This talk will focus on framing measurement error as a collider from a causal inference perspective. We will begin by demonstrating how to visually display measurement error in directed acyclic graphs (DAGs). We will then show how these graphs can be used to help communicate when corrections for measurement error are needed and how to implement these corrections in order to estimate unbiased effects. Finally, we will demonstrate how sensitivity analyses traditionally used to address omitted variable bias can be used to quantify the potential impact of measurement error.

An often repeated question is whether including the outcome in an imputation model is ‘double dipping’ or ‘peeking’ at the outcome in a way that can negatively impact the Type 1 error in studies. This talk will dive into this myth and help dispel these concerns. We mathematically demonstrate that including the outcome variable in imputation models when using stochastic methods is required to avoid biased results. A discussion of these results along with practical advice will follow.

The quality of the inferences we make from pathogen sequence data is determined by the number and composition of pathogen sequences that make up the sample used to drive that inference. However, there remains limited guidance on how to best structure and power studies when the end goal is phylogenetic inference. One question that we can attempt to answer with molecular data is whether some people are more likely to transmit a pathogen than others. In this talk we will present an estimator to quantify differential transmission, as measured by the ratio of reproductive numbers between people with different characteristics, using transmission pairs linked by molecular data, along with a sample size calculation for this estimator. We will also provide extensions to our method to correct for imperfect identification of transmission linked pairs, overdispersion in the transmission process, and group imbalance. We validate this method via simulation and provide tools to implement it in an R package, phylosamp.

Invited sessions at conferences provide important opportunities for the exchange of ideas. But how do we get invited? And how can we do the inviting? In this panel, we will bring together experienced women in statistics from all career stages to share their tips on organizing invited sessions. Our panelists have planned and participated in numerous successful invited sessions at statistical conferences and have served on program committees to plan and select these sessions on a large scale. This panel is intended to demystify the invited session proposal process and to empower researchers to submit their ideas in the future.

A challenge that all data analysts face is building a data analysis that is useful for a given audience. In this talk, we will begin by proposing a set of principles for describing data analyses. We will then introduce a concept that we call the alignment of a data analysis between the data analyst and audience. We define a successfully aligned data analysis as the matching of principles between the analyst and the audience for whom the analysis is developed. We will propose a statistical model and general framework for evaluating the alignment of a data analysis. This framework can be used as a guide for practicing data scientists and students in data science courses for how to build better data analyses.

Handling missing data is a frequent challenge in analyses of health data, and imputation techniques are often employed to address this issue. This talk focuses on scenarios where a covariate with missing values is to be imputed and examines the prevailing recommendation to include the outcome variable in the imputation model. Specifically, we delve into stochastic imputation methods and their effects on accurately estimating the relationship between the imputed covariate and the outcome. Through mathematical proofs and a series of simulations, we demonstrate that incorporating the outcome variable in imputation models is essential for achieving unbiased results with stochastic imputation. Furthermore, we address the concern that this practice constitutes “double dipping” or data dredging. By providing both theoretical and empirical evidence, we show why including the outcome variable is a legitimate and necessary approach rather than a source of bias.

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.

This talk will focus on best practices for using modern statistics in health sciences.

This talk will discuss a framework for developing AI-powered agents for observing data analytic negotiations between an analyst and client.

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. This talk will investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e., single imputation with a fixed value) and stochastic imputation (i.e., single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Likewise, we mathematically demonstrate that including the outcome variable in imputation models when using deterministic methods is not recommended, and doing so will induce biased results. A discussion of these results along with practical advice will follow.

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. This talk will investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e., single imputation with a fixed value) and stochastic imputation (i.e., single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This talk aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations.

