Abstract
In this paper we illustrate the so-called “indirect” method of inference, originally developed from the econometric literature, with analyses of three biological data sets involving longitudinal or repeated events data. This method is often more convenient computationally than maximum likelihood estimation when handling such model complexities as random effects and measurement error, for example; and it can also serve as a basis for robust inference with less stringent assumptions on the data generating mechanism.
The first data set involves times of recurrences of skin tumors in individual patients in a clinical trial. The methodology is applied in a regression analysis to accommodate random effects and covariate measurement error. The second data set concerns prevention of mammary tumors in rats and is analyzed using a Poisson regression model with overdispersion. The third application is to longitudinal data on epileptic seizures and analyzed using indirect inference based on low order moments.
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Jiang, W., Turnbull, B.W. (2004). Some Applications of Indirect Inference to Longitudinal and Repeated Events Data. In: Lin, D.Y., Heagerty, P.J. (eds) Proceedings of the Second Seattle Symposium in Biostatistics. Lecture Notes in Statistics, vol 179. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9076-1_6
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DOI: https://doi.org/10.1007/978-1-4419-9076-1_6
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