Journal
BIOMETRICS
Volume 66, Issue 2, Pages 393-404Publisher
WILEY
DOI: 10.1111/j.1541-0420.2009.01307.x
Keywords
Chi-squared test; Correlated failure times; Cox's model; Generalized estimating equation; Marginal hazard rate; Quadratic inference function
Funding
- National Science Foundation [DMS-03-48764]
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We propose a new estimation method for multivariate failure time data using the quadratic inference function (QIF) approach. The proposed method efficiently incorporates within-cluster correlations. Therefore, it, is more efficient than those that ignore within-cluster correlation. Furthermore, the proposed method is easy to implement. Unlike the weighted estimating equations in Cai taut Prentice (1995, Biometrika 82, 151-164), it is not necessary to explicitly estimate the correlation parameters. This simplification is particularly useful in analyzing data with large cluster size where it is difficult to estimate intracluster correlation. Under certain regularity conditions, we show the consistency and asymptotic normality of the proposed QIF estimators. A chi-squared test is also developed for hypothesis testing. We conduct extensive Monte Carlo simulation studies to assess the finite sample performance of the proposed methods. We also illustrate the proposed methods by analyzing primary biliary cirrhosis (PBC) data.
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