Biostatistics Seminar: Beyond Global Correlations
Presentation: Beyond Global Correlations
Speaker: Hongyu Zhao, PhD, Ira V. Hiscock Professor of Biostatistics, Yale School of Public Health
Abstract: Correlations are one of the most commonly used statistics that quantify the dependence between two variables. Although global correlations calculated using all the data points collected in a study are informative, some distinct and important local and context-dependent patterns may be masked by global measures. In this presentation, we will discuss the need, benefit, and challenges in inferring local and context-dependent correlations in genetics and genomics studies. We will focus on examples on local genetic correlations to identify genomic regions with shared effects on different complex traits and cell-type-specific correlations to identify/quantify co-regulated genes in disease-relevant cell types.
To join this event via Zoom, please email your request to the seminar coordinator.
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Campus room: Room 135, Hans Rosling Center for Population Health. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Lectures/Seminars.
Thursday, May 2, 2024, 3:30 PM – 5:00 PM.
Biostatistics Seminar: Anatomy of Event Studies: Hypothetical Experiments, Exact Decomposition, and Robust Estimation
Presentation: Anatomy of Event Studies: Hypothetical Experiments, Exact Decomposition, and Robust Estimation
Speaker: José Zubizarreta, PhD, Professor of Health Care Policy, Harvard Medical School; Professor of Biostatistics, Harvard School of Public Health
Abstract: In recent decades, event studies have emerged as a central methodology in health and social research for evaluating the causal effects of staggered interventions. In this paper, we analyze event studies from the perspective of experimental design and provide a novel characterization of the classical dynamic two-way fixed effects (TWFE) regression estimator for event studies. Our decomposition is expressed in closed form and reveals in finite samples the hypothetical experiment that TWFE regression adjustments approximate. This decomposition offers insights into how standard regression estimators borrow information from various units and time points, clarifying and generalizing the notion of forbidden comparison noted in the literature in…
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Campus room: Room 135, Hans Rosling Center for Population Health. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Lectures/Seminars.
Thursday, May 9, 2024, 3:30 PM – 5:00 PM.
SPH Excellence Awards
SPH Excellence Awards
May 14, 2024
4 - 6 pm
Hans Rosling Center for Population Health Lobby
Join us for the annual SPH Excellence Awards! Each year, the School recognizes exemplary staff, faculty and students for their dedication, service, and many contributions to SPH.
Light refreshments and hor d'oeurves will be provided.
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Campus room: Hans Rosling Center, first floor. Accessibility Contact: Meghan Herman megnewt@uw.edu or Sara Bean saracoll@uw.edu. Event Types: Special Events. Target Audience: All students, staff, faculty at SPH.
Tuesday, May 14, 2024, 4:00 PM – 6:00 PM.
Biostatistics Seminar: Statistical Analysis for Correlative Data in CAR-T Cell Immunotherapy Trials: Challenges and Opportunities
Presentation: Statistical Analysis for Correlative Data in CAR-T Cell Immunotherapy Trials: Challenges and Opportunities
Speaker: Qian (Vicky) Wu, PhD, Associate Professor, Clinical Research and Public Health Sciences Divisions, and Member, Translational Data Science Integrated Research Center, Fred Hutch
Abstract: T-cell (CAR-T) therapy shows great efficacy for blood cancer patients, while it’s largely unknown why some patients are not responding, which makes it crucial to identify biomarkers to predict response in CAR-T studies. One of the research bottlenecks is the lack of adequate methods to identify biomarkers from high dimensional data where many biomarkers are highly correlated and the sample size (n) is limited. Motivated by the real data from CAR-T study, we proposed to use a cutting-edge high dimensional inference (HDI) method to identify biomarkers associated with different types of clinical outcomes. HDI can deal with “large p small n” situations, and provide “de-biased” estimates for…
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Campus room: Room 135, Hans Rosling Center for Population Health. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Lectures/Seminars.
Thursday, May 16, 2024, 3:30 PM – 5:00 PM.
