Multivariate frailty models for multi-type recurrent event data and its application to cancer prevention trial

Abstract

. (2016). Multivariate frailty models for multi-type recurrent event data and its application to cancer prevention trial. Computational Statistics and Data Analysis, 101, 161-173. DOI: 10.1016/j.csda.2016.01.018 General rights Copyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from Discovery Research Portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain.• You may freely distribute the URL identifying the publication in the public portal. Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. AbstractMulti-type recurrent event data arise in many situations when two or more different event types may occur repeatedly over an observation period. For example, in a randomized controlled clinical trial to study the efficacy of nutritional supplements for skin cancer prevention, there can be two types of skin cancer events occur repeatedly over time. The research objectives of analyzing such data often include characterizing the event rate of different event types, estimating the treatment effects on each event process, and understanding the correlation structure among different event types. In this paper, we propose the use of a proportional intensity model with multivariate random effects to model such data. The proposed model can take into account the dependence among different event types within a subject as well as the treatment effects.Maximum likelihood estimates of the regression coefficients, variance-covariance components, and the nonparametric baseline intensity function are obtained via a Monte Carlo Expectation Maximization (MCEM) algorithm. The expectation step of the algorithm involves the calculation of the conditional expectations of the random effects by using the Metropolis-Hastings sampling. Our proposed method can easily handle recurrent event data that have more than two types of events. Simulation studies were used to validate the performance of the proposed method, followed by an application to the skin cancer prevention data.

Copula-Frailty Models for Recurrent Event Data Based on Monte Carlo EM Algorithm

“…In literature, multivariate frailties/random effects are incorporated into models to accommodate for within-subject event dependence and the dependence among different types of events. A common assumption is that the distribution of frailties/random effects belongs to some parametric family, and the normal distribution is used most of the time for modeling random effects (e.g., Zeng et al 2014, Bedair et al 2016, and Lin et al 2017).…”

“…The multivariate normal distribution can be obtained using the Gaussian copula with normal marginal distributions. Bedair et al (2016) used a multivariate normal distribution to model multi-type recurrent event data. Tawiah, McLachlan, and Ng (2020) also used mul-tivariate normal distribution to model recurrent events with dependent censoring and cure fraction.…”

Methods for Multivariate Recurrent Event Data with Measurement Error and Informative Censoring

“…Hence, the assumption of independent censoring may not hold in the NPC data. To incorporate informative censoring in multivariate recurrent event data analysis, some authors consider shared frailty models (Ning et al, 2017; Bedair et al, 2016). To be specific, a shared frailty model allows each type of recurrent event process to be correlated with the censoring time through a type-specific frailty variable.…”

“…To be specific, a shared frailty model allows each type of recurrent event process to be correlated with the censoring time through a type-specific frailty variable. The existing methods based on the shared frailty model assume either a specific distribution for the frailties (Bedair et al, 2016) or the Poisson model for the multivariate recurrent event processes conditioning on the frailties (Ning et al, 2017). Although these two assumptions can be relaxed simultaneously by a recent work (Xu et al, 2017), all of these methods require that the covariates are accurately measured, which may not be valid in practice.…”

Modeling past event feedback through biomarker dynamics in the multistate event analysis for cardiovascular disease data

“…Cook et al (2004) introduced multivariate random effects to describe the dependence among events in clustered progressive multi-state processes. Bedair et al (2016) considered a multivariate frailty model to characterize the event rate of multi-type recurrent event data, taking into account the dependence among different event types. The random effects may help in understanding the dependence between state transitions.…”