Springer Forecasting Product Liability Claims: Epidemiology and Modeling in the Manville Asbestos Case (Statistics for Biology and Health)

Product Description This selection of papers encompasses recent methodological advances in several important areas, such as multivariate failure time data and interval censored data, as well as innovative applications of the existing theory and methods. Using a rigorous account of statistical forecasting efforts that led to the successful resolution of the John-Manville asbestos litigation, the models in this volume can be adapted to forecast industry-wide asbestos liability. More generally, because the models are not overly dependent on the U.S. legal system and the role of asbestos, this volume will be of interest in other product liability cases, as well as similar forecasting situations for a range of insurable or compensational events. Throughout the text, the emphasis is on the iterative nature of model building and the uncertainty generated by lack of complete knowledge of the injury process. This uncertainty is balanced against the court's need for a definitive settlement, and how these opposing principles can be reconciled. A valuable reference for researchers and practitioners in the field of survival analysis. Review From the reviews:"This monograph shows then epidemiological prediction in action and is more eloquent than most textbooks as regards to methodology and difficulties encountered with field data.  It should interest far outside the circle of asbestos-related epidemiology because of its rigorous exposition, the relevance of its questions, the adequacy of the solutions offered, the discussion of its results, and its legal, financial, and human consequences." Mathematical Population Studies, 12:181-182, 2005"Over 750,000 claimants have filed suit … for illnesses and deaths related to exposure to asbestos, and at least 65 companies had been driven to bankruptcy; these numbers continue to grow. … This work led to substantial advances in the art of forecasting the number, timing, and nature of new claims. The authors present a lucid explanation of these advances and a description of how the matters in litigation have been settled. The presiding judge, Jack Weinstein, has contributed an informative preface … ." (J.C. Bailar, Short Book Reviews Publication of the International Statistical Institute, Vol. 25 (1), 2005)"Demographers Eric Stallard, Kenneth Manton and Joel Cohen use a risk assessment framework to estimate the numbers of claims expected during the period between 1990 and 2049 for asbestos-related disease among men exposed to … asbestos products. … Forecasting Product Liability Claims is notable for its illustration of the possibility of using epidemiologic and demographic methods to develop models for broad policy purposes. … It is formula-rich and dense in its description of data sources and the machinery of the models, as it should be." (Jonathan M. Samet, Science-AAAS, May, 2006)"This book summarizes the statistical models for projecting the number, timing, and nature of future claims, and it discusses how these predictions are used in developing a fair and equitable distribution of insufficient funds. … This book provides a fascinating account of how predication models are used to solve a very-real problem. It makes wonderful reading for statisticians interested in prediction problems, epidemiologists, actuaries, and lawyers involved in product liability suits and having to predict the number of possible litigants." (Johannes Ledolter, Zentralblatt MATH, Vol. 1099 (1), 2007) From the Back Cover  This volume presents a rigorous account of statistical forecasting efforts that led to the successful resolution of the Johns-Manville asbestos litigation. This case, taking 12 years to reach settlement, is expected to generate nearly 500,000 claims at a total nominal value of over $34 billion. The forecasting task, to project the number, timing, and nature of claims for asbestos-related injuries from a set of exposed persons of unknown size, is a general problem: the models in thi