Biostatistics Seminar: Simulator: The Swiss Army knife of biomedical data science
Presentation: Simulator: The Swiss Army knife of biomedical data science
Speaker: Jingyi Jessica Li, PhD, Professor of Statistics and Data Science, UCLA; JSB Lab, Abstract: Reference-based simulators, which generate realistic synthetic data as digital twins of reference real data, can help researchers imagine hypothetical experimental results, thus informing study design, method benchmarking, and scientific discovery. In this talk, I will introduce our recent development of simulators for single-cell and spatial multi-omics data, including count data and raw sequencing reads. Our simulators aim to balance two aspects: (1) mimicking real data and (2) allowing user-specified ground truths. Specifically, our latest count simulator scDesign3 uses a unified probabilistic model for single-cell and spatial multi-omics count data. Hence, scDesign3 can infer biologically meaningful parameters; assess the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generate synthetic negative-…
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Campus room: Room 135, Hans Rosling Center for Population Health. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Lectures/Seminars.
Thursday, May 23, 2024, 3:30 PM – 5:00 PM.
SPH Undergraduate Symposium
Join the School of Public Health in celebrating the accomplishments of our undergraduate student body in their experiential learning work! More information on how to RSVP, sign up as a participant, and additional details will be updated soon.
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Campus room: 180. Accessibility Contact: Jillian McBride-Payne - phgh@uw.edu. Event Types: Exhibits.
Thursday, May 23, 2024, 4:00 PM – 6:00 PM.
For more info visit sph.washington.edu. Biostatistics Seminar: Robust Methods for Surrogate Marker Evaluation
Presentation: Robust Methods for Surrogate Marker Evaluation
Speaker: Layla Parast, PhD, Associate Professor of Statistics and Data Sciences, The University of Texas at Austin
Abstract: For many clinical outcomes, randomized clinical trials to evaluate the effectiveness of a treatment or intervention require long-term follow-up of participants. In such settings, there is substantial interest in identifying and using surrogate markers - measurements or outcomes measured at an earlier time or with less cost that are predictive of the primary clinical outcome of interest - to evaluate the treatment effect. Several statistical methods have been proposed to evaluate potential surrogate markers including parametric and nonparametric methods to estimate the proportion of treatment effect explained by the surrogate, methods within a principal stratification framework, and methods for a meta-analytic setting i.e. where information from multiple trials is available. While useful, these methods generally do not…
Event interval: Single day event. Campus location: Hans Rosling Center for Population Health (HRC). Campus room: Room 135, Hans Rosling Center for Population Health. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Lectures/Seminars.
Thursday, May 30, 2024, 3:30 PM – 5:00 PM.
SISCR Module 1: Design and Analysis of Clinical Trials
SISCR Module 1: The design and analysis of a randomized clinical trial involves a series of decisions, including the choice of the primary outcome, sample size, randomization algorithm, interim monitoring plan, and the choice of the primary analysis and estimand of interest. This course will focus on the statistical considerations that inform each of these decisions. Additional topics include addressing multiple comparisons, handling missing data, and whether to consider an adaptive design. We will present a set of simple tools and principles that go a long way towards defining a robust clinical trial design. We will also shed light on some common pitfalls to avoid. Discussions will be driven by examples of trials from a variety of domains including cardiovascular disease, infectious disease (HIV, Ebola, COVID-19), as well as other settings.
We assume enrollees will be familiar with topics taught in introductory statistics (t-tests, regression, confidence intervals, p-values, and a basic understanding of th…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Monday, July 8, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 2: Causal Inference with Observational Data: Common Designs and Statistical Methods
SISCR Module 2: Observational studies are non-interventional empirical investigations of causal effects and are playing an increasingly vital role in healthcare decision making in the era of data science. The study design is particularly important in planning observational studies due to the lack of randomization. Aspects of design include defining the objectives and context under investigation, collecting the right data, and choosing suitable strategies to remove bias from measured and unmeasured confounders. Statistical analysis should also align with the design.
