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Nonparametric Modeling of the Mean Survival Time in a Multi‐factor Design Based on Randomly Right‐Censored Data
Author(s) -
Rahbar M. H.,
Gardiner J. C.
Publication year - 2004
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200210048
Subject(s) - covariate , censoring (clinical trials) , estimator , statistics , nonparametric statistics , statistical inference , econometrics , accelerated failure time model , inference , standard error , mathematics , regression analysis , computer science , artificial intelligence
Statistical procedures and methodology for assessment of interventions or treatments based on medical data often involves complexities due to incompleteness of the available data as a result of drop out or the inability of complete follow up until the endpoint of interest. In this article we propose a nonparametric regression model based on censored data when we are concerned with investigation of the simultaneous effects of the two or more factors. Specifically, we will assess the effect of a treatment (dose) and a covariate (e.g., age categories) on the mean survival time of subjects assigned to combinations of the levels of these factors. The proposed method allows for varying levels of censorship in the outcome among different groups of subjects at different levels of the independent variables (factors). We derive the asymptotic distribution of the estimators of the parameters in our model, which then allows for statistical inference. Finally, through a simulation study we assess the effect of the censoring rates on the standard error of these types of estimators. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)