Statistics Seminars Series

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Brown Statistical Seminars are hosted collaboratively by the Department of Biostatistics and the Center for Statistical Sciences to provide educational and research opportunities to graduate, undergraduate, and medical students as well as to researchers across the University.  The seminars take place on selected Mondays throughout the academic year and feature leading researchers from the US and internationally.   

To receive our seminar announcements, contact us.

  • Dr. Elizabeth Tipton, Associate Professor of Statistics, Co-Director, Statistics for Evidence-Based Policy & Practice (STEPP) Center,Faculty Fellow, Institute for Policy Research

    Title: When you don’t know the covariance: Combining model-based and robust standard errors

    Abstract:
    When faced with complex data structures, one approach is to estimate standard errors based upon a model, while another approach is to rely on the CLT and use ‘robust’ standard errors. This problem arises frequently in meta-analysis, when there are multiple effect sizes reported in each study, but the nature of the dependence between these effect sizes is completely unknown. Here multiple approaches have been developed, including the use of multivariate models (based on assumed covariance structures), multi-level models, and the use of “robust variance estimation”. In this talk, I provide an overview of a new approach which combines these previous methods. By beginning with a ‘working model’ and then using robust standard errors, I show that the resulting regression estimators are more efficient (than standard robust methods) and the hypothesis tests are more robust (than model-based methods).

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Dr. Jean Feng, Assistant Professor, Department of Epidemiology and Biostatistics, University of C...
    Nov
    1

    Dr. Jean Feng, Assistant Professor, Department of Epidemiology and Biostatistics, University of California, San Francisco (UCSF)

    Safe approval policies for continual learning systems in healthcare
    The number of machine learning (ML)-based medical devices approved by the US Food and Drug Administration (FDA) has been rapidly increasing. The current regulatory policy requires these algorithms to be locked post-approval; subsequent changes must undergo additional scrutiny. Nevertheless, ML algorithms have the potential to improve over time by training over a growing body of data, better reflect real-world settings, and adapt to distributional shifts. To facilitate a move toward continual learning algorithms, the FDA is looking to streamline regulatory policies and design Algorithm Change Protocols (ACPs) that autonomously approve proposed modifications. However, the problem of designing ACPs cannot be taken lightly. We show that policies without error rate guarantees are prone to “bio-creep” and may not protect against distributional shifts. To this end, we investigate the problem of ACP design within the frameworks of online hypothesis testing and online learning and take the first steps towards developing safe ACPs.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Dr. Jonathan Bartlett PhD, Reader in Statistics, Department of Mathematical Sciences, University of Bath, England

    Hypothetical estimands in clinical trials - a unification of causal inference and missing data methods

    In clinical trials events may take place which complicate interpretation of the treatment effect. For example, in diabetes trials, some patients may require rescue medication during follow-up if their diabetes is not well controlled. Interpretation of the intention to treat effect is then complicated if the level of rescue medication is imbalanced between treatment groups. In such cases we may be interested in a so-called hypothetical estimand which targets what effect would have been seen in the absence of rescue medication. In this talk I will discuss estimation of such hypothetical estimands. Currently such estimands are typically estimated using standard missing data techniques after exclusion of any outcomes measured after such events take place. I will define hypothetical estimands using potential outcomes, and exploit standard results for identifiability of causal effects from observational data to describe assumptions sufficient for identification of hypothetical estimands in trials. I will then discuss both ‘causal inference’ and ‘missing data’ methods (such as mixed models) for estimation, and show that in certain situations estimators from these two sets are identical. These links may help those familiar with one set of methods but not the other. They may also identify situations where currently adopted estimation approaches may be relying on unrealistic assumptions, and suggest alternative approaches for estimation.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Dr. Li-Xuan Qin, Associate Member in Biostatistics; PhD in biostatistics; Memorial Sloan Ketterin...
    Dec
    6
    12:00pm - 1:00pm

    Statistics Seminar Series | Dr. Li-Xuan Qin

    121 South Main Street, Room 245

    Dr. Li-Xuan Qin, Associate Member in Biostatistics; PhD in biostatistics; Memorial Sloan Kettering Cancer

    Transcriptomics Data Normalization: Let’s Put It into Context
    This talk will describe an assessment of transcriptomics data normalization (for removing artifacts due to inconsistent experimental handling in data collection) in the context of downstream analysis. With robustly benchmarked data and novel re-sampling-based simulations, I will illustrate several caveats of data normalization for biomarker discovery, sample classification, and survival prediction. I will then discuss the underlying causes for these caveats and provide alternative approaches that are more effective for dealing with the data artifacts.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development