This module covers key concepts and useful methods for designing and analyzing observational studies. The first part of the module will focus on matching and weighting methods for cohort and case-control studies for causal inference. Specific topics include basic tools of matching and weighting, randomization inference, and sensitivity analysis. The second part of the module will focus on methods to address unmeasured confounding via causal exclus…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Monday, July 8, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 1: Design and Analysis of Clinical Trials
SISCR Module 1: The design and analysis of a randomized clinical trial involves a series of decisions, including the choice of the primary outcome, sample size, randomization algorithm, interim monitoring plan, and the choice of the primary analysis and estimand of interest. This course will focus on the statistical considerations that inform each of these decisions. Additional topics include addressing multiple comparisons, handling missing data, and whether to consider an adaptive design. We will present a set of simple tools and principles that go a long way towards defining a robust clinical trial design. We will also shed light on some common pitfalls to avoid. Discussions will be driven by examples of trials from a variety of domains including cardiovascular disease, infectious disease (HIV, Ebola, COVID-19), as well as other settings.
We assume enrollees will be familiar with topics taught in introductory statistics (t-tests, regression, confidence intervals, p-values, and a basic understanding of th…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Tuesday, July 9, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 2: Causal Inference with Observational Data: Common Designs and Statistical Methods
SISCR Module 2: Observational studies are non-interventional empirical investigations of causal effects and are playing an increasingly vital role in healthcare decision making in the era of data science. The study design is particularly important in planning observational studies due to the lack of randomization. Aspects of design include defining the objectives and context under investigation, collecting the right data, and choosing suitable strategies to remove bias from measured and unmeasured confounders. Statistical analysis should also align with the design.
This module covers key concepts and useful methods for designing and analyzing observational studies. The first part of the module will focus on matching and weighting methods for cohort and case-control studies for causal inference. Specific topics include basic tools of matching and weighting, randomization inference, and sensitivity analysis. The second part of the module will focus on methods to address unmeasured confounding via causal exclus…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Tuesday, July 9, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 1: Design and Analysis of Clinical Trials
SISCR Module 1: The design and analysis of a randomized clinical trial involves a series of decisions, including the choice of the primary outcome, sample size, randomization algorithm, interim monitoring plan, and the choice of the primary analysis and estimand of interest. This course will focus on the statistical considerations that inform each of these decisions. Additional topics include addressing multiple comparisons, handling missing data, and whether to consider an adaptive design. We will present a set of simple tools and principles that go a long way towards defining a robust clinical trial design. We will also shed light on some common pitfalls to avoid. Discussions will be driven by examples of trials from a variety of domains including cardiovascular disease, infectious disease (HIV, Ebola, COVID-19), as well as other settings.
We assume enrollees will be familiar with topics taught in introductory statistics (t-tests, regression, confidence intervals, p-values, and a basic understanding of th…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Wednesday, July 10, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 2: Causal Inference with Observational Data: Common Designs and Statistical Methods
SISCR Module 2: Observational studies are non-interventional empirical investigations of causal effects and are playing an increasingly vital role in healthcare decision making in the era of data science. The study design is particularly important in planning observational studies due to the lack of randomization. Aspects of design include defining the objectives and context under investigation, collecting the right data, and choosing suitable strategies to remove bias from measured and unmeasured confounders. Statistical analysis should also align with the design.
