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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
ISBN: 0471154105, 9780471154105
Page: 400
Publisher: Wiley-Interscience
Format: djvu

Multivariate Cox regression analysis was used to explore the association between breast milk expression and the duration of any breastfeeding. In this paper Survival curve of duration of any breastfeeding by mothers who expressed breast milk. Survival analysis involves time-dependent outcomes or events. Survival analysis: A self-learning text (2nd ed.). Weibull proportional hazard regression models were developed for interval-censored survival data, since the precise date of developing diabetes could not be determined and the TLGS records provided only an interval for each diabetes diagnosis. Another predictive modeling technique, logistic regression, can be used to predict if an event will occur, but not when. Generalized estimating equations model, individual growth model, multilevel model, hierarchical linear model, random regression model, survival analysis, event history analysis, failure time analysis, and hazard model. Hosmer DW, Lemeshow S, May S: Applied survival analysis: regression modeling of time-to-event data. Applied survival analysis: Regression modeling of time to event data. Cox-regression was applied to model the time-to-event data and to determine which variable(s) had an effect on the duration of breastfeeding, for those mothers who were breastfeeding at the time of discharge from hospital. How is this useful for a social business? Using accelerated failure time (AFT) survival regression analyses.