It’s ME hi, I’m the collider it’s ME

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.

Including the outcome in your imputation model – why isn’t this ‘double dipping’?

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.

Power and sample size calculations for testing the ratio of reproductive values in phylogenetic samples

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.

The Art of the Invite: Crafting Successful Invited Session Proposals

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.

Evaluating the Alignment of a Data Analysis between Analyst and Audience

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.

Why You Must Include the Outcome in Your Imputation Model (and Why It’s Not Double Dipping)

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.

Causal Inference in R

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.

Statistical Rigor in Academic Medicine: Practical Strategies for Improvement

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

A Framework for Developing AI-powered Negotiation Agents to Examine Producer-Consumer Dynamics in Data Science

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

When to Include the Outcome in Your Imputation Model: A Mathematical Demonstration and Practical Advice

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.