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Stata Computer Technology Workshops (CTWs) at JSM 2019 Annual Meeting

Title: Causal inference and treatment effects using Stata
Presenter: Charles Lindsey, Senior Statistician and Software Developer, StataCorp
Where: JSM 2019 Annual Meeting
Date: Wednesday, 31 July 2019
Time: 8:00 AM to 9:45 AM
Abstract: Researchers are often challenged with making causal inferences based on observational data instead of experimental data. In this workshop, I provide an overview of causal inference methods and demonstrate how to implement these methods in Stata. Causal inferences are often framed in terms of treatment effects, which are measurements of the difference in an outcome between a treatment and the control. I will discuss techniques for estimating treatment effects such as regression adjustment, inverse probability weighting, and propensity-score matching. I will also introduce methods for estimating treatment effects when observational data complications such as sample selection (data missing not at random) and unobserved confounding are present. In addition, I will show how to estimate the effect of changing levels of a continuous predictor under these complications. I will provide examples demonstrating how to perform causal inference and treatment-effect estimation within Stata. No prior knowledge of Stata is required, but basic familiarity with regression modeling will prove useful.
Fee: Added fee during registration

Title: Bayesian multilevel modeling using Stata
Presenter: Yulia Marchenko, Executive Director of Statistics, StataCorp
Where: JSM 2019 Annual Meeting
Date: Wednesday, 31 July 2019
Time: 10:00 AM to 11:45 AM
Abstract: This workshop will cover Bayesian multilevel modeling and how to fit such models using Stata. In multilevel or hierarchical data, which include longitudinal and repeated-measures data, observations belong to different groups. Groups may represent different levels of hierarchy, such as hospitals, doctors nested within hospitals, and patients nested within doctors nested within hospitals. Multilevel models incorporate group-specific effects in the regression model and assume that they vary randomly across groups according to some a priori distribution, commonly a normal distribution. This assumption makes multilevel models natural candidates for Bayesian analysis. Bayesian multilevel models additionally assume that other model parameters such as regression coefficients and variance components–variances of group-specific effects–are also random. This workshop will provide a brief overview of Bayesian analysis and classical multilevel models and will concentrate on multilevel modeling from the Bayesian perspective. It will demonstrate the use of Bayesian multilevel models in various applications and how to fit them using Stata. Basic familiarity with Bayesian analysis and classical multilevel models and how to use them in Stata will prove useful.
Fee: Added fee during registration

Title: Survey data analysis with Stata
Presenter: Bill Rising, Director of Educational Services, StataCorp
Where: JSM 2019 Annual Meeting
Date: Wednesday, 31 July 2019
Time: 1:00 PM to 2:45 PM
Abstract: This workshop will cover using Stata for survey data analysis, i.e. data arising from designed samples from a fixed population. I will briefly introduce the sampling methods used to collect survey data, demonstrate how to tell Stata about these methods, and discuss how they affect common estimators such as totals, ratios, and regression coeffcients. We will then show how variance estimates incorporating the sampling design can be computed simply by using the svy prefix command. Finally, I will talk about poststratification, calibration, subpopulation estimation, and visualizing models in some detail, including how to work with certainty sampling units and strata with a single sampling unit. I will illustrate each topic with an example in a Stata session. Knowledge of Stata is not required, but basic statistical knowledge, such as topics covered in an introductory statistics course, is assumed.
Fee: Added fee during registration

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