Search
   >> Home >> Resources & support >> Users Group meetings >> 2012 Italian Stata Users Group meeting >> Abstracts

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.

Materials:
it12_bartlett.pdf

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.

Materials:
it12_cerulli.pdf

Spatial Data Analysis in Stata: An Overview


Maurizio Pisati
Università degli Studi di Milano–Bicocca

Abstract not available.

Materials:
it12_pisati.pdf

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.

Materials:
it12_daniel_de_stavolo_cousens.pdf

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.

Materials:
it12_de_stavolo_daniel.pdf

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.

Materials:
it12_rising.pdf

The Stata Blog: Not Elsewhere Classified Find us on Facebook Follow us on Twitter LinkedIn Google+ Watch us on YouTube