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Andrea Lamont

Researcher at University of South Carolina

Publications -  31
Citations -  634

Andrea Lamont is an academic researcher from University of South Carolina. The author has contributed to research in topics: Sample size determination & Mixture model. The author has an hindex of 11, co-authored 27 publications receiving 451 citations.

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A practical implementation science heuristic for organizational readiness: R = MC(2).

TL;DR: It is proposed that organizational readiness involves: 1) the motivation to implement an innovation, 2) the general capacities of an organization, and 3) the innovation-specific capacities needed for a particular innovation.
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Initiation and persistence of alcohol use in United States Black, Hispanic, and White male and female youth

TL;DR: Novel findings from the new analytic models suggest differential implications of early alcohol use by race and gender might be less consequential for males who initiate alcohol use early, Black, and Hispanic youth than for their female and White counterparts.
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Identification of predicted individual treatment effects in randomized clinical trials.

TL;DR: This study first applies the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms and conducts a Monte Carlo simulation study to evaluate the accuracy of predictedindividual treatment effects.
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Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

Cara Lewis, +343 more
TL;DR: Table of contentsIntroduction to the 3rd Biennial Conference of the Society for Implementation Research Collaboration: advancing efficient methodologies through team science and community partnerships.
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Regression mixture models : Does modeling the covariance between independent variables and latent classes improve the results?

TL;DR: This simulation study tested the effects of violating an implicit assumption often made in regression mixture models; that is, independent variables in the model are not directly related to latent classes, to see if these models can detect piecewise relations.