Browsing by Author "Huang, Erich"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    A longitudinal study of convergence between Black and White COVID-19 mortality: A county fixed effects approach.
    (Lancet regional health. Americas, 2021-09) Lawton, Ralph; Zheng, Kevin; Zheng, Daniel; Huang, Erich

    Background

    Non-Hispanic Black populations have suffered much greater per capita COVID-19 mortality than White populations. Previous work has shown that rates of Black and White mortality have converged over time. Understanding of COVID-19 disparities over time is complicated by geographic changes in prevalence, and some prior research has claimed that regional shifts in COVID-19 prevalence may explain the convergence.

    Methods

    Using county-level COVID-19 mortality data stratified by race, we investigate the trajectory of Black and White per capita mortality from June 2020-January 2021. We use a county fixed-effects model to estimate changes within counties, then extend our models to leverage county-level variation in prevalence to study the effects of prevalence versus time trajectories in mortality disparities.

    Findings

    Over this period, cumulative mortality rose by 61% and 90% for Black and White populations respectively, decreasing the mortality ratio by 0.4 (25.8%). These trends persisted when a county-level fixed-effects model was applied. Results revealed that county-level changes in prevalence nearly fully explain changes in mortality disparities over time.

    Interpretation

    Results suggest mechanisms underpinning convergence in Black/White mortality are not driven by fixed county-level characteristics or changes in the regional dispersion of COVID-19, but instead by changes within counties. Further, declines in the Black/White mortality ratio over time appear primarily linked to county-level changes in COVID-19 prevalence rather than other county-level factors that may vary with time. Research into COVID-19 disparities should focus on mechanisms that operate within-counties and are consistent with a prevalence-disparity relationship.

    Funding

    This work was supported by the National Center for Advancing Translational Sciences [E.H.: UL1TR002553].
  • Thumbnail Image
    ItemOpen Access
    Correction to: The role of machine learning in clinical research: transforming the future of evidence generation.
    (Trials, 2021-09) Weissler, E Hope; Naumann, Tristan; Andersson, Tomas; Ranganath, Rajesh; Elemento, Olivier; Luo, Yuan; Freitag, Daniel F; Benoit, James; Hughes, Michael C; Khan, Faisal; Slater, Paul; Shameer, Khader; Roe, Matthew; Hutchison, Emmette; Kollins, Scott H; Broedl, Uli; Meng, Zhaoling; Wong, Jennifer L; Curtis, Lesley; Huang, Erich; Ghassemi, Marzyeh
    Following the publication of the original article [1], we were notified that current affiliations 17, 18 and 19 were erroneously added to the first author rather than the senior author (Marzyeh Ghassemi). The original article has now been corrected.
  • Thumbnail Image
    ItemOpen Access
    The role of machine learning in clinical research: transforming the future of evidence generation.
    (Trials, 2021-08) Weissler, E Hope; Naumann, Tristan; Andersson, Tomas; Ranganath, Rajesh; Elemento, Olivier; Luo, Yuan; Freitag, Daniel F; Benoit, James; Hughes, Michael C; Khan, Faisal; Slater, Paul; Shameer, Khader; Roe, Matthew; Hutchison, Emmette; Kollins, Scott H; Broedl, Uli; Meng, Zhaoling; Wong, Jennifer L; Curtis, Lesley; Huang, Erich; Ghassemi, Marzyeh

    Background

    Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum.

    Results

    Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas.

    Conclusions

    ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.