Estimating Longevity Using Non Parametric and Semi Parametric Survival Functions
Downloads
The conventional models that are frequently used to summaries the general health of people in different nations are longevity and life expectancy. There are many other different methods of estimating life expectancy and these different methods give widely different answers. When selecting a method for estimating life expectancy, it is important to ensure that the method used is suitable for the data available and for the life history of the respondents. Although there is rarely only one correct method of summarizing demographic information, the problem of life table is that follow cohorts for long periods of time are not common, which prevents cohort analysis, and the critical assumption of a stable-age distribution so difficult to meet. Our derived longevity survival based models form parametric and non-parametric clearly demonstrated that the conventional life table and survival methods are clearly inconsistent and give misleading results. This study utilised University academic retirees data obtained from two premier Universities in Western Nigeria. The estimated mean life expectancy from life table model of Universities academic retirees is 18 years and estimated mean of post retirement years for Universities academic retirees from derived longevity using Kaplan Meier model is 22 years. Utilisation of explanatory variable from derived longevity using Cox proportional model estimated mean of post retirement years for universities academic retirees is 22 years. Based on standard error estimate we can say that life table model is inappropriate for estimating life expectancy.
Ajayi Moses Adedapo, Shangodoyin Dahud Kehinde, Thaga K and Mokgathle Lucky (2014): Estimation of Longevity using Survival Functions, International Journal of Sciences: Basic and Applied Research (IJSBAR) ISSN 2307-4531
Anousschka van der Meulen (2012): Life tables and Survival analysis Life tables, Statistics Netherlands.
Botman S, Moore T, Moriarity C, and Parsons V. (2000): Design and Estimation for the National Health Interview Survey, 1995-2004, Vital Health Stat 2(130).
Jerry A. Hausman (2005): Semi-parametric Methods for Survival and Longitudinal Data, Massachusetts Institute of Technology.
Joseph C. Gardiner (2010): Survival Analysis: Overview of Parametric, Nonparametric and Semi parametric approaches and New Developments Division of Biostatistics, Department of Epidemiology, Michigan State University, East Lansing, MI 48824.
Robert J. Wiese and Kevin Willis (2004): Calculation of Longevity and Life Expectancy in Captive Elephants TECHNICAL REPORT, Minnesota Zoological Gardens, Apple Valley, Minnesota
Shepard, Jon; Robert W. Greene (2003). Sociology and You. Ohio: Glencoe McGraw-Hill. pp. A–22. ISBN 0-07-828576-3.
Saunders Comprehensive Veterinary Dictionary, 3 ed. © 2007 Elsevier.
Preston, Samuel H.; Patrick Heuveline; Michel Guillot (2001). Demography: measuring and modeling population processes. Blackwell Publishers. ISBN 1-55786-214-1
Kate bull and David J. Spiegelhalter (1997): Tutorial in Biostatistics survival analysis in observational studies, Cardiothoracic unit, hospital for sick children, Great Ormond street, London wc1n 3jh, U.K.
John P. Klein and Melvin L. Moeschberger (2005): Survival Analysis: Techniques for Censored and Truncated, Spinger.
David W. Hosmer and Stanley Lemeshow (1999): Applied Survival Analysis, Regression Modeling of time to Event Data, John Wiley & Sons, INC.
Jiezhi Qi (2009): Comparison of Proportional Hazards and Accelerated Failure Time Models, University of Saskatchewan Saskatoon, Saskatchewan
USAID,(Measure and Evaluation): Overview of Life Tables and Survival Rates.
All Content should be original and unpublished.