Handling missing data presents a significant challenge in epidemiological data analysis, with imputation frequently employed to handle this issue. It is often advised to use the outcome variable in the imputation model for missing covariates, though the rationale of this advice is not always clear. This presentation will explore both deterministic imputation (i.e., single imputation using fixed values) and stochastic imputation (i.e., single or multiple imputation using random values) approaches and their effects on estimating the association between an imputed covariate and outcome. We will show that the inclusion of the outcome variable in imputation models is not merely a suggestion but a necessity for obtaining unbiased estimates in stochastic imputation approaches. Furthermore, we will clarify misconceptions regarding deterministic imputation models and explain why the outcome variable should be excluded from these models. The goal of this presentation is to connect theory behind imputation and its practical application, offering mathematical proofs to elucidate common statistical guidelines.

In this talk we will discuss four datasets, similar to Anscombe’s quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. Despite the fact that the statistical summaries and visualizations for each dataset are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example datasets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone.

As biostatisticians, we are often tasked with collaborating on a data analysis with many stakeholders. While much has been written about statistical thinking when designing these analyses, a complementary form of thinking that appears in the practice of data analysis is design thinking – the problem-solving process to understand the people for whom a product is being designed. For a given problem, there can be significant or subtle differences in how a biostatistician (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow. These choices can affect the data analysis products themselves and the experience of the consumer of the data analysis. Therefore, the role of a producer can be thought of as designing the data analysis with a set of design principles. This talk will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide empirical evidence of variation of these principles within and between producers of data analyses. We then provide a mathematical framework for alignment between the data analysts and their audience. This will hopefully provide guidance for future work in characterizing the data analytic process.

This talk will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We will then introduce a mathematical framework for describing the alignment in these principles between the analyst and audience.

This talk will focus on best practices for using modern statistics in health sciences.

A data jamboree is a party of different computing tools solving the same data science problems. The NYC Open Data of 311 Service Requests contains all 311 requests of NYC from 2010 to present. This talk with demonstrate how to analyze this data using R.

The data revolution has sparked greater interest in data analysis practices. While much attention has been given to statistical thinking, another type of complementary thinking that appears in data analysis is design thinking – a problem-solving approach focused on understanding the intended users of a product. When facing a problem, differences arise in how data analysts construct data analyses, including choices in methods, tools, and workflows. These choices impact the analysis outputs and user experience. Therefore, a data analyst’s role can be seen as designing the analysis with specific principles. This webinar will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide empirical evidence of variation of these principles within and between data analysts. This will hopefully provide guidance for future work in characterizing the data analytic process.

The data revolution has sparked greater interest in data analysis practices. While much attention has been given to statistical thinking, another type of complementary thinking that appears in data analysis is design thinking – a problem-solving approach focused on understanding the intended users of a product. When facing a problem, differences arise in how data analysts construct data analyses, including choices in methods, tools, and workflows. These choices impact the analysis outputs and user experience. Therefore, a data analyst’s role can be seen as designing the analysis with specific principles. This webinar will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide empirical evidence of variation of these principles within and between data analysts. This will hopefully provide guidance for future work in characterizing the data analytic process.

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. In this talk, we investigate the scenario where covariates used in an analysis have missingness and will be imputed. There are recommendations to include the ultimate outcome in the imputation model for missing covariates, but it’s not necessarily clear when this recommendation holds and why this is true. We examine deterministic imputation (i.e., single imputation where the imputed values are treated as fixed) and stochastic imputation (i.e., single imputation with a random value or multiple imputation) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This talk aims to bridge the gap between imputation in theory and practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model.

Invited sessions at conferences provide important opportunities for the exchange of ideas. But how do we get invited? And how can we do the inviting? In this panel, we bring together experienced women in statistics from all career stages to share their tips on organizing invited sessions. Our panelists have planned and participated in numerous successful invited sessions at statistical conferences and have served on program committees to plan and select these sessions on a large scale. This session, sponsored by the Caucus for Women in Statistics, is intended to demystify the invited session proposal process and to empower researchers to submit their ideas in the future.

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for statisticians to point to when explaining these concepts to collaborators and students. To adjust or not adjust, that is the question; we demonstrate that statistics alone cannot be used to establish which variables to adjust for when estimating causal effects. The second half of the talk will focus on how statistical techniques can be leveraged to address unmeasured confounding. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders. These techniques will be applied using the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.

