Abstract
The partial likelihood theory for survival data, introduced in Chap. 5, allows one to model survival times while accommodating covariate information. An important caveat to this theory is that the values of the covariates must be determined at time t = 0, when the patient enters the study, and remain constant thereafter. This issue arises with survival data because such data evolve over time, and it would be improper to use the value a covariate to model survival information that is observed before the covariate’s value is known. To accommodate covariates that may change their value over time (“time dependent covariates”) , special measures are necessary to obtain valid parameter estimates. An intervention that occurs after the start of the trial, or a covariate (such as air pollution exposure) that changes values over the course of the study are two examples of time dependent variables.
The original version of this chapter was revised. An erratum to this chapter can be found at DOI 10.1007/978-3-319-31245-3_13
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-31245-3_13
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Moore, D.F. (2016). Time Dependent Covariates. In: Applied Survival Analysis Using R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-31245-3_8
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DOI: https://doi.org/10.1007/978-3-319-31245-3_8
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