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Applied Survival Analysis: Regression Modeling of Time to Event Data David W. Hosmer, Stanley Lemeshow
Publisher: Wiley-Interscience

Weibull proportional hazards regression was used to estimate the risk of .. Survival analysis, also identified as event history evaluation, is a class of statistical methods for studying the occurrence and timing survival data have two attributes that are challenging to handle with other statistical methods: censoring and time-dependent covariates. 14 Growth in Cross-Sectional Area of Surviving Trees. Applied survival analysis : regression modeling of time-to-event data R853 .S7 H67 2008. (2013) Towards Renewed Health Economic Simulation of Type 2 Diabetes: Risk Equations for First and Second Cardiovascular Events from Swedish Register Data. 8 Severity and Consequences of Damage. Hosmer DW, Lemeshow S (1999) Applied Survival Analysis. The Prentice, Williams, and Peterson gap time model [26 ] was applied to estimate the hazard ratios of first and second CVD events in separate equations. The study of events involving an element of time has a long and important history in statistical study and practice. 8 Incidence and Reoccurrence of Damage. Applied survival Evaluation: Regression Modeling of Time to Occasion Information. 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). Survival time was measured from the date of surgery to the date of event or last follow-up. Patients alive at the end of the study were censored for the purpose of data analysis.