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Study of bootstrap estimates in Cox regression model with delayed entry. (English) Zbl 1302.62208

Summary: In most clinical studies, patients are observed for extended time periods to evaluate influences in treatment such as drug treatment, approaches to surgery, etc. The primary event in these studies is death, relapse, adverse drug reaction, or development of a new disease. The follow-up time may range from few weeks to many years. Although these studies are long term, the number of observed events is small. Longitudinal studies have increased the importance of statistical methods for time-to event data that can incorporate time-dependent covariates. The Cox proportional regression model is a widely used method. It is a statistical technique for exploring the relationship between the survival of a patient and several explanatory variables. We apply Cox regression models when right censoring and delayed entry survival data are considered. Y.-R. Su and J.-L. Wang [Ann. Stat. 40, No. 3, 1465–1488 (2012; Zbl 1257.62114)] stated that delayed entry produced biased sample. In the paper we present how re-sampling together with effect of delayed entry affect estimated parameters. The possibilities as well as limitations of this approach are demonstrated through the retrospective study of mitral valve replacement in children under 18 years.

MSC:

62N01 Censored data models
62N02 Estimation in survival analysis and censored data
62G09 Nonparametric statistical resampling methods
62P10 Applications of statistics to biology and medical sciences; meta analysis

Citations:

Zbl 1257.62114

Software:

SAS; SAS/STAT; bootstrap
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References:

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