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Andrea Rotnitzky
Researcher at Torcuato di Tella University
Publications - 104
Citations - 17263
Andrea Rotnitzky is an academic researcher from Torcuato di Tella University. The author has contributed to research in topics: Estimator & Missing data. The author has an hindex of 45, co-authored 101 publications receiving 15538 citations. Previous affiliations of Andrea Rotnitzky include Harvard University & National Institutes of Health.
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Estimation of Regression Coefficients When Some Regressors are not Always Observed
TL;DR: In this paper, a new class of semiparametric estimators, based on inverse probability weighted estimating equations, were proposed for parameter vector α 0 of the conditional mean model when the data are missing at random in the sense of Rubin and the missingness probabilities are either known or can be parametrically modeled.
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Weight Gain as a Risk Factor for Clinical Diabetes Mellitus in Women
TL;DR: The relations between change in adult weight and the risk for noninsulin-dependent diabetes mellitus among women during 14 years of follow-up were quantified.
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The Prevention and Treatment of Missing Data in Clinical Trials
Roderick J. A. Little,Ralph B. D'Agostino,Michael L. Cohen,Kay Dickersin,Scott S. Emerson,John T. Farrar,Constantine Frangakis,Joseph W. Hogan,Geert Molenberghs,Susan A. Murphy,James D. Neaton,Andrea Rotnitzky,Daniel O. Scharfstein,Weichung Joe Shih,Jay P. Siegel,Hal S. Stern +15 more
TL;DR: Methods for preventing missing data and, failing that, dealing with data that are missing in clinical trials are reviewed.
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Analysis of semiparametric regression models for repeated outcomes in the presence of missing data
TL;DR: In this article, the authors proposed a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on the vector of explanatory variables in the presence of missing response data.
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Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
TL;DR: In this article, the conditional hazard of dropout is modeled semiparametrically and no restrictions are placed on the joint distribution of the outcome and other measured variables, and it is shown how to make inferences about the marginal mean μ0 when the continuous dropout time Q is modeled semi-parameterically.