A Mixture Model for Bivariate Interval-Censored Failure Times with Dependent Susceptibility

Spatial dependence modeling of latent susceptibility and time to joint damage in psoriatic arthritis

Important scientific insights into chronic diseases affecting several organ systems can be gained from modeling spatial dependence of sites experiencing damage progression. We describe models and methods for studying spatial dependence of joint damage in psoriatic arthritis (PsA). Since a large number of joints may remain unaffected even among individuals with a long disease history, spatial dependence is first modeled in latent joint-specific indicators of susceptibility. Among susceptible joints, a Gaussian copula is adopted for dependence modeling of times to damage. Likelihood and composite likelihoods are developed for settings, where individuals are under intermittent observation and progression times are subject to type K interval censoring. Two-stage estimation procedures help mitigate the computational burden arising when a large number of processes (i.e., joints) are under consideration. Simulation studies confirm that the proposed methods provide valid inference, and an application to the motivating data from the University of Toronto Psoriatic Arthritis Clinic yields important insights which can help physicians distinguish PsA from arthritic conditions with different dependence patterns.

Kaplan-Meier Survival, Actuarial Survival, Censoring, and Competing Events - What is What?

Survival analyses, most commonly Kaplan-Meier curves, are frequently utilized in the field of cardiovascular medicine to analyze and graphically illustrate the differences in outcomes between two or multiples study groups in randomized-controlled trials. While Kaplan-Meier curves provide a nice representation of the survival (or the occurrence of other events of interest) of one or several groups of patients, they are commonly misused, especially in the setting of interval censoring/actuarial survival and/or competing events. Here, we sought to provide the reader with a simple example that clarifies some of these concepts. Many randomized-controlled trials (RCTs) and observational studies in the field of cardiovascular medicine utilize some sort of survival analysis, the most common being the reporting of Kaplan-Meier curves and a corresponding P value. Unfortunately in many instances, the terms Kaplan-Meier Survival and Actuarial Survival are used by authors interchangeably, where in fact, these two terms represent very different concepts that go beyond semantics and therefore need to be distinguished. This is also the case for other important concepts related to survival analysis such as right censoring, interval censoring, and competing events, and this is despite the presence of extensive articles on the proper use and interpretation of survival analyses for both statisticians and physicians without formal statistical background.(1) Here, we seek to provide the reader with a simple example that clarifies some of these concepts and emphasizes understanding the salient differences between them.