Titlebook: Survival Analysis; Techniques for Censo John P. Klein,Melvin L. Moeschberger Textbook 19971st edition Springer Science+Business Media New Y

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Estimation of Basic Quantities for Other Sampling Schemes,ection 4.6. In this chapter, we discuss how to estimate the survival function for other sampling schemes, namely, left, double, and interval censoring, right-truncation, and grouped data. Each sampling scheme provides different information about the survival function and requires a different techniq

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Refinements of the Semiparametric Proportional Hazards Model, start of the study whose values are fixed throughout the course of the study. For instance, in Example 8.4, where acute leukemia patients were given a bone marrow transplant, we considered the three risk groups, donor age, recipient age, and several other variables, as fixed-time covariates. The ba

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Additive Hazards Regression Models,riates was to act multiplicatively on some unknown baseline hazard rate. Covariates which do not act on the baseline hazard rate in this fashion were modeled either by the inclusion of a time-dependent covariate or by stratification.

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Regression Diagnostics,regression model was presented. In these chapters, the focus was on estimating and testing covariate effects assuming that the model was correctly chosen. Only limited checks of the assumptions were made. A check of the proportional hazards assumption in the Cox model with a constructed time-depende

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Inference for Parametric Regression Models,ta which do not require any specific distributional assumptions about the shape of the survival function. In this chapter, we shall discuss the use of parametric models for estimating univariate survival and for the censored-data regression problem. When these parametric models provide a good fit to

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Multivariate Survival Analysis, based on the assumption that the survival times of distinct individuals are independent of each other. Although this assumption may be valid in many experimental settings, it may be suspect in others. For example, we may be making inferences about survival in a sample of siblings or litter mates wh

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Censoring and Truncation,both left and right censoring. Each type will lead to a different likelihood function which will be the basis for the inference. As we shall see in section 3.5, though the likelihood function is unique for each type of censoring, there is a common approach to be used in con tructing it.