Yei Eun Shin

It is now quite common to have panels in which T, the number of time series observations on the N groups, is quite large. The usual practice is either to estimate N separate regressions and calculate the mean, which we call the Mean Group estimator, or to pool the data and assume the slope coefficients and variances are identical. In this paper, the authors propose an intermediate procedure, referred to as the Pooled Mean Group (PMG) estimator, which constrains the long-run coefficients to be id

Cohort studies provide information on relative hazards and pure risks of disease. For rare outcomes, large cohorts are needed to have sufficient numbers of events, making it costly to obtain covariate information on all cohort members. We focus on nested case-control designs that are used to estimate relative hazard in the Cox regression model. In 1997, Langholz and Borgan showed that pure risk can also be estimated from nested case-control data. However, these approaches do not take advantage o

In the wind industry, engineers perform retrofitting upgrades on in-service wind turbines for the purpose of improving power production capabilities. Considering how costly an upgrade can be, people often wonder about the upgrade effect: whether it indeed improves turbine performances, and if so, how much. One cannot simply compare power outputs for the purpose of assessing a turbine’s improvement, as wind power generation is affected by an array of environmental covariates, including wind speed

This paper focuses on analysis of spatiotemporal binary data with absorbing states. The research was motivated by a clinical study on amyotrophic lateral sclerosis (ALS), a neurological disease marked by gradual loss of muscle strength over time in multiple body regions. We propose an autologistic regression model to capture complex spatial and temporal dependencies in muscle strength among different muscles. As it is not clear how the disease spreads from one muscle to another, it may not be re

We study the efficiency of covariate-specific estimates of pure risk (one minus the survival function) when some covariates are only available for case-control samples nested in a cohort. We focus on the semiparametric additive hazards model in which the hazard function equals a baseline hazard plus a linear combination of covariates with either time-varying or time-invariant coefficients. A published approach uses the design-based inclusion probabilities to reweight the nested case-control data

Amyotrophic lateral sclerosis (ALS) is a neurological disease that starts at a focal point and gradually spreads to other parts of the nervous system. One of the main clinical symptoms of ALS is muscle weakness. To study spreading patterns of muscle weakness, we analyze spatiotemporal binary muscle strength data, which indicates whether observed muscle strengths are impaired or healthy. We propose a hidden Markov model-based approach that assumes the observed disease status depends on two latent

Nested case-control design (NCC) is a cost-effective outcome-dependent design in epidemiology that collects all cases and a fixed number of controls at the time of case diagnosis from a large cohort. Due to inefficiency relative to full cohort studies, previous research developed various estimation methodologies but changing designs in the formulation of risk sets was considered only in view of potential bias in the partial likelihood estimation. In this paper, we study a modified design that ex

The standard approach to regression modeling for cause-specific hazards with prospective competing risks data specifies separate models for each failure type. An alternative proposed by Lunn and McNeil (1995) assumes the cause-specific hazards are proportional across causes. This may be more efficient than the standard approach, and allows the comparison of covariate effects across causes. In this paper, we extend Lunn and McNeil (1995) to nested case-control studies, accommodating scenarios wit

Missing covariates are a common challenge when applying an existing logistic regression model to new or external datasets, particularly in the context of model updating. While regression calibration and model updating methods have been developed to address such partial data availability, each has limitations in terms of bias, variance, and sensitivity to model misspecification. In this study, we propose a surrogate-calibrated updating (SCU) method that integrates calibration and updating approac

Longitudinal data are often available in cohort studies and clinical settings, such as covariates collected at cohort follow-up visits or prescriptions captured in electronic health records. Such longitudinal information, if correlates with the health event of interest, may be incorporated to dynamically predict the probability of a health event with better precision. Landmarking is a popular approach to dynamic prediction. There are well-established methods for landmarking using full cohort dat

BACKGROUND: Understanding factors associated with opioid dispensing in cancer patients is important for developing tailored guidelines and ensuring equitable access to pain management. We examined patterns and predictors of opioid dispensing among older cancer patients from 2008 to 2015. METHODS: We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database linked to Medicare claims. We included the most common cancer types among patients aged 66-95 years. Opioids dispens