This module covers key concepts and useful methods for designing and analyzing observational studies. The first part of the module will focus on matching and weighting methods for cohort and case-control studies for causal inference. Specific topics include basic tools of matching and weighting, randomization inference, and sensitivity analysis. The second part of the module will focus on methods to address unmeasured confounding via causal exclus…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Wednesday, July 10, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 4: Introduction to Survival Analysis
SISCR Module 4: Censored time-to-event data, where not all subjects experience the event of interest, are common in biomedical research. This module introduces some essential statistical tools in the so-called “survival analysis” of censored time-to-event data that are frequently encountered in biomedical research. The module will: Introduce important functions, including the survival function, the hazard function, and the median survival time, in analysis of time-to-event data; , Review life-table analysis, and introduce Kaplan-Meier estimates; , Introduce log-rank tests, and alternative testing procedures that weight group comparisons differently over the follow-up time interval; , Introduce the Cox proportional hazards model for regression analysis of censored time-to-event outcomes; , Cover power and sample size calculation for the design of a clinical study with censored time-to-even outcomes; , Introduce other topics, such as competing risks and biased sampling, arising from observational studies, if…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Thursday, July 11, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 4: Introduction to Survival Analysis
SISCR Module 4: Censored time-to-event data, where not all subjects experience the event of interest, are common in biomedical research. This module introduces some essential statistical tools in the so-called “survival analysis” of censored time-to-event data that are frequently encountered in biomedical research. The module will: Introduce important functions, including the survival function, the hazard function, and the median survival time, in analysis of time-to-event data; , Review life-table analysis, and introduce Kaplan-Meier estimates; , Introduce log-rank tests, and alternative testing procedures that weight group comparisons differently over the follow-up time interval; , Introduce the Cox proportional hazards model for regression analysis of censored time-to-event outcomes; , Cover power and sample size calculation for the design of a clinical study with censored time-to-even outcomes; , Introduce other topics, such as competing risks and biased sampling, arising from observational studies, if…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Friday, July 12, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 5: Improving Precision and Power in Randomized Trials by Leveraging Baseline Variables
SISCR Module 5: In randomized clinical trials with baseline variables that are correlated with the outcome, there is potential to improve precision and reduce the required sample size by appropriately adjusting for these variables in the statistical analysis (called covariate adjustment). The resulting sample size reductions can lead to substantial cost savings, and also can lead to more ethical trials since they avoid exposing more participants than necessary to experimental treatments. Despite regulators such as the U.S. Food and Drug Administration and the European Medicines Agency recommending covariate adjustment, it remains underutilized leading to inefficient trials in many disease areas. This is especially true for trials with binary, ordinal, and time-to-event outcomes, which are quite common. In this module, we explain what covariate adjustment is, how it works, when it may be useful to apply, and how to implement it (in a preplanned way that is robust to model misspecification) for a variety of sce…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Friday, July 12, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 6: Topics in Clinical Trials: Issues in Non-inferiority Trials and Addressing Missing Data
SISCR Module 6: Over the past 7 decades, the randomized clinical trial (RCT) has become the gold standard for evaluation of new drugs, biologics, devices, procedures, and behavioral interventions. In a half-day short course, two critically important topics will be discussed that have broad implications in the design and conduct of clinical trials: the Design of Non-Inferiority Trials and the Prevention of Missing Data.
Design of Non-Inferiority Trials: Suppose a standard therapy has been established to provide a clinically important reduction in risk of irreversible morbidity or mortality. In that setting, the safety and efficacy of an experimental intervention often would be assessed in a clinical trial providing a comparison with that standard therapy rather than with a placebo arm. Such a trial often is designed to assess whether the efficacy of the experimental intervention is not unacceptably worse than that of standard therapy, and is called a non-inferiority trial. Formally, the non-inferiority tria…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Monday, July 15, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 7: Generalized Estimating Equations and Mixed-effects Models for Longitudinal Data Analysis
SISCR Module 7: Longitudinal studies follow individuals over time and repeatedly measure health status, which facilitates prospective ascertainment of exposures and incident outcomes, and identification of changes over time within individuals. Analyses of longitudinal data must account for the correlation that arises from collecting repeated measures on the same individuals over time.
This module will introduce statistical methods for the analysis of longitudinal data, with a focus on marginal (or, population-averaged) models fit via generalized estimating equations and conditional (or, subject-specific) models fit via generalized linear mixed-effects models. Relevant theoretical background will be provided. Illustrative examples and interactive activities (conducted in R) will be used to practice analysis approaches, modeling strategies, and interpretation of results.
This course is targeted toward individuals with little or no prior experience with statistical methods for longitudinal data analysis. Exper…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Monday, July 15, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 7: Generalized Estimating Equations and Mixed-effects Models for Longitudinal Data Analysis
SISCR Module 7: Longitudinal studies follow individuals over time and repeatedly measure health status, which facilitates prospective ascertainment of exposures and incident outcomes, and identification of changes over time within individuals. Analyses of longitudinal data must account for the correlation that arises from collecting repeated measures on the same individuals over time.
This module will introduce statistical methods for the analysis of longitudinal data, with a focus on marginal (or, population-averaged) models fit via generalized estimating equations and conditional (or, subject-specific) models fit via generalized linear mixed-effects models. Relevant theoretical background will be provided. Illustrative examples and interactive activities (conducted in R) will be used to practice analysis approaches, modeling strategies, and interpretation of results.
