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2012 Italian Stata Users Group meeting: Abstracts

Handling missing data in Stata—a whirlwind tour

Jonathan Bartlett
London School of Hygiene and Tropical Medicine

Missing data is a pervasive issue in epidemiological, clinical, social, and economic studies. This presentation offers a brief overview of the conceptual issues raised by missing data, followed by an overview of some of the principled statistical methods of handling missing data that are implemented in Stata 12, including multiple imputation and inverse probability weighting.


Advanced Stata Dialog Programming with VISUA

Giovanni Luca Lo Magno
Università di Palermo

contreatreg: A Stata module for estimating dose response treatment models under (continuous) treatment endogeneity and heterogeneous response to observable confounders

Giovanni Cerulli
Institute for Economic Research on Firms and Growth, National Research Council of Italy

Abstract not available.


Spatial Data Analysis in Stata: An Overview

Maurizio Pisati
Università degli Studi di Milano–Bicocca

Abstract not available.


gformula: Estimating causal effects in the presence of time-varying confounding or mediation

Rhian Daniel
London School of Hygiene and Tropical Medicine

Abstract not available.


Maternal characteristics, childhood growth, and eating disorders: a study of mediation using gformula

Bianca De Stavola
London School of Hygiene and Tropical Medicine

Abstract not available.


Working in the margins to plot a clear course

Bill Rising
StataCorp LP

Visualizing the true effect of a predictor over a range of values can be difficult for models that are not parameterized in their natural metric, such as logistic or (even more so) probit models. Interaction terms in such models cause even more fogginess. This illustrates how both the margins and the marginsplot commands can make for much clearer explanations of effects for both nonstatisticians and statisticians alike.






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