Applied Survival Analysis: Regression Modeling of by David W. Hosmer

By David W. Hosmer

THE so much sensible, up to date advisor TO MODELLING AND examining TIME-TO-EVENT DATA—NOW IN A helpful NEW EDITION

considering that e-book of the 1st version approximately a decade in the past, analyses utilizing time-to-event tools have elevate significantly in all components of clinical inquiry in most cases due to model-building tools to be had in sleek statistical software program applications. although, there was minimum insurance within the on hand literature to9 advisor researchers, practitioners, and scholars who desire to follow those how to health-related components of analysis. utilized Survival research, moment version presents a finished and updated advent to regression modeling for time-to-event info in scientific, epidemiological, biostatistical, and different health-related study.

This e-book locations a distinct emphasis at the useful and modern functions of regression modeling instead of the mathematical idea. It bargains a transparent and obtainable presentation of recent modeling ideas supplemented with real-world examples and case reviews. Key themes lined comprise: variable choice, id of the size of constant covariates, the function of interactions within the version, overview of healthy and version assumptions, regression diagnostics, recurrent occasion types, frailty types, additive types, competing probability versions, and lacking information.

good points of the second one version comprise:

  • Expanded insurance of interactions and the covariate-adjusted survival services
  • The use of the Worchester middle assault learn because the major modeling info set for illustrating mentioned strategies and strategies
  • New dialogue of variable choice with multivariable fractional polynomials
  • Further exploration of time-varying covariates, advanced with examples
  • Additional therapy of the exponential, Weibull, and log-logistic parametric regression versions
  • Increased emphasis on reading and utilizing effects in addition to using a number of imputation ways to examine facts with lacking values
  • New examples and routines on the finish of every bankruptcy

Analyses during the textual content are played utilizing Stata® model nine, and an accompanying FTP website comprises the information units utilized in the ebook. utilized Survival research, moment variation is a perfect publication for graduate-level classes in biostatistics, data, and epidemiologic equipment. It additionally serves as a beneficial reference for practitioners and researchers in any health-related box or for execs in coverage and government.Content:
Chapter 1 creation to Regression Modeling of Survival info (pages 1–15):
Chapter 2 Descriptive tools for Survival info (pages 16–66):
Chapter three Regression types for Survival information (pages 67–91):
Chapter four Interpretation of a outfitted Proportional risks Regression version (pages 92–131):
Chapter five version improvement (pages 132–168):
Chapter 6 review of version Adequacy (pages 169–206):
Chapter 7 Extensions of the Proportional dangers version (pages 207–243):
Chapter eight Parametric Regression types (pages 244–285):
Chapter nine different versions and themes (pages 286–354):

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Additional resources for Applied Survival Analysis: Regression Modeling of Time-to-Event Data, Second Edition

Sample text

We begin by discussing confidence interval estimation of the survival function itself. Several different approaches may be taken to derive an estimator for the variance of the Kaplan-Meier estimator. We derive it from a technique that is often referred to as the delta method and is based on a first-order Taylor series expansion. This method is presented in general terms in Appendix 1. The Kaplan-Meier estimator at any time t may be viewed as a product of proportions. Rather than derive a variance estimator of this product, we derive one for its log because the variance of a sum is easier to calculate than the variance of a product.

3. 9 is statistically significant. A number of statistical tests have been proposed to answer this question, and most software packages provide results from at least two of these tests. However, comparison of the results obtained by different packages can become confusing due to small but annoying differences in terminology and methods used to calculate the tests. The original developers of these tests sought ways to extend tests used with non-censored data to the censored data setting (Gehan (1965), Mantel (1966), Breslovv (1970), Peto and Peto (1972)], Prentice ( 1978).

The Hall and Wellner limits may be obtained in the SAS version 9 software package. To assist users of other packages, we include a table of percentiles obtained from Hall and Wellner (1980) in Appendix 3. Given the tabled percentiles, confidence bands based on the estimated survival function itself, or its log-log transformation, are not difficult to calculate. Borgan and Leist0l (1990) show that the performance of the Hall and Wellner confidence bands is comparable for both functions (survival function and loglog transformation) and is adequate for samples as small as 25 with up to 50 percent censoring.

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