Nonparametric inference and uniqueness for periodically observed progressive disease models

Beth Griffin and Stephen Lagakos have a new paper in Lifetime Data Analysis. They consider panel observed progressive disease model (chain-of-events) data. The NPMLE estimator under a discrete-time semi-Markov assumption was developed by Sternberg and Satten (Biometrics, 1999). For datasets where individuals are observed at different times, some discretization of the data is required. An issue with the NPMLE is that it is not guaranteed to be unique and therefore reporting a single NPMLE may be misleading. The paper develops procedures for determining which components of the NPMLE are unique based on considering various re-parameterizations of the likelihood. The method is demonstrated on three example datasets including one on bronchiolitis obliterans syndrome in post-lung transplantation patients and one on primary HIV infection. In addition, the authors also provide a more intuitive algorithm for obtaining the NPMLE than the self-consistency algorithm of Sternberg and Satten.

On Induced Dependent Censoring for Quality Adjusted Lifetime (QAL) Data in Simple Illness-Death Model

A new paper by Pradhan and Dewanji in Statistics and Probability Letters considers the problem of induced dependent censoring in quality adjusted lifetime data. Quality adjusted survival time and quality adjusted censoring times are correlated even if the raw survival and censoring times are independent. Kaplan-Meier based estimates of QAL using the QA survival and censoring times will therefore be biased. The paper investigates the nature of the correlation and bias for the case of a simple three-state illness-death model. Under a semi-Markov assumption, they show that QA survival and censoring are positively correlated when the healthy state has greater utility than illness, but the correlation is negative if the relative utilities are reversed.