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Cancer Working Group

Quantitative Issues in Cancer Research Working Seminar

In Person

Jodeci Wheaden PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Lifestyle factors and impact on colon cancer risk Abstract: In my exploration titled "Lifestyle Factors and Impact on Colon Cancer Risk," I initially set out to understand the relationship between lifestyle choices and colon cancer risk. However, a pivot was necessary … Continue reading "Quantitative Issues in Cancer Research Working Seminar"

Cancer Working Group Seminar 2-14-2024 Flyer

Quantitative Issues in Cancer Research Working Seminar

In Person

Giovanni Parmigiani Professor, Department of Data Science, Dana Farber Cancer Institute and Department of Biostatistics, T.H. Chan School of Public Health Digressions on Simpson’s Paradox

HIV Working Group

In Person

Daniela van Santen Postdoctoral Research Fellow, Harvard T.H. Chan School of Public Health, Department of Biostatistics The effect of direct-acting antivirals on the risk of hepatocellular carcinoma in people with HIV and HCV co-infection using a target trial emulation approach

Quantitative Issues in Cancer Research Working Seminar

In Person

Luke Benz PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Adjusting for Selection Bias Due to Missing Eligibility Criteria in Emulated Target Trials Abstract: Target trial emulation (TTE) is a popular framework for observational studies based on electronic health records (EHR). A key component of this framework is determining the patient … Continue reading "Quantitative Issues in Cancer Research Working Seminar"

Cancer Working Group Seminar Flyer for talk by Raphael Kim

Quantitative Issues in Cancer Research Working Group Seminar

In Person

Raphael Kim, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Title: Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling Abstract: There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

In Person

Phillip Nicol, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Estimation in Poisson log-bilinear models Abstract: The Poisson log-bilinear model, also known as GLM-PCA, is a commonly used approach for dimension reduction in single-cell RNA-seq data. Model parameters are usually estimated via maximum likelihood. However, we show that the MLE can … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

PQG Student and Postdoc Seminar

In Person

Kodi Taraszka Research Fellow in Medicine Dana-Farber Cancer Institute COX proportional hazards Mixed Model (COXMM) accurately estimates the heritability of time-to-event traits With large biobanks connecting electronic health records with genetic sequencing, our understanding of the genetic architecture of time-to-event (TTE) traits such as age-of-onset, treatment response, and disease progression has grown. As a result, … Continue reading "PQG Student and Postdoc Seminar"

Quantitative Issues in Cancer Research Working Group Seminar

In Person

Sajia Darwish, PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health Discussion of “What is the probability of replicating a statistically significant effect?” (Miller 2009) Abstract: If an initial experiment produces a statistically significant effect, what is the probability that this effect will be replicated in a follow-up experiment? argues that this … Continue reading "Quantitative Issues in Cancer Research Working Group Seminar"

HIV Working Group Seminar

Virtual In Person

Tanayott (Tony) Thaweethai Associate Director for Biostatistics Research and Engagement, Massachusetts General Hospital Biostatistics Development of a symptom-based definition of long COVID using negative-unlabeled data Abstract: While most people infected with COVID-19 recover after the acute phase of infection, some people continue to experience persistent symptoms months and even years after infection. These symptoms, also … Continue reading "HIV Working Group Seminar"