This talk covers several diagnostic plots that are helpful when attempting to answer a causal question. They can be used to visualize the target population, balance, and treatment effect heterogeneity.

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.

In this session, Dr. Lucy D’Agostino McGowan will discuss some of the major challenges in causal inference, and why statistical tools alone cannot uncover the data-generating mechanism when attempting to answer causal questions. As part of this talk, Lucy will showcase the Causal Quartet, which consists of four datasets that have the same statistical properties, but different true causal effects due to different ways in which the data was generated. These examples illustrate the limitations of relying solely on statistical tools in data analyses and highlight the crucial role of domain-specific knowledge.

This lecture will explore two major challenges in causal inference: (1) how to determine which variables to adjust for and (2) how to assess the impact of unmeasured variables. The first half of the talk will showcase the Causal Quartet, which consists of four datasets that have the same statistical properties, but different true causal effects due to different ways in which the data was generated. Then we will discuss sensitivity analyses for unmeasured confounders, showcasing the tipr R package.

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for biostatisticians to point to when explaining these concepts to collaborators and students. Here, statistics can’t solve your causal inference problem because statistics alone cannot be used to establish which variables to adjust for when estimating causal effects. Statistics can help us explore the impact of unmeasured variables. The second half of the talk will focus on how statistical techniques can be leveraged to address unmeasured confounding. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders. These techniques will be applied using the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.

A panel of faculty experts on machine learning, language learning, neurobiology, and philosophy of mind help us understand how ChatGPT and large language models work.

The transition from graduate school to navigate the unknowns of the job market is challenging for every new statistician. Proper training, efficient networking, and building a professional profile are some of the early initiatives to prepare graduate students for this change. Statisticians are trained in modeling and data analysis; however, the real-world job market requires skills beyond technical knowledge, including communication, presentation, leadership, and collaborative skills, as well as pitching one’s ideas and goals, and being able to advocate for oneself. As a group of emerging statisticians, CENS would like to fill this gap and invite early-career statisticians to discuss the unique challenges that early-career statisticians might face in a new work environment and how to deal with them. Our panel includes both academic and industry statisticians at the MS and PhD level, several of whom graduated in the last five years. Their valuable insights and mentoring guidance will be useful for newly-emerging statisticians to build a path to kickstart their careers achieving their goals.

The data revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the practice of data analysis is design thinking – the problem-solving process to understand the people for whom a product is being designed. For a given problem, there can be significant or subtle differences in how a data analyst (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow. These choices can affect the data analysis products themselves and the experience of the consumer of the data analysis. Therefore, the role of a producer can be thought of as designing the data analysis with a set of design principles. This talk will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide empirical evidence of variation of these principles within and between producers of data analyses. This will hopefully provide guidance for future work in characterizing the data analytic process.

This talk will focus on best practices for using modern statistics in health sciences.

This 6 week series will cover causal inference model building and evaluation techniques. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

This talk will focus on an application, ConTESSA, along with the accompanying R package, tti, designed to help quantify the impact of contact tracing programs. The talk will walk through the technical aspects of the underlying model as well as highlight how R, and in particular shiny, were used to create this product.

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

The Wake Forest Conference on Analytics Impact is focused on the impactful use of analytics to solve problems in business, non-profits, government agencies and society. During the pandemic, government officials and healthcare professionals have more so than ever before, had to communicate to the public using healthcare data. How to communicate these data statistically and visually to influence people’s behavior has proven very challenging. What have we learned about communicating with data during this crisis? What did we get right and what failed? This year’s Conference on Analytics Impact is focused on communicating with health care data and lessons learned from the pandemic.

Without strong communication skills, all the advanced analysis we have performed might be overrun. At this event, our expert panelists will share tips and advice on how to clearly and effectively communicate statistics, particularly in social media, and answer questions from the audience.

