scispace - formally typeset
J

Jason P. Fine

Researcher at University of North Carolina at Chapel Hill

Publications -  288
Citations -  25597

Jason P. Fine is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Estimator & Covariate. The author has an hindex of 60, co-authored 280 publications receiving 21613 citations. Previous affiliations of Jason P. Fine include University of Wisconsin-Madison & Harvard University.

Papers
More filters
Journal ArticleDOI

A Proportional Hazards Model for the Subdistribution of a Competing Risk

TL;DR: This article proposes methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation, but these methods do not allow the analyst to directly assess the effect of a covariate on the marginal probability function.
Journal ArticleDOI

Cystic fibrosis: a worldwide analysis of CFTR mutations--correlation with incidence data and application to screening.

TL;DR: From comprehensive assessment of data, it is offered recommendations that multiple CFTR alleles should eventually be included to increase the sensitivity of newborn screening programs employing two‐tier testing with trypsinogen and DNA analysis.
Journal ArticleDOI

Diagnosis of Invasive Aspergillosis Using a Galactomannan Assay: A Meta-Analysis

TL;DR: The galactomannan assay has moderate accuracy for diagnosis of invasive aspergillosis in immunocompromised patients and is more useful in patients who have hematological malignancy or who have undergone hematopoietic cell transplantation than in solid-organ transplant recipients.
Journal ArticleDOI

Practical recommendations for reporting Fine-Gray model analyses for competing risk data

TL;DR: It is found that many authors provided an unclear or incorrect interpretation of the regression coefficients associated with the Fine‐Gray subdistribution hazard model, and suggestions for interpreting these coefficients are proposed.
Journal ArticleDOI

Estimating equations for association structures.

TL;DR: This paper investigates generalized estimating equations for association parameters, which are frequently of interest in family studies, with emphasis on covariance estimation, and finds that the formula for the approximate jackknife variance estimator in Ziegler et al. is deficient, resulting in systematic deviations from the fully iterated jackknifevariance estimator.