Premium
Multivariate Parametric Random Effect Regression Models for Fecundability Studies
Author(s) -
Ecochard René,
Clayton David G.
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.01023.x
Subject(s) - multivariate statistics , covariate , generalization , context (archaeology) , statistics , mathematics , marginal distribution , biometrics , parametric statistics , proportional hazards model , generalized linear model , econometrics , marginal model , event (particle physics) , regression , regression analysis , random variable , computer science , artificial intelligence , mathematical analysis , paleontology , physics , quantum mechanics , biology
Summary. Delay until conception is generally described by a mixture of geometric distributions. Weinberg and Gladen (1986, Biometrics 42 , 547–560) proposed a regression generalization of the beta‐geometric mixture model where covariates effects were expressed in terms of contrasts of marginal hazards. Scheike and Jensen (1997, Biometrics 53 , 318–329) developed a frailty model for discrete event times data based on discrete‐time analogues of Hougaard's results (1984, Biometrika 71 , 75–83). This paper is on a generalization to a three‐parameter family distribution and an extension to multivariate cases. The model allows the introduction of explanatory variables, including time‐dependent variables at the subject‐specific level, together with a choice from a flexible family of random effect distributions. This makes it possible, in the context of medically assisted conception, to include data sources with multiple pregnancies (or attempts at pregnancy) per couple.