This talk will focus on leveraging social media to communicate statistical concepts. From summarizing other’s content to promoting your own work, we will discuss best practices for effective statistical communication that simultaneously is clear, engaging, and understandable while remaining rigorous and mathematically correct. It is increasingly important for people to be able to sift through what is important and what is noise, what is evidence and what is an anecdote. This talk focuses on techniques to strike an appropriate balance, with specifics on how to communicate complex statistical concepts in an engaging manner without sacrificing truth and content.

This talk will focus on leveraging social media to communicate statistical concepts. From summarizing other’s content to promoting your own work, we will discuss best practices for effective statistical communication that simultaneously is clear, engaging, and understandable while remaining rigorous and mathematically correct. It is increasingly important for people to be able to sift through what is important and what is noise, what is evidence and what is an anecdote. This talk focuses on techniques to strike an appropriate balance, with specifics on how to communicate complex statistical concepts in an engaging manner without sacrificing truth and content.

Clear statistical communication is both an educational and public health priority. This talk will focus on best practices for effective statistical communication that simultaneously is clear, engaging, and understandable while remaining rigorous and mathematically correct. It is increasingly important for people to be able to sift through what is important and what is noise, what is evidence and what is an anecdote. This talk focuses on techniques to strike an appropriate balance, with specifics on how to communicate complex statistical concepts in an engaging manner without sacrificing truth and content.

We are thrilled to host a series of experts to discuss their experiences working with different types of COVID-19 data, insights they’ve gleaned, and challenges they’ve encountered with these complex and rapidly evolving data.

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

We are interested in studying best practices for introducing students in statistics or data science to the programming language R. The “tidyverse” is a suite of R packages created to help with common statistics and data science tasks that follow a consistent philosophy. We have created two sets of online learning modules, one that introduces tidyverse concepts first and then dives into idiosyncrasies of R as a programming language, the second that takes a more traditional approach, first introducing R broadly and then following with an introduction to a particular suite of packages, the tidyverse. We have created a randomized study to examine whether the order certain concepts are introduced impacts whether learning objectives are met and/or how engaged students are with the material. This talk will focus on the mechanics of this study: how it was designed, how we enrolled participants, and how we evaluated outcomes.

Clear statistical communication is both an educational and public health priority. This session will focus on best practices for effective statistical communication that simultaneously is clear, engaging, and understandable while remaining rigorous and mathematically correct. The panelists have a range of experience with communicating complex statistical concepts to both technical and lay audiences via multiple communication mechanisms including podcasting, Twitter, engaging with journalists in print, and television correspondence on networks such as CNN and BBC. The session will begin with moderated questions posed by the organizer and then open the discussion to audience members.

This talk will cover two R packages: matahari ( https://github.com/jhudsl/matahari) and tidycode ( https://lucymcgowan.github.io/tidycode/). The matahari package is a simple package for tidy logging of everything you type into the R console. The tidycode package allows users to analyze R expressions in a tidy way (i.e. take the code captured from matahari and put it in a tidy table for downstream analysis with the tidyverse).

This talk will walk through Sir Austin Bradford Hill’s viewpoints for causality, using XKCD comics along the way.

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

This talk will focus on the tipr R package.

This talk will focus on an application, ConTESSA, along with the accompanying R package, tti, designed to help quantify the efficacy of contact tracing programs. The talk will walk through the technical aspects of the underlying model as well as highlight how R, and in particular shiny, were used to create this product.

This talk will focus on bringing pedagogical best practices into the data science classroom. We will begin by focusing on building confident coders, followed by an exploration of developing quantitative intuition, with a particular focus on understanding uncertainty. Finally, we will wrap up with tips for empowering strong data science communicators.

