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

14 Growth in Cross-Sectional Area of Surviving Trees. Child Development, 69, 979-990. New York: Springer-Verlag; 2000:74. Our analysis of survival time used an “accelerated failure-time regression model” to quantify the effect of independent variables on the distribution of survival times (Allison 1995, Hosmer and Lemeshow 1999). Hosmer DW, Lemeshow S: Applied Survival Analysis: Regression Modeling of Time to Event Data. The standard multiple linear regression model is not well suited to survival data for several reasons; among these are (i) survival times are typically not normally distributed, and (ii) censored data is commonplace, resulting in missing values for the Early attempts to circumvent these problems involved applying the log transform to survival time, but this worked well only when censoring was present in a very small percentage of the observations (Everitt and Rabe-Hesketh, [2]). Applied Turbulence Modelling in Marine Waters. Thus, one can estimate the effect of the G-E interaction term approximately correctly without performing a logistic regression of D. Applied survival analysis: Regression modeling of time to event data. Therneau TM, Grambsch PM: Modeling Survival Data: Extending the Cox Model. 8 Severity and Consequences of Damage. 8 Incidence and Reoccurrence of Damage. Applying Generalized Linear Models. This approach can also be applied in logistic models in the presence of covariates [39]. Intention and knowledge in preschoolers' conception of pretend. Applied Survival Analysis: Regression Modeling of Time to Event Data.