This course is targeted toward individuals with little or no prior experience with statistical methods for longitudinal data analysis. Exper…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Tuesday, July 16, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 8: Modern Statistical Learning for Observational Data
SISCR Module 8: While clinical trials provide the highest level of evidence to compare clinical treatments or public health interventions, they are often not feasible due to ethical, logistic or economic constraints. Observational studies provide an opportunity to learn about the effect of interventions for which little or no trial data are available. These studies constitute a potentially rich and relatively cheap source of information. However, in such studies, treatment or intervention allocation may be strongly confounded by other important patient characteristics and much care is needed to disentangle observed relationships and infer causal effects.
In this course, we will provide an overview of modern statistical techniques for analyzing observational data. We will focus primarily on recent advances in the field of targeted learning, which facilitates the use of state-of-the-art machine learning tools to flexibly adjust for confounding while yielding valid statistical inference. In contrast, conventiona…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Tuesday, July 16, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 3: Design, Conduct, and Analysis of Randomized Clinical Trials with Time-to-Event Primary Endpoints
SISCR Module 3:
Schedule Monday, July 8 – Pre-recorded module content available for registrants (approximately 14 hours of content)
, Live sessions: Discussion of course content and case studies: Tuesday, July 16, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , Wednesday, July 17, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , Friday, July 19, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , , Description: There will be three 90-minute live sessions to discuss module material and case studies on July 16, 17, and 19.
The analysis of data measuring time to event is often complicated by incomplete observations: Some subjects have not yet had an event at the time of data analysis. A wide variety of statistical methods have been developed for this setting of “right censored data” including parametric and semiparametric regression models, as well as a broad array of nonparametric methods. The common problems that arise in the clinical trial settings interact with the statistical behavior of the analysis methods in such a way as to…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Tuesday, July 16, 2024, 10:00 AM – 11:30 AM.
For more info visit si.biostat.washington.edu. SISCR Module 7: Generalized Estimating Equations and Mixed-effects Models for Longitudinal Data Analysis
SISCR Module 7: Longitudinal studies follow individuals over time and repeatedly measure health status, which facilitates prospective ascertainment of exposures and incident outcomes, and identification of changes over time within individuals. Analyses of longitudinal data must account for the correlation that arises from collecting repeated measures on the same individuals over time.
This module will introduce statistical methods for the analysis of longitudinal data, with a focus on marginal (or, population-averaged) models fit via generalized estimating equations and conditional (or, subject-specific) models fit via generalized linear mixed-effects models. Relevant theoretical background will be provided. Illustrative examples and interactive activities (conducted in R) will be used to practice analysis approaches, modeling strategies, and interpretation of results.
This course is targeted toward individuals with little or no prior experience with statistical methods for longitudinal data analysis. Exper…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Wednesday, July 17, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 8: Modern Statistical Learning for Observational Data
SISCR Module 8: While clinical trials provide the highest level of evidence to compare clinical treatments or public health interventions, they are often not feasible due to ethical, logistic or economic constraints. Observational studies provide an opportunity to learn about the effect of interventions for which little or no trial data are available. These studies constitute a potentially rich and relatively cheap source of information. However, in such studies, treatment or intervention allocation may be strongly confounded by other important patient characteristics and much care is needed to disentangle observed relationships and infer causal effects.
In this course, we will provide an overview of modern statistical techniques for analyzing observational data. We will focus primarily on recent advances in the field of targeted learning, which facilitates the use of state-of-the-art machine learning tools to flexibly adjust for confounding while yielding valid statistical inference. In contrast, conventiona…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Wednesday, July 17, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 3: Design, Conduct, and Analysis of Randomized Clinical Trials with Time-to-Event Primary Endpoints
SISCR Module 3:
Schedule Monday, July 8 – Pre-recorded module content available for registrants (approximately 14 hours of content)
, Live sessions: Discussion of course content and case studies: Tuesday, July 16, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , Wednesday, July 17, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , Friday, July 19, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , , Description: There will be three 90-minute live sessions to discuss module material and case studies on July 16, 17, and 19.
