Dependence estimation for marginal models of multivariate survival data.
We have previously (Segal and Neuhaus, 1993) devised methods for obtaining marginal regression coefficients and associated variance estimates for multivariate survival data, using a synthesis of the Poisson regression formulation for univariate censored survival analysis and generalized estimating equations (GEE's). The method is parametric in that a baseline survival distribution is specified. Analogous semiparametric models, with unspecified baseline survival, have also been developed (Wei, Lin and Weissfeld, 1989; Lin, 1994). Common to both these approaches is the provision of robust variances for the regression parameters. However, none of this work has addressed the more difficult area of dependence estimation. While GEE approaches ostensibly provide such estimates, we show that there are problems adopting these with multivariate survival data. Further, we demonstrate that these problems can affect estimation of the regression coefficients themselves. An alternate, ad hoc approach to dependence estimation, based on design effects, is proposed and evaluated via simulation and illustrative examples.
Segal
M R
MR
Division of Biostatistics, University of California, San Francisco 94143-0560, USA. mark@biostat.ucsf.edu
Neuhaus
J M
JM
James
I R
IR
D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D015993:Life Tables; D016013:Likelihood Functions; D015999:Multivariate Analysis; D016012:Poisson Distribution; D016019:Survival Analysis