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

PSC In The News

RSS Feed icon

Indian lab cofounded by Adhvaryu demonstrates links among women's skills training, employment, welfare, and company profits

Bleakley says state educational initiatives favoring skills-oriented career training may have more ROI for employers than workers

Bailey's study linking Pill access to women's wage gains bolsters NYT critique of federal anti-contraception moves

More News


PDHP invites applications for Faculty Small Grants in support of population science

ISR seeking applicants for new Community Guides program

PRB policy communication training for pre-docs extends application deadline to March 12

Needham, Hicken, Mitchell and colleagues link maternal social disadvantage and newborn telomere length

More Highlights

Next Brown Bag

Mon, April 2, 2018, noon: Sean Reardon on Educational Inequality

Bias Due to Left Truncation and Left Censoring in Longitudinal Studies of Developmental and Disease Processes

Publication Abstract

Cain, K., Sioban D. Harlow, R. J. A. Little, B. Nan, M. Yosef, J. Taffe, and Michael R. Elliott. 2011. "Bias Due to Left Truncation and Left Censoring in Longitudinal Studies of Developmental and Disease Processes." American Journal of Epidemiology, 173(9): 1078-1084.

In longitudinal studies of developmental and disease processes, participants are followed prospectively with intermediate milestones identified as they occur. Frequently, studies enroll participants over a range of ages including ages at which some participants' milestones have already passed. Ages at milestones that occur prior to study entry are left censored if individuals are enrolled in the study or left truncated if they are not. The authors examined the bias incurred by ignoring these issues when estimating the distribution of age at milestones or the time between 2 milestones. Methods that account for left truncation and censoring are considered. Data on the menopausal transition are used to illustrate the problem. Simulations show that bias can be substantial and that standard errors can be severely underestimated in naive analyses that ignore left truncation. Bias can be reduced when analyses account for left truncation, although the results are unstable when the fraction truncated is high. Simulations suggest that a better solution, when possible, is to modify the study design so that information on current status (i.e., whether or not a milestone has passed) is collected on all potential participants, analyzing those who are past the milestone at the time of recruitment as left censored rather than excluding such individuals from the analysis.

DOI:10.1093/aje/kwq481 (Full Text)

PMCID: PMC3121224. (Pub Med Central)

Country of focus: United States of America.

Browse | Search : All Pubs | Next