure (39 controls, 31 exposed). Occupational benzene and TCE exposure were associated with increased epigenetic age acceleration measured by the Skin-Blood Clock. For TCE, there was some evidence of epigenetic age acceleration for lower exposures compared to controls. Our results suggest that some chemical carcinogens may accelerate epigenetic aging.
                    Cover page: Epigenetic aging biomarkers and occupational exposure to benzene, trichloroethylene and formaldehydeCreative Commons 'BY-NC-ND' version 4.0 license
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