Date/Time: 3pm-4pm ET (2pm - 3pm CT) on March 29 (Wednesday)
Recorded video:
https://drive.google.com/file/d/1X5drIPVy1_LXYw3x75N5tJ2kzp9Zyac9/view?usp=sharing
Registration is required for this meeting. After registering, you will receive a confirmation email containing information about joining the meeting.
Speaker: Dr. Noorie Hyun (Kaiser Permanente Washington Health Research Institute)
Title: An augmented likelihood approach that incorporates error-prone auxiliary data into a survival analysis
Abstract: In this big data era, we can easily observe substantial amounts of clinical data in large observational studies or electronic health records (EHR). Data accuracy can vary according to measurement methods. For example, self-reported medical history can include bias, such as recall bias or response bias. In contrast, gold standard diagnostic tests are less likely to be biased but may not be available on all individuals in a large prospective study due to cost or participant burden. We are motivated to study what benefit we can gain by augmenting analyses of the gold standard disease outcome with error-prone self-reported disease diagnoses in regression for time-to-disease onset. The proposed model addresses left-truncation and interval-censoring in time-to-disease onset outcomes while correcting errors in self-reported disease diagnosis in a joint likelihood for the gold standard and error-prone outcomes. The proposed model is applied to the Hispanic Community Health Study/ Study of Latino data to quantify risk factors associated with diabetes onset.
This is joint work with Pamela A. Shaw.
We are pleased to announce that Dr. Hyunseung Kang from University of Wisconsin-Madison will give a talk at 12:00 pm (ET) on February 8. Please use the link below to register for the KISS webinar. The webinar title and abstract are as follows. We look forward to seeing you all at the KISS webinar.
Date/Time: noon-1pm ET (11am - 12pm CT) on February 8 (Wednesday)
Recorded video:
https://drive.google.com/file/d/1yPpUdikMAWbKoFZB6id5YUPRzsei4wUx/view?usp=sharing
Registration is required for this meeting. After registering, you will receive a confirmation email containing information about joining the meeting.
Speaker: Dr. Hyunseung Kang (University of Wisconsin-Madison)
Title: Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects
Abstract: Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The article presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known compliance groups. For the ITT effects, we study the overall and the heterogeneous ITT effects among the observed covariates where we do not impose parametric models or asymptotic restrictions on cluster size. For the network effects among compliance groups, we propose a new bound-based method that uses pretreatment covariates, classification algorithms, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong.
This is joint work with Chan Park (The Wharton School of Business, University of Pennsylvania)
Link to Paper:
https://www.tandfonline.com/doi/abs/10.1080/01621459.2021.1983437
The 15th KISS Webinar
We are pleased to announce that Dr. Wooseok Ha from UC Berkeley will give a talk at 5 pm (ET) on November 29. Please use the zoom link below to register for the KISS webinar. The webinar title and abstract are as follows. We look forward to seeing you all at the KISS webinar.
Date/Time: 5-6 pm ET (4-5pm CT) on November 29 (Tuesday)
Recorded video:
https://drive.google.com/file/d/1SdlO5SoVeZg07bLZZQXrqcwFrbCMYwS4/view?usp=sharing
After registering, you will receive a confirmation email containing information about joining the meeting.
Speaker: Dr. Wooseok Ha (UC Berkeley)
Title: Fast and flexible estimation of effective migration surfaces.
Abstract: An important feature in spatial population genetic data is often “isolation-by-distance,” where genetic differentiation tends to increase as individuals become more geographically distant. Recently, Petkova et al. (2016) developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. In this talk, I will present a new method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node as in EEMS. When tested with coalescent simulations, FEEMS accurately recovers effective migration surfaces with complex gene-flow histories, including those with anisotropy. Applications of FEEMS to population genetic data from North American gray wolves show it to perform comparably to EEMS, but with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.
