When several types of recurrent events may arise, interest often lies in marginal modeling and studying the nature of the dependence structure. In this paper, we propose a multivariate mixed-Poisson model with the dependence between events accommodated by type-specific random effects which are associated through use of a Gaussian copula. Such models retain marginal features with a simple interpretation, reflect the heterogeneity in risk for each type of event, and provide insight into the dependence between the different types of events. Semiparametric inference is proposed based on composite likelihood to avoid high dimensional integration. An application to a study of nutritional supplements in malnourished children is given in which the goal is to evaluate the reduction in the rate of several different kinds of infection.
Objective To assess sex-specific risk factors for Graves’ orbitopathy (GO) in newly diagnosed Graves’ disease (GD) patients. Methods A retrospective cohort study was conducted using the National Health Insurance Service’s sample database, which consisted of 1,137,861 subjects from 2002 to 2019. The international classification of disease-10 codes was used to identify those who developed GD (E05) and GO (H062). A multivariable Cox proportional hazards model was used to estimate the effect of risk factors on GO development. Results Among 2145 male and 5047 female GD patients, GO occurred in 134 men (6.2%) and 293 women (5.8%). A multivariable Cox regression model revealed that GO development was significantly associated with younger age (HR = 0.84, 95% CI = 0.73–0.98), low income (HR = 0.55, 95% CI = 0.35–0.86), and heavy drinking (HR = 1.79, 95% CI = 1.10–2.90) in men, and with younger age (HR = 0.89, 95% CI = 0.81–0.98), lower body mass index (HR = 0.55, 95% CI = 0.33–0.90), high total cholesterol (HR = 1.04, 95% CI = 1.01–1.06), hyperlipidaemia (HR = 1.37, 95% CI = 1.02–1.85), and lower statin dose (HR = 0.37, 95% CI = 0.22–0.62) in women. There was no association between smoking and GO development in both men and women. Conclusions The risk factors for GO development were sex-dependent. These results show the need for more sophisticated attention and support considering sex characteristics in GO surveillance.
Summary Family studies involve the selection of affected individuals from a disease registry who provide right-truncated ages of disease onset. Coarsened disease histories are then obtained from consenting family members, either through examining medical records, retrospective reporting, or clinical examination. Methods for dealing with such biased sampling schemes are available for continuous, binary, and failure time responses, but methods for more complex life history processes are less developed. We consider a simple joint model for clustered illness-death processes which we formulate to study covariate effects on the marginal intensity for disease onset and to study the within-family dependence in disease onset times. We construct likelihoods and composite likelihoods for family data obtained from biased sampling schemes. In settings where the disease is rare and data are insufficient to fit the model of interest, we show how auxiliary data can augment the composite likelihood to facilitate estimation. We apply the proposed methods to analyze data from a family study of psoriatic arthritis carried out at the University of Toronto Psoriatic Arthritis Registry.
Marginal rate-based analyses are widely used for the analysis of recurrent events in clinical trials. In many areas of application, the events are not instantaneous but rather signal the onset of a symptomatic episode representing a recurrent infection, respiratory exacerbation, or bout of acute depression. In rate-based analyses, it is unclear how to best handle the time during which individuals are experiencing symptoms and hence are not at risk. We derive the limiting value of the Nelson-Aalen estimator and estimators of the regression coefficients under a semiparametric rate-based model in terms of an underlying two-state process. We investigate the impact of the distribution of the episode durations, heterogeneity, and dependence on the asymptotic and finite sample properties of standard estimators. We also consider the impact of these features on power in trials designed to test intervention effects on rate functions. An application to a trial of individuals with herpes simplex virus is given for illustration.
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