Two-level proportional hazards models.

We extend the proportional hazards model to a two-level model with a random intercept term and random coefficients. The parameters in the multilevel model are estimated by a combination of EM and Newton-Raphson algorithms. Even for samples of 50 groups, this method produces estimators of the fixed effects coefficients that are approximately unbiased and normally distributed. Two different methods, observed information and profile likelihood information, will be used to estimate the standard errors. This work is motivated by the goal of understanding the determinants of contraceptive use among Nepalese women in the Chitwan Valley Family Study (Axinn, Barber, and Ghimire, 1997). We utilize a two-level hazard model to examine how education and access to education for children covary with the initiation of permanent contraceptive use.

[1]  W. Axinn,et al.  Social Change, the Social Organization of Families, and Fertility Limitation1 , 2001, American Journal of Sociology.

[2]  D. Bates,et al.  Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data , 1988 .

[3]  R Crouchley,et al.  A comparison of frailty models for multivariate survival data. , 1995, Statistics in medicine.

[4]  D. Hedeker,et al.  A random-effects ordinal regression model for multilevel analysis. , 1994, Biometrics.

[5]  William G. Axinn,et al.  The Effects of Children's Schooling on Fertility Limitation , 1993 .

[6]  D. Hedeker,et al.  MIXOR: a computer program for mixed-effects ordinal regression analysis. , 1996, Computer methods and programs in biomedicine.

[7]  I G Kreft,et al.  The Effect of Different Forms of Centering in Hierarchical Linear Models. , 1995, Multivariate behavioral research.

[8]  Eric R. Ziegel,et al.  Multivariate Statistical Modelling Based on Generalized Linear Models , 2002, Technometrics.

[9]  K. Liang,et al.  Modelling Marginal Hazards in Multivariate Failure Time Data , 1993 .

[10]  Niels Keiding,et al.  Censoring, truncation and filtering in statistical models based on counting processes , 1988 .

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

[12]  W G Axinn,et al.  7. The Neighborhood History Calendar: A Data Collection Method Designed for Dynamic Multilevel Modeling , 1997, Sociological methodology.

[13]  Ronald A. Thisted,et al.  Elements of statistical computing , 1986 .

[14]  W. Axinn,et al.  Mass Education and Fertility Transition , 2001, American Sociological Review.

[15]  J. Klein,et al.  Statistical Models Based On Counting Process , 1994 .

[16]  F. Vaida,et al.  Proportional hazards model with random effects. , 2000, Statistics in medicine.

[17]  C. Mcgilchrist,et al.  Regression with frailty in survival analysis. , 1991, Biometrics.

[18]  D. Dey,et al.  Semiparametric Bayesian analysis of survival data , 1997 .

[19]  J P Klein,et al.  Semiparametric estimation of random effects using the Cox model based on the EM algorithm. , 1992, Biometrics.

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

[21]  Guang Guo,et al.  Estimating a Multivariate Proportional Hazards Model for Clustered Data Using the EM Algorithm, with an Application to Child Survival in Guatemala , 1992 .

[22]  A. W. van der Vaart,et al.  On Profile Likelihood , 2000 .

[23]  Richard D. Gill,et al.  A counting process approach to maximum likelihood estimation in frailty models , 1992 .

[24]  L. J. Wei,et al.  Regression analysis of multivariate incomplete failure time data by modeling marginal distributions , 1989 .

[25]  D.,et al.  Regression Models and Life-Tables , 2022 .

[26]  Susan A. Murphy,et al.  Semiparametric likelihood ratio inference , 1997 .

[27]  Laurence L. George,et al.  The Statistical Analysis of Failure Time Data , 2003, Technometrics.

[28]  D. Spiegelhalter,et al.  Modelling Complexity: Applications of Gibbs Sampling in Medicine , 1993 .

[29]  D J Sargent,et al.  A general framework for random effects survival analysis in the Cox proportional hazards setting. , 1998, Biometrics.

[30]  P Gustafson,et al.  Large hierarchical Bayesian analysis of multivariate survival data. , 1997, Biometrics.

[31]  N. Sastry A nested frailty model for survival data, with an application to the study of child survival in northeast Brazil. , 1997, Journal of the American Statistical Association.

[32]  Susan A. Murphy,et al.  6. Discrete-Time Multilevel Hazard Analysis , 2000 .

[33]  William G. Axinn,et al.  Innovations in life history calendar applications , 1999 .

[34]  Elja Arjas,et al.  A Marked Point Process Approach to Censored Failure Data with Complicated Covariates , 1984 .

[35]  Noreen Goldman,et al.  An assessment of estimation procedures for multilevel models with binary responses , 1995 .

[36]  McGilchrist Ca,et al.  Regression with frailty in survival analysis. , 1991 .

[37]  D. Alwin,et al.  The life history calendar: a technique for collecting retrospective data. , 1988, Sociological methodology.

[38]  D. Clayton A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence , 1978 .

[39]  S. Raudenbush,et al.  Maximum Likelihood for Generalized Linear Models with Nested Random Effects via High-Order, Multivariate Laplace Approximation , 2000 .

[40]  Philip Hougaard MODELLING HETEROGENEITY IN SURVIVAL DATA , 1991 .

[41]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[42]  R. Mauro Counting process , 2022 .

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

[44]  D. Oakes A Model for Association in Bivariate Survival Data , 1982 .

[45]  A. Yashin,et al.  Correlated individual frailty: an advantageous approach to survival analysis of bivariate data. , 1995, Mathematical population studies.