Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Kruger says reports of phantom mobile phone ringing/vibrating more common among anxious

Stafford says too early to say whether stock market declines will curtail Americans' spending

Eisenberg says many colleges now train campus personnel to spot and refer troubled college students


Call for papers: Conference on Integrating Genetics and the Social Sciences, Oct 21-22, 2016, CU-Boulder

PRB training program in policy communication for pre-docs. Application deadline, 2.28.2016

Call for proposals: PSID small grants for research on life course impacts on later life wellbeing

PSC News, fall 2015 now available

Next Brown Bag

Monday, Feb 1 at noon, 6050 ISR-Thompson
Sarah Miller

A Bayesian model for time-to-event data with informative censoring

Publication Abstract

Kaciroti, N., Trivellore Raghunathan, J. Taylor, and S. Julius. 2012. "A Bayesian model for time-to-event data with informative censoring." Biostatistics, 13(2): 341-54.

Randomized trials with dropouts or censored data and discrete time-to-event type outcomes are frequently analyzed using the Kaplan-Meier or product limit (PL) estimation method. However, the PL method assumes that the censoring mechanism is noninformative and when this assumption is violated, the inferences may not be valid. We propose an expanded PL method using a Bayesian framework to incorporate informative censoring mechanism and perform sensitivity analysis on estimates of the cumulative incidence curves. The expanded method uses a model, which can be viewed as a pattern mixture model, where odds for having an event during the follow-up interval $$({t}{k-1},{t}{k}]$$, conditional on being at risk at $${t}_{k-1}$$, differ across the patterns of missing data. The sensitivity parameters relate the odds of an event, between subjects from a missing-data pattern with the observed subjects for each interval. The large number of the sensitivity parameters is reduced by considering them as random and assumed to follow a log-normal distribution with prespecified mean and variance. Then we vary the mean and variance to explore sensitivity of inferences. The missing at random (MAR) mechanism is a special case of the expanded model, thus allowing exploration of the sensitivity to inferences as departures from the inferences under the MAR assumption. The proposed approach is applied to data from the TRial Of Preventing HYpertension.

DOI:10.1093/biostatistics/kxr048 (Full Text)

PMCID: PMC3297827. (Pub Med Central)

Browse | Search : All Pubs | Next