A frailty model approach for regression analysis of multivariate current status data

This paper discusses regression analysis of multivariate current status failure time data (The Statistical Analysis of Interval-censoring Failure Time Data. Springer: New York, 2006), which occur quite often in, for example, tumorigenicity experiments and epidemiologic investigations of the natural history of a disease. For the problem, several marginal approaches have been proposed that model each failure time of interest individually (Biometrics 2000; 56:940-943; Statist. Med. 2002; 21:3715-3726). In this paper, we present a full likelihood approach based on the proportional hazards frailty model. For estimation, an Expectation Maximization (EM) algorithm is developed and simulation studies suggest that the presented approach performs well for practical situations. The approach is applied to a set of bivariate current status data arising from a tumorigenicity experiment.

[1]  Xingwei Tong,et al.  Regression Analysis of Multivariate Interval‐Censored Failure Time Data with Application to Tumorigenicity Experiments , 2008, Biometrical journal. Biometrische Zeitschrift.

[2]  Philip Hougaard,et al.  Analysis of Multivariate Survival Data , 2001 .

[3]  Xiaonan Xue,et al.  The analysis of multivariate interval‐censored survival data , 2002, Statistics in medicine.

[4]  D. Oakes,et al.  Bivariate survival models induced by frailties , 1989 .

[5]  D. Clayton,et al.  Multivariate generalizations of the proportional hazards model , 1985 .

[6]  F. T. Wright,et al.  Order restricted statistical inference , 1988 .

[7]  D. Lin,et al.  Regression analysis of multivariate grouped survival data. , 1994, Biometrics.

[8]  Testing Independence for Bivariate Current Status Data , 2004 .

[9]  Xingwei Tong,et al.  The proportional odds model for multivariate interval‐censored failure time data , 2007, Statistics in medicine.

[10]  David B Dunson,et al.  Bayesian Models for Multivariate Current Status Data with Informative Censoring , 2002, Biometrics.

[11]  J. Wellner,et al.  Information Bounds and Nonparametric Maximum Likelihood Estimation , 1992 .

[12]  E. Gumbel.  Bivariate Exponential Distributions , 1960 .

[13]  Hongqi Xue,et al.  Sieve Maximum Likelihood Estimator for Semiparametric Regression Models With Current Status Data , 2004 .

[14]  Jianguo Sun,et al.  A goodness-of-fit test for the marginal Cox model for correlated interval-censored failure time data. , 2006, Biometrical journal. Biometrische Zeitschrift.

[15]  A. Adam Ding,et al.  On assessing the association for bivariate current status data , 2000 .

[16]  Xingqiu Zhao,et al.  Estimating equation approach for regression analysis of failure time data in the presence of interval-censoring , 2005 .

[17]  Jian Huang,et al.  Sieve Estimation for the Proportional-Odds Failure-Time Regression Model with Interval Censoring , 1997 .

[18]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[19]  D. Finkelstein,et al.  A proportional hazards model for interval-censored failure time data. , 1986, Biometrics.

[20]  J. Palmgren,et al.  Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood , 2000, Biometrics.

[21]  D. Finkelstein,et al.  A Proportional Hazards Model for Multivariate Interval‐Censored Failure Time Data , 2000, Biometrics.