The analysis of data measuring time to event is often complicated by incomplete observations: Some subjects have not yet had an event at the time of data analysis. A wide variety of statistical methods have been developed for this setting of “right censored data” including parametric and semiparametric regression models, as well as a broad array of nonparametric methods. The common problems that arise in the clinical trial settings interact with the statistical behavior of the analysis methods in such a way as to…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Wednesday, July 17, 2024, 10:00 AM – 11:30 AM.
For more info visit si.biostat.washington.edu. SISCR Module 8: Modern Statistical Learning for Observational Data
SISCR Module 8: While clinical trials provide the highest level of evidence to compare clinical treatments or public health interventions, they are often not feasible due to ethical, logistic or economic constraints. Observational studies provide an opportunity to learn about the effect of interventions for which little or no trial data are available. These studies constitute a potentially rich and relatively cheap source of information. However, in such studies, treatment or intervention allocation may be strongly confounded by other important patient characteristics and much care is needed to disentangle observed relationships and infer causal effects.
In this course, we will provide an overview of modern statistical techniques for analyzing observational data. We will focus primarily on recent advances in the field of targeted learning, which facilitates the use of state-of-the-art machine learning tools to flexibly adjust for confounding while yielding valid statistical inference. In contrast, conventiona…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Thursday, July 18, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 9: Introduction to Missing Data Methods
SISCR Module 9: Although missing data are pervasive in studies across disciplines, the impact of missing data on estimation and inference and the strengths and weaknesses of modern approaches to handling missing data are not widely understood.
This module will review common missing data mechanisms, then introduce a variety of methods for estimation and inference in the presence of missing data, including conventional methods, the EM algorithm, multiple imputation, and semi-parametric methods. Approaches to sensitivity analyses will also be discussed. All methods will be illustrated in R using data from observational studies.
This course is targeted towards individuals with little or no prior experience with modern missing data methods. Experience using regression methods to analyze data (e.g. linear regression, logistic regression) is important background for this module.
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Thursday, July 18, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 8: Modern Statistical Learning for Observational Data
SISCR Module 8: While clinical trials provide the highest level of evidence to compare clinical treatments or public health interventions, they are often not feasible due to ethical, logistic or economic constraints. Observational studies provide an opportunity to learn about the effect of interventions for which little or no trial data are available. These studies constitute a potentially rich and relatively cheap source of information. However, in such studies, treatment or intervention allocation may be strongly confounded by other important patient characteristics and much care is needed to disentangle observed relationships and infer causal effects.
In this course, we will provide an overview of modern statistical techniques for analyzing observational data. We will focus primarily on recent advances in the field of targeted learning, which facilitates the use of state-of-the-art machine learning tools to flexibly adjust for confounding while yielding valid statistical inference. In contrast, conventiona…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Friday, July 19, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 9: Introduction to Missing Data Methods
SISCR Module 9: Although missing data are pervasive in studies across disciplines, the impact of missing data on estimation and inference and the strengths and weaknesses of modern approaches to handling missing data are not widely understood.
This module will review common missing data mechanisms, then introduce a variety of methods for estimation and inference in the presence of missing data, including conventional methods, the EM algorithm, multiple imputation, and semi-parametric methods. Approaches to sensitivity analyses will also be discussed. All methods will be illustrated in R using data from observational studies.
This course is targeted towards individuals with little or no prior experience with modern missing data methods. Experience using regression methods to analyze data (e.g. linear regression, logistic regression) is important background for this module.
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Friday, July 19, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 3: Design, Conduct, and Analysis of Randomized Clinical Trials with Time-to-Event Primary Endpoints
SISCR Module 3:
Schedule Monday, July 8 – Pre-recorded module content available for registrants (approximately 14 hours of content)
, Live sessions: Discussion of course content and case studies: Tuesday, July 16, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , Wednesday, July 17, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , Friday, July 19, 10-11:30 a.m. PDT (1-2:30 p.m. EDT) , , Description: There will be three 90-minute live sessions to discuss module material and case studies on July 16, 17, and 19.
