JSM 2005 Online Program

Abstract #302515

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	296
Type:	Invited
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Committee on Women in Statistics
Abstract - #302515
Title:	Methods for Validation Sets for Outcomes in Changing Temporal Conditions
Author(s):	M. Elizabeth Halloran*+
Companies:	Emory University
Address:	1518 Clifton Road NE, Room 364, Atlanta , GA, 30322,
Keywords:	vaccine ; missing data ; bootstrap ; time-to-event data
Abstract:	Methods for adjusting for bias in estimates due to mismeasured or missing covariates and outcomes using validation sets have been developed in many types of health studies. These methods can be used for the efficient design and analysis of vaccine studies as well. On one hand, nonspecific case definitions can lead to attenuated efficacy and effectiveness estimates, but confirmation by culture or a quick test of the infectious agent also is expensive and difficult. On the other hand, exposure to infection data can influence estimates of vaccine efficacy, but good data on exposure is difficult to obtain. In this paper, we show how use of small validation sets can correct the bias of the estimates obtained from a large main study while maintaining efficiency. We develop methods for the situation in which the relation of the disease of interest, such as influenza, with respect to the nonspecific illness, is changing over time. We also propose methods for when the vaccine status changes over the course of the study period. We use a semiparametric approach with the bootstrap to estimate confidence intervals.

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