Effects of unmeasured heterogeneity in the linear transformation model for censored data |
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Authors: | Bin Zhang Yi Li Rebecca A Betensky |
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Institution: | (1) Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA |
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Abstract: | We investigate the effect of unobserved heterogeneity in the context of the linear transformation model for censored survival
data in the clinical trials setting. The unobserved heterogeneity is represented by a frailty term, with unknown distribution,
in the linear transformation model. The bias of the estimate under the assumption of no unobserved heterogeneity when it truly
is present is obtained. We also derive the asymptotic relative efficiency of the estimate of treatment effect under the incorrect
assumption of no unobserved heterogeneity. Additionally we investigate the loss of power for clinical trials that are designed
assuming the model without frailty when, in fact, the model with frailty is true. Numerical studies under a proportional odds
model show that the loss of efficiency and the loss of power can be substantial when the heterogeneity, as embodied by a frailty,
is ignored.
An erratum to this article can be found at |
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Keywords: | Omitted covariate Frailty |
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