The 20th KISS Webinar
We are pleased to announce that Dr. Dong-Yun Kim from NIH will give a talk at 3 pm (ET) on June 12. 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.<u1:p></u1:p>
Date/Time: 3pm - 4pm ET (2pm - 3pm CT) on June 12 (Monday)<u1:p></u1:p>
Registration link:
https://uwmadison.zoom.us/meeting/register/tJErfuygpjwqGdDppXafGgQ2KzZNhVcfJ3fD
Registration is required for this meeting. After registering, you will receive a confirmation email containing information about joining the meeting (Of note, this webinar will not be recorded). <u1:p></u1:p>
Speaker: Dr. Dong-Yun Kim (NIH)<u1:p></u1:p>
Title: A fully sequential event rate monitoring method in a clinical trial<u1:p></u1:p>
Abstract: <u1:p></u1:p>
In this talk, we introduce Sequential Event Rate Monitoring (SERM), a new continuous monitoring method for the event rate of time-to-event data in a clinical trial. SERM gives an early warning if the target rate is unlikely to be achieved by the end of study. Since SERM is designed to monitor the overall event rate, blindness of the trial is preserved. If necessary, the method could suggest the number of extra recruitments required for the planned number of primary events. It can also be used to estimate an extension of the follow-up time. We illustrate the methods using data from a well-known Phase III clinical trial.
We are pleased to announce that Dr. Dongjun Chung from Ohio State University will give a talk at 2 pm (ET) on May 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: 2pm - 3pm ET (1pm - 2pm CT) on May 8 (Monday)
Recoded video:
(https://drive.google.com/file/d/1Gsfb7_Si1gvjMzcoNTERVEThgd2T4hdV/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. Dongjun Chung (Ohio State University)
Title: Statistical Problems and Approaches for Spatial Genomic Data Analysis
Abstract:
Spatial gene expression experiment is a newly emerging genomic profiling technology and recently gains significant attention as it revolutionizes biomedical research by profiling high-dimensional gene expression at the close-to-cell level while also retaining spatial localization information on a specific tissue. While this new technology provides unprecedented opportunities for biomedical research, there are also significant and urgent needs for developing new statistical approaches that can effectively analyze this new type of data. In this presentation, I will discuss characteristics of spatial gene expression data, key analytical problems, currently ongoing research, and open questions. In addition, I will discuss multiple statistical modeling projects in my research group to solve these key analytical problems and address open questions. This includes SPRUCE, a Bayesian spatial multivariate finite mixture model for cell spot cluster identification using spatial gene expression data. SPRUCE accurately reflects various key features, including skewness and heavy tails, spatial correlation in gene expression patterns and cluster memberships, among others. I will illustrate them through simulation studies and real data applications.
Best regards,
Yeonhee Park
KISS Program Chair Elect
We are pleased to announce that Dr. Youjin Lee from Brown University will give a talk at 1 pm (ET) on April 6. 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: 1pm-2pm ET (noon - 1pm CT) on April 6 (Thursday)
Recorded video:
https://drive.google.com/file/d/1rX_npObyaQ7jdiRY89JyhzYBAGSElWHV/view?usp=sharing
Speaker: Dr. Youjin Lee (Brown University)
Title: Policy effect evaluation, spillover effects, and causal inference
Abstract: Policy interventions can spill over to units of a population that are not directly exposed to the policy but are geographically close to the units receiving the intervention. In recent work, investigations of spillover effects on neighboring regions have focused on estimating the average treatment effect of a particular policy in an observed setting. Our research question broadens this scope by asking what policy consequences would the treated units have experienced under hypothetical exposure settings. When we only observe treated unit(s) surrounded by controls -- as is common when a policy intervention is implemented in a single city or state -- this effect inquires about the policy effects under a counterfactual neighborhood policy status that we do not, in actuality, observe. In this talk, I will introduce our novel difference-in-differences (DiD) approaches to spillover settings and develop identification conditions required to evaluate policy effects in counterfactual treatment scenarios. These causal quantities are policy-relevant for designing effective policies for populations subject to various neighborhood statuses. We develop doubly robust estimators and use extensive numerical experiments to examine their performance under heterogeneous spillover effects. I will illustrate the application of our proposed method to investigate the effect of the Philadelphia beverage tax on unit sales.
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
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
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.
