activity
20042023
most citedRobust Inference for Univariate Proportional Hazards Frailty Regression Models

107 citations · 154 across the 8 of their papers we have counts for

collaborators

8 papers

math.ST2023

Regression analysis of longitudinal data with mixed synchronous and asynchronous longitudinal covariates

Zhuowei Sun, Hongyuan Cao, Li Chen +1

In linear models, omitting a covariate that is orthogonal to covariates in the model does not result in biased coefficient estimation. This in general does not hold for longitudina…

stat.ME2021★ 1 cited

A Parametric Approach to Relaxing the Independence Assumption in Relative Survival Analysis

Reuben Adatorwovor, Aurelien Latouche, Jason P. Fine

With known cause of death (CoD), competing risk survival methods are applicable in estimating disease-specific survival. Relative survival analysis may be used to estimate disease-…

physics.med-ph2020

Validation of Sinus Drug Delivery Computational Fluid Dynamics (CFD) Modeling with In Vitro Gamma Scintigraphy

K. Kudlaty, W. Bennett, L. Holbrook +10

Background: Chronic rhinosinusitis (CRS) is a prevalent and disruptive disease. Medical management including nasal steroid sprays is the primary treatment modality. Computational f…

physics.med-ph2019

Numerical evaluation of spray position for improved nasal drug delivery

Saikat Basu, Landon T Holbrook, Kathryn Kudlaty +16

Topical intra-nasal sprays are amongst the most commonly prescribed therapeutic options for sinonasal diseases in humans. However, inconsistency and ambiguity in instructions show…

math.ST2019

Efficient estimation of accelerated lifetime models under length-biased sampling

Pourab Roy, Jason P. Fine, Michael R. Kosorok

In prevalent cohort studies where subjects are recruited at a cross-section, the time to an event may be subject to length-biased sampling, with the observed data being either the…

stat.ME2019★ 6 cited

Causal Proportional Hazards Estimation with a Binary Instrumental Variable

Behzad Kianian, Jung In Kim, Jason P. Fine +1

Instrumental variables (IV) are a useful tool for estimating causal effects in the presence of unmeasured confounding. IV methods are well developed for uncensored outcomes, partic…