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.

Regression analysis of mean quality-adjusted survival time based on pseudo-observations

Gisela Tunes da Silva and John Klein have a new paper in Statistics in Medicine. This applies the pseudo-observations approach to the estimation of mean quality adjusted survival time (QAS). They assume an identity link function for the relationship between mean QAS and covariates at baseline. This direct regression is analogous to similar approaches taken in the competing risks context by Graw, Gerds and Schumacher in Lifetime Data Analysis.