Survival analysis is a highly active area of research with applications spanning the physical, engineering, biological, and social sciences. In addition to statisticians and biostatisticians, researchers in this area include epidemiologists, reliability engineers, demographers and economists. The economists survival analysis by the name of duration analysis and the analysis of transition data. We attempted to bring together leading researchers, with a common interest in developing methodology in survival analysis, at the NATO Advanced Research Workshop. The research works collected in this volume are based on the presentations at the Workshop. Analysis of survival experiments is complicated by issues of censoring, where only partial observation of an individual's life length is available and left truncation, where individuals enter the study group if their life lengths exceed a given threshold time. Application of the theory of counting processes to survival analysis, as developed by the Scandinavian School, has allowed for substantial advances in the procedures for analyzing such experiments. The increased use of computer intensive solutions to inference problems in survival analysis~ in both the classical and Bayesian settings, is also evident throughout the volume. Several areas of research have received special attention in the volume.

Klappentext
This volume includes papers (with discussions) by 26 of the leading researchers in these areas. Topics considered in the volume are the use of frailty models in survival analysis, left-censored and left-truncated data, multivariate survival analysis, competing risks theory, kernel density estimation and semiparametric estimation for right-censored data. Both Bayesian and classical viewpoints are studied. The techniques presented are applied to applications in reliability, fatigue crack growth, field reliability, clinical trials, genetics, gerontology, health monitoring, AIDS, economics and demography.

Zusammenfassung
Proceedings of the NATO Advanced Research Workshop on Survival Analysis and Related Topics, Columbus, Ohio, USA, June 23-28, 1991

Inhalt
Life Testing and Reliability Estimation Under Asymmetric Loss.- Bayesian Computations In Survival Models Via the Gibbs Sampler.- Bayesian Nonparametric Survival Analysis: A Comparison of the Kaplan-Meier and Berliner-Hill Estimators.- Modelling Time-Varying Hazards and Covariate Effects.- Analysis of Trials with Treatment Individual Interactions.- Assessment of Dependence In the Life Times of Twins.- Estimating Random Effects In the Framingham Heart Study.- Survival Analysis in Genetics: Danish Twin Data Applied To a Gerontological Question.- Some Issues in the Collection and Analysis of Field Reliability Data.- Bayesian Modelling For Fatigue Crack Curves.- Statistical Analysis Of a Weibull Process With Left- Censored Data.- Kernel Density Estimation from Record-Breaking Data.- Semiparametric Estimation Of Parametric Hazard Rates.- Cox-Type Regression Analysis for Large Numbers of Small Groups of Correlated Failure Time Observations.- Information Bounds For the Additive and Multiplicative Intensity Models.- Survival Analysis For Left Censored Data.- Regression Analysis for Discrete and Continuous Truncated and Eventually Censored Data.- Independent Delayed Entry.- Periodic Inspections in a Longitudinal Study: Viewing Occult Tumors Through a Filter.- Survival Under Multiple Time Scales in Dynamic Environments.- Nonparametric Identifiability of Marginal Survival Distributions in the Presence of Dependent Competing Risks and a Prognostic Covariate.- Frailty Models For Multiple Event Times.- A Nonparametric Approach To Dependence For Bivariate Censored Data.- Marginal and Conditional Models for the Analysis of Multivariate Failure Time Data.- Multivariate Failure Time Analysis: Discussion of Papes by Oakes; Pons, Kaddour and De Turckheim; and Prentice and Cai.- SurvivorFunctions as Dependent Variables In Demographic Analysis.- Relation Between The Rate Of Return To Tenure, Earnings Growth and Job Switching.- List of Contributors.