CIMPOD 2017

Abstract: This workshop on propensity scores is built around a comparative effectiveness question involving statins. Referring to the research involved in the following reference (Am J Cardiol 2003;92:1447–1451), this workshop will aim to provide attendees with propensity score tools that can be applied to a wide range of causal questions. The research question will be discussed and translated into analytic approaches, and the propensity score tool will be applied to draw an answer to the research question from an observational data source. Both background and dataset with SAS programs will be provided.

Abstract: This workshop will briefly introduce inverse probability weighting (IPW) as a method to appropriately adjust for confounding and selection bias by time-varying covariates affected by prior exposure. The primary focus of the workshop will be on presenting applications of this method in clinical research. Two case studies will be presented: one evaluating the effect of combination antiretroviral therapy on mortality among perinatally HIV-infected youth, and the other evaluating the effect of in-utero exposure to atazanavir on neurodevelopment among perinatally exposed, but uninfected infants. Guided exercises using SAS will be conducted to help participants learn how to construct and apply IPW.

Abstract: We will give two demonstrations of targeted maximum likelihood estimation. The first demonstration involves a cohort study of marginally housed HIV infected adults in San Francisco, where we estimate the causal effect of adherence to antiretroviral therapy. The second involves a randomized trial of a new surgical treatment for stroke, where we adjust for prognostic baseline variables to improve precision and deal with informative censoring of the outcome. We will explain the double robustness property and demonstrate software implementing a targeted maximum likelihood estimator (which is double robust).

Abstract: This workshop will introduce instrumental variable (IV) methods, a set of methods that can potentially estimate causal effects even when confounding is unmeasured. Two case studies will be presented. The first will demonstrate an IV analysis to adjust for non-compliance in a randomized trial; the second will demonstrate an IV analysis to adjust for (measured and unmeasured) confounding in an observational study. In both settings, we will discuss the strengths and limitations of IV approaches, and apply tools for evaluating the validity and robustness of IV effect estimate

Abtract: This workshop will extend the tools learned in the “Constructing Inverse Probability Weights for Static Interventions” workshop to dynamic interventions . Dynamic interventions are treatment strategies that depend on the evolution of one or more time-dependent covariates. They are regularly used in clinical practice, but rarely compared in clinical research. Two case studies comparing dynamic strategies for the care of HIV-infected individuals will be presented. The first focuses on the initiation of antiretroviral therapy, the other on the monitoring of biomarkers. Guided exercises using SAS will give participants hands-on experience building and implementing IPW for dynamic strategies.

Abstract: Upon finding an important socio-economic disparity in breast cancer survival, we may wish to investigate how much of this disparity is via choice of treatment. This is an example of mediation analysis.

Traditionally, such a question was addressed informally and only by fitting a series of simple linear regression models. But thanks to the recent prolific contributions of VanderWeele, Vansteelandt and others, the statistical toolbox for such analyses has been hugely expanded, and the theoretical underpinning formalised.

In these workshops we will introduce the counterfactual-based approach to mediation analysis, focusing on two case studies, in alcohol-related cardiovascular disease and breast cancer.

Abstract: In this workshop, we will learn about the parametric g-formula, an approach to estimating the effects of time-varying treatment effects using observational data with complex time-varying confounding. We will review the motivation behind the approach and the mechanics of the procedure, along with its practical advantages and disadvantages. We will then review how to use the GFORMULA SAS macro to implement this procedure in practice. We will walk through two examples with simulated data motivated by published applications of this method. For the applied part of the workshop, attendees should have working knowledge of SAS and, preferably, the SAS macro language. Attendees can review the documentation for the SAS macro prior to attending the workshop. This may be accessed at https://www.hsph.harvard.edu/causal/software/.

Abstract: Machine learning methods are most commonly used for prediction research questions, but can also be central in causal inference methods. These sessions will focus on understanding ensembled machine learning using the SuperLearner R package. This framework allows investigators to run multiple algorithms (eliminating the need to guess beforehand which single algorithm might perform best in a given data) with the opportunity to outperform any single algorithm by additionally considering all weighted averages of algorithms. The workshop will also describe how to incorporate the super learner within targeted maximum likelihood estimation for causal effects in the tmle R package.

Abstract: This talk reviews treatment-effect estimation with observational data and discusses Stata examples that illustrate syntax and parameter interpretation. After reviewing the potential-outcome framework, the talk discusses estimators for the average treatment effect (ATE) that require exogenous treatment assignment and some estimators that allow for endogenous treatment assignment. The talk also discusses checks for balance, checks for overlap, and some estimators for the ATE from survival-time data. Finally, the talk discusses estimating and interpreting quantile treatment effects.