The analysis of data measuring time to event is often complicated by incomplete observations: Some subjects have not yet had an event at the time of data analysis. A wide variety of statistical methods have been developed for this setting of “right censored data” including parametric and semiparametric regression models, as well as a broad array of nonparametric methods. The common problems that arise in the clinical trial settings interact with the statistical behavior of the analysis methods in such a way as to…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Friday, July 19, 2024, 10:00 AM – 11:30 AM.
For more info visit si.biostat.washington.edu. SISCR Module 10: Evaluation of Biomarkers and Risk Models
SISCR Module 10: This module covers methodology for evaluating biomarkers and risk prediction models, covering principles, concepts, metrics, and graphical tools.
We will discuss motivations for risk prediction in clinical medicine and public health, and clarify the concept of “personal” risk. Metrics and graphical tools will include ROC curves and AUC; calibration plots for risk prediction models; and net benefit and decision curves. The module will also discuss methods for comparing risk prediction models and, in particular, assessing the incremental value of a new biomarker when there are already established predictors. We will consider evaluating the utility of a single or composite biomarker for prognostic enrichment of a clinical trial.
There will be an opportunity for hands-on practice in R using relevant packages such as rms, rmda, and BioPET. However, the software component of the module is small and knowledge of R is not required for this module.
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Monday, July 22, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 11: Analyzing Data from Complex Surveys
SISCR Module 11: Health scientists increasingly use data from unequal-probability survey designs, either public-use data from national surveys such as NHANES and NLSY, or their own surveys, or from subsamples of existing cohorts or databases. Correct analysis of survey data requires appropriate software and an understanding of basic survey concepts, but is otherwise just like any data analysis. In this module we will cover the concepts of weights, clusters, and strata, and how to use the R survey package to conduct analyses.
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Monday, July 22, 2024, 1:00 PM – 4:30 PM.
For more info visit si.biostat.washington.edu. SISCR Module 11: Analyzing Data from Complex Surveys
SISCR Module 11: Health scientists increasingly use data from unequal-probability survey designs, either public-use data from national surveys such as NHANES and NLSY, or their own surveys, or from subsamples of existing cohorts or databases. Correct analysis of survey data requires appropriate software and an understanding of basic survey concepts, but is otherwise just like any data analysis. In this module we will cover the concepts of weights, clusters, and strata, and how to use the R survey package to conduct analyses.
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Tuesday, July 23, 2024, 1:00 PM – 4:30 PM.
For more info visit si.biostat.washington.edu. SISCR Module 12: Absolute Risk: Methods and Applications in Clinical Care and Public Health
SISCR Module 12: This course is an introduction to absolute risk, the probability of developing a specific outcome, over a specified time interval, in the presence of competing causes of mortality. This course will define absolute risk and discusses methodological issues relevant to the development and evaluation of absolute risk models. We will present the cause-specific and cumulative incidence approaches to incorporating covariates, and discuss various study designs and data for model building, including cohort, nested case-control, and case-control data combined with registry data. We will show how to evaluate the performance of risk prediction models and discuss the use of absolute risk in individual counseling for prevention strategies, including interventions that can have adverse effects. We will address the impact of different distributions of model predictors and differences in verifying the disease status or outcome on measures of calibration, accuracy and discrimination of a model. We will pres…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Wednesday, July 24, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu. SISCR Module 12: Absolute Risk: Methods and Applications in Clinical Care and Public Health
SISCR Module 12: This course is an introduction to absolute risk, the probability of developing a specific outcome, over a specified time interval, in the presence of competing causes of mortality. This course will define absolute risk and discusses methodological issues relevant to the development and evaluation of absolute risk models. We will present the cause-specific and cumulative incidence approaches to incorporating covariates, and discuss various study designs and data for model building, including cohort, nested case-control, and case-control data combined with registry data. We will show how to evaluate the performance of risk prediction models and discuss the use of absolute risk in individual counseling for prevention strategies, including interventions that can have adverse effects. We will address the impact of different distributions of model predictors and differences in verifying the disease status or outcome on measures of calibration, accuracy and discrimination of a model. We will pres…
Event interval: Single day event. Accessibility Contact: Deb Nelson, nelsod6@uw.edu, 206-685-9323. Event Types: Workshops.
Thursday, July 25, 2024, 8:30 AM – 12:00 PM.
For more info visit si.biostat.washington.edu.