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Last updated: 12 October 2012
Grand Hotel Majestic “Giá Baglioni”
Via Indipendenza, 8
40121 Bologna
Italy
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
Abstract not available.
Materials:
it12_lo_magno.pdf
visua_0_1_beta_windows_setup_and_sources.zip
Abstract not available.
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it12_cerulli.pdf
Abstract not available.
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it12_pisati.pdf
Abstract not available.
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it12_daniel_de_stavolo_cousens.pdf
Abstract not available.
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it12_de_stavolo_daniel.pdf
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
Una-Louise Bell, TStat S.r.l.
tstat@tstat.itRino Bellocco, Karolinska Institutet
rino.bellocco@mep.ki.seGiovanni Capelli, Università degli Studi di Cassino
g.capelli@unicas.itMarcello Pagano, Harvard School of Public Health
pagano@biostat.harvard.eduMaurizio Pisati, Università degli Studi di Milano–Bicocca
maurizio.pisati@unimib.it
TStat S.r.l, the official distributor of Stata in Italy.