Location: UW Biotechnology Center Auditorium
Day and Time: Fridays from noon to 1 pm central time
Zoom options will be available, although in-person attendance is preferred.
Further details should be available about a week before the seminar.
Note: some events may have a different date and time.
September 22, 2023
Speaker: Casey Taylor, Johns Hopkins University
Title: Clinical decision support for unsolicited genomic results.
Poster: Taylor Poster 20230922
Abstract: Given that clinical genomic tests can be initiated outside of the clinical setting (for example, in a research study), from the clinician’s perspective, they can be characterized as “unsolicited,” which brings the challenge of how to determine the value and use of those data in patient care. Dr. Taylor will describe her ongoing research investigating the information and technical requirements for software to enable risk-benefit stratification of clinical decision support (CDS) for unsolicited genomic results (UGR) and the attributes needed for genomic service providers to decide how to prioritize and implement CDS based on UGR. Furthermore, Dr. Taylor is exploring how approaches used for UGR apply for prediction models. The broader impacts of this work are enhancing the productivity and effectiveness of genomic service providers; a greater awareness of how software can be used to support the work of genomic service providers; and an increased ability to implement CDS based upon UGR and prediction models within heterogeneous clinical IT infrastructures.
September 29, 2023
- Speaker: Hongtu Zhu, University of North Carolina at Chapel Hill
- Title: Establishing the Causal Genetic Imaging Clinical Pathway for Brain-Related Disorders
- NOTE – Alternate location: 1345 HSLC
- Zoom: https://uwmadison.zoom.us/j/93430373918
- Abstract: Our causal genetic imaging clinical (CGIC) pathway is motivated by the joint analysis of genetic, imaging, and clinical (GIC) data collected in many large-scale biomedical studies, such as the UK Biobank study and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We propose a statistical framework based on partially functional linear regression models and varying coefficient models to map CGIC pathway for phenotypes associated with brain-related disorders. We develop a joint model selection, estimation, and inference procedure by embedding imaging data in the reproducing kernel Hilbert space and imposing the penalty for the coefficients of scalar variables. We systematically investigate the theoretical properties of scalar and functional efficient estimators, including non-asymptotic error bound, minimax error bound, and asymptotic normality. We apply the proposed method to the ADNI and UKB datasets to identify important features from several millions of genetic polymorphisms and study the effects of a certain set of informative genetic variants and the hippocampus surface on different clinical variables.
October 6, 2023
Speaker: Claudia Solis-Lemus, Department of Plant Pathology, Affiliate Faculty Biostatistics and Medical Informatics
NOTE – Alternate Location: 1345 HSLC
October 13, 2023
Speaker: Solomon Harrar, University of Kentucky
Speaker: Yeonhee Park, Department of Biostatistics and Medical Informatics
October 20 (NOTE TIME CHANGE: 3-5 PM)
Special Event – SMPH Collaborate
More Information and Registration: go.wisc.edu/smphcollaborate
October 26 – DeMets Lecture
(NOTE VENUE and TIME CHANGE: HSLC 1335 from 3:30-4:45 pm, followed by a reception in the HSLC Atrium from 4:45-6:00 pm.)
Speaker: Colin Begg, Memorial Sloan Kettering Cancer Center
Speaker: Alan Moses, University of Toronto
Speaker: Lu Mao, Biostatistics and Medical Informatics
NOTE: Alternate Location: 1345 HSLC
Speaker: Dongjun Chung, Ohio State University
Speaker: Christina Leslie, Memorial Sloan-Kettering Cancer Center
- no seminar (holiday)
- Speaker: Felix Elwert, Department of Sociology, Faculty Affiliate Department of Biostatistics and Medical Informatics
- NOTE: Alternate Location: 1306 HSLC
- Zoom: https://uwmadison.zoom.us/j/91444087740
Speakers: Fall 2023 Student Rotation Presentations
September 8, 2023
Title: Summer 2023 Student Rotation Presentations
Project: Statistical Methods for Analyzing Stepped Wedge Cluster Randomized Trials: A Selective Review
Project: Detecting Intron Retention Events in DEAD-Box Helicase 41 Germline Variants
Project: Using Optimal Transport for Quantile Treatment Effect Estimation
Project: Predicting Ovarian Cancer Using EHR Data
Mentor: Irene Ong
Project: Determining Potential Differences in Secretome Cytokine Expression using Linear Discriminant Analysis”
Project: Evaluating Cell-Cell Interaction (CCI) Scores Using Spatial Transcriptomics Data
Mentor: Huy Dinh
Project: Benchmarking Causal Graph Inference Algorithms on Simulated and Real Expression Datasets
September 15, 2023
Speaker: Joseph Ibrahim, University of North Carolina at Chapel Hill
Title: The Scale Transformed Power Prior for Time-to-Event Data
Poster: Ibrahim_Poster 20230915
Abstract: In clinical trials, data is often available from a previous trial with a different outcome (i.e., binary vs time-to-event). The power prior proposed by Ibrahim and Chen (2000) does not account for different data types in the context discussed here. To accommodate settings in which the historical data and the current data involve different data types, we develop the partial-borrowing scale transformed power prior (straPP) for several commonly used time-to-event models. The partial-borrowing straPP is developed through rescaling the parameter vector from the historical data to align with that of the new data using a transformation based on the Fisher information matrices from the two data models. We also develop the generalized scale transformed power prior (Gen-straPP) to provide added robustness for the case in which the scaled parameters are not equal. Several real data sets from the Eastern Cooperative Oncology Group are used to motivate the use of the proposed methods. We demonstrate the advantages of the partial-borrowing straPP over other common priors via simulation and real data analyses using the proportional hazards model and the mixture cure rate model.
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