scispace - formally typeset
J

John Neuhaus

Researcher at University of California, San Francisco

Publications -  304
Citations -  23514

John Neuhaus is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Covariate & Generalized linear mixed model. The author has an hindex of 73, co-authored 283 publications receiving 20432 citations. Previous affiliations of John Neuhaus include Children's Oncology Group & Leipzig University.

Papers
More filters
Journal ArticleDOI

Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia.

TL;DR: The revised criteria for behavioural variant frontotemporal dementia improve diagnostic accuracy compared with previously established criteria in a sample with known frontotmporal lobar degeneration and reflect the optimized diagnostic features, less restrictive exclusion features and a flexible structure that accommodates different initial clinical presentations.
OtherDOI

Generalized Linear Mixed Models

TL;DR: Generalized linear mixed models are a class of statistical models that handle a wide variety of distributions for the outcome, accommodate nonlinear models, and model correlated data that are capable of estimation and testing of covariate effects.
Journal ArticleDOI

Unanticipated admission to the hospital following ambulatory surgery.

TL;DR: The results indicate that the likelihood of unanticipated admission is related more to the type of anesthesia and surgical procedure rather than to the patient's clinical characteristics.
Journal ArticleDOI

A Comparison of Cluster-Specific and Population-Averaged Approaches for Analyzing Correlated Binary Data

TL;DR: It is shown that, unlike models for correlation Gaussian outcomes, the parameters of the cluster-specific and population-averaged models for correlated binary data describe different types of effects of the covariates on the response probabilities.