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In the age of “big data” there is an information overload. It is increasingly important for people to be able to sift through what is important and what is noise, what is evidence and what is an anecdote. Accordingly, the effective communication of statistical concepts to diverse audiences is currently an education and public health priority. This talk focuses on techniques to strike an appropriate balance, with specifics on how to communicate complex statistical concepts in an engaging manner without sacrificing truth and content, specifically addressing how to help the general public read past headlines to the actual evidence, or lack there of. We will discuss engaging with the public via organizations such as TED Ed - focusing both best practices and lessons learned.

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

We are interested in studying best practices for introducing students in statistics or data science to the programming language R. The “tidyverse” is a suite of R packages created to help with common statistics and data science tasks that follow a consistent philosophy. We have created two sets of online learning modules, one that introduces tidyverse concepts first and then dives into idiosyncrasies of R as a programming language, the second that takes a more traditional approach, first introducing R broadly and then following with an introduction to a particular suite of packages, the tidyverse. We have created a randomized study to examine whether the order certain concepts are introduced impacts whether learning objectives are met and/or how engaged students are with the material. This talk will focus on the mechanics of this study: how it was designed, how we enrolled participants, and how we evaluated outcomes.

In this session we will discuss three different aspects of engaging students during the COVID-19 health crisis:

How do we engage students with a sensitive topic like COVID-19?
How do we engage students with COVID-19 data?
How do we engage students in a virtual environment?

With the current emphasis on reproducibility and replicability, there is an increasing need to examine how data analyses are conducted. In order to analyze the between researcher variability in data analysis choices as well as the aspects within the data analysis pipeline that contribute to the variability in results, we have created two R packages: matahari and tidycode. These packages build on methods created for natural language processing; rather than allowing for the processing of natural language, we focus on R code as the substrate of interest. The matahari package facilitates the logging of everything that is typed in the R console or in an R script in a tidy data frame. The tidycode package contains tools to allow for analyzing R calls in a tidy manner. We demonstrate the utility of these packages as well as walk through two examples.

This workshop covers set up, implementation, and tips and tricks for integrating RStudio Cloud in your classroom. RStudio Cloud is a great way to incorporate R in the classroom without the hassle of installation and complex set up.

This talk addresses challenges with making health record data and clinical trial data compatible. The data collected in trials is collected regularly and in an organized way, while data from health records is messier and more haphazard. A clinical trial has a clear start and endpoint, while health record data is collected continuously. Additionally, clinical trial participants may be healthier than patients we see in health records. Covariates are defined in advance for a trial, but must be predicted or imputed from the health record. In this talk I will discuss some of the challenges we have encountered in trying to integrate trial data with observational health records to improve power and design new trials.

We are in an exciting new age with access to an overwhelming amount of data and information. This talk will focus on three areas that have become increasingly important as a result. First, we will discuss the importance of reproducibility during this age of information overload. As quantitatively minded people, we are being pushed to innovate and develop best practices for reproducibility. We will talk a bit about tools that make this possible and the next steps in this important area. We will then discuss new opportunities for developing innovative methods, particularly in the observational research space. This portion will include a brief introduction to causal inference for the data scientist. Finally, we will examine the importance of well-developed communication skills for quantitatively savvy people. These aspects will be discussed in the context of my winding path to data science, speckled with some advice and lessons learned.

“If you’re navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it’s a relief. It’s like coming across a clearing in the jungle.” – David McCandless.

The ability to create polished, factual, and easily-understood data visualizations is a crucial skill for the modern statistician. Visualizations aid with all steps of the data analysis pipeline, from exploratory data analysis to effectively communicating results to a broad audience. This tutorial will first cover best practices in data visualization. We will then dive into a hands on experience building intuitive and elegant graphics using R with the ggplot2 package, a system for creating visualizations based on The Grammar of Graphics.

The principle limitation of all observational studies is the potential for unmeasured confounding. Various study designs may perform similarly in controlling for bias due to measured confounders while differing in their sensitivity to unmeasured confounding. Design sensitivity (Rosenbaum, 2004) quantifies the strength of an unmeasured confounder needed to nullify an observed finding. In this presentation, we explore how robust certain study designs are to various unmeasured confounding scenarios. We focus particularly on two exciting new study designs - ATM and ATO weights. We illustrate the performance in a large electronic health records based study and provide recommendations for sensitivity to unmeasured confounding analyses in ATM and ATO weighted studies, focusing primarily on the potential reduction in finite-sample bias.

