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

PSC In The News

RSS Feed icon

Miller et al. find benefits of Medicaid for pregnant mothers in 1980s carry over two generations

Starr's findings account for some of the 19% black-white gap in federal sentencing

Frey says suburbs are aging, cities draw millennials

More News

Highlights

Bailey et al. find higher income among children whose parents had access to federal family planning programs in the 1960s and 70s

U-M's campus climate survey results discussed in CHE story

U-M honors James Jackson's groundbreaking work on how race impacts the health of black Americans

U-M is the only public and non-coastal university on Forbes' top-10 list for billionaire production

More Highlights

Next Brown Bag

Mon, Jan 22, 2018, noon: Narayan Sastry

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