A varying-coefficient model for gap times between recurrent events.

Recurrent events often arise in follow-up studies where a subject may experience multiple occurrences of the same type of event. Most regression models for recurrent events consider the time scale measured from the study origin and assume constant effects of covariates. In many applications, however, gap times between recurrent events are of natural interest and moreover the effects may actually vary over time. In this article, we propose a marginal varying-coefficient model for gap times between recurrent events that allows for the intra-individual correlation between events. Estimation and inference procedures are developed for the varying coefficients. Consistency and weak convergence of the proposed estimator are established. Monte Carlo simulation studies demonstrate that the proposed method works well with practical sample sizes. The proposed method is illustrated with an analysis of bladder tumor clinical data.

Dynamic regression with recurrent events.

Recurrent events often arise in follow-up studies where a subject may experience multiple occurrences of the same event. Most regression models with recurrent events tacitly assume constant effects of covariates over time, which may not be realistic in practice. To address time-varying effects, we develop a dynamic regression model to target the mean frequency of recurrent events. We propose an estimation procedure which fully exploits observed data. Consistency and weak convergence of the proposed estimator are established. Simulation studies demonstrate that the proposed method works well, and two real data analyses are presented for illustration.

Testing for Hepatitis C Virus Infection Among Adults Aged ≥18 in the United States, 2013-2017.

OBJECTIVE: Approximately 2.4 million people in the United States are living with hepatitis C virus (HCV) infection. The objective of our study was to describe demographic and socioeconomic characteristics, liver disease-related risk factors, and modifiable health behaviors associated with self-reported testing for HCV infection among adults. METHODS: Using data on adult respondents aged ≥18 from the 2013-2017 National Health Interview Survey, we summarized descriptive data on sociodemographic characteristics and liver disease-related risk factors and stratified data by educational attainment. We used weighted logistic regression to examine predictors of HCV testing. RESULTS: During the study period, 11.7% (95% CI, 11.5%-12.0%) of adults reported ever being tested for HCV infection. Testing was higher in 2017 than in 2013 (adjusted odds ratio [aOR] = 1.27; 95% CI, 1.18-1.36). Adults with ≥some college were significantly more likely to report being tested (aOR = 1.60; 95% CI, 1.52-1.69) than adults with ≤high school education. Among adults with ≤high school education (but not adults with ≥some college), those who did not have health insurance were less likely than those with private health insurance (aOR = 0.78; 95% CI, 0.68-0.89) to get tested, and non-US-born adults were less likely than US-born adults to get tested (aOR = 0.77; 95% CI, 0.68-0.87). CONCLUSIONS: Rates of self-reported HCV testing increased from 2013 to 2017, but testing rates remained low. Demographic characteristics, health behaviors, and liver disease-related risk factors may affect HCV testing rates among adults. HCV testing must increase to achieve hepatitis C elimination targets.