arXiv subjects

Sharon Curhan

Publications and source records attributed to Sharon Curhan.

3 recordsLinked to original sources

arXiv2026

Joint Group-Based Trajectory Modeling for Paired Repeated Measures: An Application to Audiometric Phenotypes and Dietary Associations

The assumption of conditional independence in conventional group-based trajectory modeling (GBTM) is often violated by paired repeated-measures data with heterogeneous trajectory patterns. While random-effects models can accommodate this dependence, they inflate within-group variability and blur distinct phenotypic shapes. We propose a joint GBTM framework that explicitly models hierarchical dependence in paired trajectories while allowing them to follow different latent patterns. We develop a robust two-stage approach to address estimation challenges caused by rare latent groups, and a one-stage EM algorithm that serves as a theoretical baseline under balanced group sizes. Simulations demonstrate that our methods correct the biases caused by ignoring hierarchical dependence. The proposed model was applied to real-world data from the Conservation of Hearing Study (CHEARS) Audiology Assessment Arm (AAA), a subcohort of the Nurses' Health Study II (NHS II), to identify distinct audiometric phenotypes and to investigate the association between the Dietary Approaches to Stop Hypertension (DASH) dietary adherence score and the latent audiometric patterns.

stat.ME↗

arXiv2023

Generalized Estimating Equations for Hearing Loss Data with Specified Correlation Structures

Due to the nature of pure-tone audiometry test, hearing loss data often has a complicated correlation structure. Generalized estimating equation (GEE) is commonly used to investigate the association between exposures and hearing loss, because it is robust to misspecification of the correlation matrix. However, this robustness typically entails a moderate loss of estimation efficiency in finite samples. This paper proposes to model the correlation coefficients and use second-order generalized estimating equations to estimate the correlation parameters. In simulation studies, we assessed the finite sample performance of our proposed method and compared it with other methods, such as GEE with independent, exchangeable and unstructured correlation structures. Our method achieves an efficiency gain which is larger for the coefficients of the covariates corresponding to the within-cluster variation (e.g., ear-level covariates) than the coefficients of cluster-level covariates. The efficiency gain is also more pronounced when the within-cluster correlations are moderate to strong, or when comparing to GEE with an unstructured correlation structure. As a real-world example, we applied the proposed method to data from the Audiology Assessment Arm of the Conservation of Hearing Study, and studied the association between a dietary adherence score and hearing loss.

stat.ME↗

arXiv2022

Analytical method for detecting outlier evaluators

Epidemiologic and medical studies often rely on evaluators to obtain measurements of exposures or outcomes for study participants, and valid estimates of associations depends on the quality of data. Even though statistical methods have been proposed to adjust for measurement errors, they often rely on unverifiable assumptions and could lead to biased estimates if those assumptions are violated. Therefore, methods for detecting potential `outlier' evaluators are needed to improve data quality during data collection stage. In this paper, we propose a two-stage algorithm to detect `outlier' evaluators whose evaluation results tend to be higher or lower than their counterparts. In the first stage, evaluators' effects are obtained by fitting a regression model. In the second stage, hypothesis tests are performed to detect `outlier' evaluators, where we consider both the power of each hypothesis test and the false discovery rate (FDR) among all tests. We conduct an extensive simulation study to evaluate the proposed method, and illustrate the method by detecting potential `outlier' audiologists in the data collection stage for the Audiology Assessment Arm of the Conservation of Hearing Study, an epidemiologic study for examining risk factors of hearing loss in the Nurses' Health Study II. Our simulation study shows that our method not only can detect true `outlier' evaluators, but also is less likely to falsely reject true `normal' evaluators. Our two-stage `outlier' detection algorithm is a flexible approach that can effectively detect `outlier' evaluators, and thus data quality can be improved during data collection stage.

stat.AP↗