Pergamon zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Computers Math. Applic. zyxwvutsrqponmlkjihgfedcbaZYXWV Vol. 25, No. 7, pp. 89-107, 1997 Copyright@1997 Elsevier Science Ltd Printed in Great Britain. All rights reserved PII: s0895-7177(97)00051-4 0898-1221/97 $17.00 $- 0.00 zyxwvut Analyses of Cohort Mortality Incorporating Observed and Unobserved Risk Factors K. G. MANTON* Center for Demographic Studies, Duke University 2117 Campus Drive, Box 90408, Durham, NC 27706-0408, U.S.A. G. LOWRIMORE Center for Demographic Studies, Duke University 2117 Campus Drive, Durham, NC 27706, U.S.A. A. YASHIN Odense University, Institute of Community Health J.B. Winslows Vej 17, DK-5000 Odense, Denmark H. D. TOLLEY Department of Statistics, Brigham Young University Rm. 226 TMCB, Provo, UT 84602, U.S.A. (Received M ay 1996; accepted July 1996) Abstract-Interventions to prevent disease and increase life expectancy are most effectively de- veloped from data on pathways to disease and death. Unfortunately, most national data sets separate end-state information-i.e., causespecific mortality-from pathway data describing how specific dis- eases result from environmental and behavioral processes. Thus, a coherent empirical picture of routes to death from a diversity of causes requires a data combining and modelling strategy that, of necessity, incorporates theory and prior-knowledge-based assumptions together with sensitivity anal- yses to assess the stability of conclusions. In this paper, a general data combining statistical strategy is presented and illustrated for smoking behavior and lung cancer mortality. Specifically, National Health Interview Survey data on smoking is combined with U.S. vital statistics data 1950 to 1987 to analyze the joint distribution of total and lung cancer mortality. Parameters were estimated for mortality, smoking cessation processes, and for individual risk heterogeneity for nine U.S. white male and female cohorts aged 30 to 70 in 1950 and followed until 1987. Keywords-Gompertz hazard, Weibull hazard, Dubey distribution, Cohort mortality, Smoking cessation. zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA 1. INTRODUCTION Mortality data is useful in making national estimates of the effects of health interventions for preventing disease and increasing life expectancy. In particular, U.S. mortality data has the ad- vantages that the number of deaths is large, data on all causes of death are reported, data on individual deaths is available back to 1950, and all ages, population groups, and geographic areas are represented. However, the effects of health interventions can be better assessed when infor- mation on the pathways to disease, and then to death, are used. Unfortunately, extant national *Author to whom all correspondence should be addressed. This research was supported by NIA Grant AG01159, AG07025, and NIH/NIA Grant PO1 AG08791-01. The authors thank anonymous referees for valuable comments. Typeset by d&-T@ 89