A class of mixed models for recurrent event data. (English. French summary) Zbl 1228.62127
Summary: We propose a class of mixed models for recurrent event data. The new models include the proportional rates model and Box-Cox transformation rates models as special cases, and allow the effects of covariates on the rate functions of counting processes to be proportional or convergent. For inference on the model parameters, estimating equation approaches are developed. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed procedure is evaluated through simulation studies. A real example with data taken from a clinic study on chronic granulomatous disease (CGD) is also illustrated for the use of the proposed methodology.
MSC:
| 62N02 | Estimation in survival analysis and censored data |
| 62G20 | Asymptotic properties of nonparametric inference |
| 65C60 | Computational problems in statistics (MSC2010) |
| 62N01 | Censored data models |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
Keywords:
counting process; marginal rate model; mixed model; partial-score function; proportional and convergent effectsSoftware:
invGaussReferences:
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