A Score Test for Association of a Longitudinal Marker and an Event with Missing Data
Article first published online: 15 SEP 2009
© 2009, The International Biometric Society
Volume 66, Issue 3, pages 726–732, September 2010
How to Cite
Finkelstein, D. M., Wang, R., Ficociello, L. H. and Schoenfeld, D. A. (2010), A Score Test for Association of a Longitudinal Marker and an Event with Missing Data. Biometrics, 66: 726–732. doi: 10.1111/j.1541-0420.2009.01326.x
- Issue published online: 15 SEP 2009
- Article first published online: 15 SEP 2009
- Received January 2009. Revised May 2009. Accepted June 2009.
- EM algorithm;
- Conditional expected score test (CEST);
- Interval-censored failure time data;
- Pooling repeated observations (PRO) logistic model;
- Random effects model
Summary: Often clinical studies periodically record information on disease progression as well as results from laboratory studies that are believed to reflect the progressing stages of the disease. A primary aim of such a study is to determine the relationship between the lab measurements and a disease progression. If there were no missing or censored data, these analyses would be straightforward. However, often patients miss visits, and return after their disease has progressed. In this case, not only is their progression time interval censored, but their lab test series is also incomplete. In this article, we propose a simple test for the association between a longitudinal marker and an event time from incomplete data. We derive the test using a very intuitive technique of calculating the expected complete data score conditional on the observed incomplete data (conditional expected score test, CEST). The problem was motivated by data from an observational study of patients with diabetes.