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Applied Survival Analysis: Regression Modeling of Time to Event Data by David W. Hosmer, Stanley Lemeshow

Applied Survival Analysis: Regression Modeling of Time to Event Data David W. Hosmer, Stanley Lemeshow ebook
Format: djvu
Page: 400
Publisher: Wiley-Interscience
ISBN: 0471154105, 9780471154105

Regression Modeling of Time to Event Data, New York: Wiley & Sons. Zhang Y., Chen M.-H., Ibrahim J.G., Zeng D., Chen Q., Pan Z, and Xue X. Survival time was measured from the date of surgery to the date of event or last follow-up. Demographic Applications of Event History Analysis, Oxford: Clarendon Press. Patients alive at the end of the study were censored for the purpose of data analysis. * Co-first author; ^ corresponding author. (1999) Applied Survival Analysis. (2013) ``Bayesian Semi-Competing Risks Frailty Models for Survival data with Treatment Switching''. In an analysis of individuals' health inequality based on mortality, Gakidou [12] proposed a measure of total health inequality derived from the beta-binomial regression model, which unified treatment of various measures including the Gini coefficient [13] and other estimates of inequalities. Chen Q., Zeng D., Ibrahim J.G., Akacha M., and Schmidli H., (2013) "Estimating Time-varying Effects for Overdispersed Recurrent Events Data with Treatment Statistics in Medicine. Time to event analyses (aka, Survival Analysis and Event History Analysis) are used often within medical, sales and epidemiological research. Multilevel survival models are flexible and efficient tools in studying health inequalities of life expectancy or survival time data with a geographic structure of more than 2 levels.