@article {22509,
	title = {Applications of a Parametric Model for Informative Censoring},
	journal = {Biometrics},
	volume = {60},
	year = {2004},
	pages = {704-714},
	abstract = {<p>Summary. In this article, we explore the use of a parametric model (for analyzing survival data)which is\&nbsp;defined to allow sensitivity analysis for the presence of informative censoring. The dependence between the\&nbsp;failure and the censoring processes is expressed through a parameter δ and a general bias function B(t, θ). We calculate the expectation of the potential bias due to informative censoring, which is an overall measure\&nbsp;of how misleading our results might be if censoring is actually nonignorable. Bounds are also calculated for\&nbsp;quantities of interest, e.g., parameter of the distribution of the failure process, which do not depend on the choice of the bias function for fixed δ. An application that relates to systematic lupus erythematosus data\&nbsp;illustrates how additional information can result in reducing the uncertainty on estimates of the location\&nbsp;parameter. Sensitivity analysis on a relative risk parameter is also explored.</p>},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0006-341X.2004.00220.x},
	author = {Fotios Siannis}
}