Making believable causal claims can be difficult, especially with the much repeated adage “correlation is not causation”. This talk will walk through some tools often used to practice safe causation, such as propensity scores and sensitivity analyses. In addition, we will cover principles that suggest causation such as the understanding of counterfactuals, and applying Hill’s criteria in a data science setting. We will walk through specific examples, as well as provide R code for all methods discussed.

Join us for a GitHub journey, guided by Lucy D’Agostino McGowan! We’ll answer questions like:

What is so great about GitHub?
How can I make it work for me and my workflow?
How can I show the world some of the cool things I’m working on?

This will be a hands-on workshop that will give you all the tools to have a delightful time incorporating version control & R (and blogdown ( https://github.com/rstudio/blogdown) if you are so inclined). All levels are welcome!

There is an industry-wide push toward making workflows seamless and reproducible. Incorporating reproducibility into the workflow has many benefits; among them are increased transparency, time savings, and accuracy. We walk through how to seamlessly integrate SAS®, LaTeX, and R into a single reproducible document. We also discuss best practices for general principles such as literate programming and version control.

In studies where randomization is not possible, imbalance in baseline covariates (confounding by indication) is a fundamental concern. Propensity score matching (PSM) is a popular method to minimize this potential bias, matching individuals who received treatment to those who did not, to reduce the imbalance in pre-treatment covariate distributions. PSM methods continue to advance, as computing resources expand. Optimal matching, which selects the set of matches that minimizes the average difference in propensity scores between mates, has been shown to outperform less computationally intensive methods. However, many find the implementation daunting. SAS/IML® software allows the integration of optimal matching routines that execute in R, e.g. the R optmatch package. This presentation walks through performing optimal PSM in SAS® through implementing R functions, assessing whether covariate trimming is necessary prior to PSM. It covers the propensity score analysis in SAS, the matching procedure, and the post-matching assessment of covariate balance using SAS/STAT® 13.2 and SAS/IML procedures.

The Behavioral Risk Factor Surveillance System (BRFSS) collects data on health practices and risk behaviors via telephone survey. This study focuses on the question, On average, how many hours of sleep do you get in a 24-hour period? Recall bias is a potential concern in interviews and questionnaires, such as BRFSS. The 2013 BRFSS data is used to illustrate the proper methods for implementing PROC SURVEYREG and PROC SURVEYLOGISTIC, using the complex weighting scheme that BRFSS provides.

Existing health literacy assessment tools developed for research purposes have constraints that limit their utility for clinical practice. The measurement of health literacy in clinical practice can be impractical due to the time requirements of existing assessment tools. Single Item Literacy Screener (SILS) items, which are self-administered brief screening questions, have been developed to address this constraint. We developed a model to predict limited health literacy that consists of two SILS and demographic information (for example, age, race, and education status) using a sample of patients in a St. Louis emergency department. In this paper, we validate this prediction model in a separate sample of patients visiting a primary care clinic in St. Louis. Using the prediction model developed in the previous study, we use SAS/STAT® software to validate this model based on three goodness of fit criteria: rescaled R-squared, AIC, and BIC. We compare models using two different measures of health literacy, Newest Vital Sign (NVS) and Rapid Assessment of Health Literacy in Medicine Revised (REALM-R). We evaluate the prediction model by examining the concordance, area under the ROC curve, sensitivity, specificity, kappa, and gamma statistics. Preliminary results show 69% concordance when comparing the model results to the REALM-R and 66% concordance when comparing to the NVS. Our conclusion is that validating a prediction model for inadequate health literacy would provide a feasible way to assess health literacy in fast-paced clinical settings. This would allow us to reach patients with limited health literacy with educational interventions and better meet their information needs.