N
Nan M. Laird
Researcher at Harvard University
Publications - 354
Citations - 155833
Nan M. Laird is an academic researcher from Harvard University. The author has contributed to research in topics: Population & Genetic association. The author has an hindex of 108, co-authored 352 publications receiving 146781 citations. Previous affiliations of Nan M. Laird include Seoul National University & Yale University.
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Meta-Analysis in Clinical Trials*
TL;DR: This paper examines eight published reviews each reporting results from several related trials in order to evaluate the efficacy of a certain treatment for a specified medical condition and suggests a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.
Journal ArticleDOI
Random-effects models for longitudinal data
Nan M. Laird,James H. Ware +1 more
TL;DR: In this article, a unified approach to fitting two-stage random-effects models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed.
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Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I.
Troyen A. Brennan,Lucian L. Leape,Nan M. Laird,Liesi E. Hebert,A R Localio,Ann G. Lawthers,Joseph P. Newhouse,Paul C. Weiler,Howard H. Hiatt +8 more
TL;DR: There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.
Journal ArticleDOI
The nature of adverse events in hospitalized patients. Results of the Harvard Medical Practice Study II
Lucian L. Leape,Troyen A. Brennan,Nan M. Laird,Ann G. Lawthers,A R Localio,B A Barnes,Liesi E. Hebert,Joseph P. Newhouse,Paul C. Weiler,Howard H. Hiatt +9 more
TL;DR: The high proportion that are due to management errors suggests that many others are potentially preventable now, and reducing the incidence of these events will require identifying their causes and developing methods to prevent error or reduce its effects.