TY - JOUR
T1 - Accelerated failure time models provide a useful statistical framework for aging research
AU - Swindell, William R.
N1 - Funding Information:
This work was supported by NIA training Grant T32-AG000114 and the University of Michigan Department of Pathology. Two anonymous reviewers provided helpful comments and suggestions for this paper. Additionally, the author thanks a number of researchers that contributed the experimental survivorship data analyzed in this report, including Andrzej Bartke, Michael S. Bonkowski, Cheryl A. Conover, Bruno Conti, Marco Giorgio, Siegfried Hekimi, Makoto Kuro-o, Richard A. Miller, Piegiuseppe Pelicci, Peter S. Rabinovitch, Qitao Ran, Arlan Richardson and Junji Yodoi. Appendix A
PY - 2009/3
Y1 - 2009/3
N2 - Survivorship experiments play a central role in aging research and are performed to evaluate whether interventions alter the rate of aging and increase lifespan. The accelerated failure time (AFT) model is seldom used to analyze survivorship data, but offers a potentially useful statistical approach that is based upon the survival curve rather than the hazard function. In this study, AFT models were used to analyze data from 16 survivorship experiments that evaluated the effects of one or more genetic manipulations on mouse lifespan. Most genetic manipulations were found to have a multiplicative effect on survivorship that is independent of age and well-characterized by the AFT model "deceleration factor". AFT model deceleration factors also provided a more intuitive measure of treatment effect than the hazard ratio, and were robust to departures from modeling assumptions. Age-dependent treatment effects, when present, were investigated using quantile regression modeling. These results provide an informative and quantitative summary of survivorship data associated with currently known long-lived mouse models. In addition, from the standpoint of aging research, these statistical approaches have appealing properties and provide valuable tools for the analysis of survivorship data.
AB - Survivorship experiments play a central role in aging research and are performed to evaluate whether interventions alter the rate of aging and increase lifespan. The accelerated failure time (AFT) model is seldom used to analyze survivorship data, but offers a potentially useful statistical approach that is based upon the survival curve rather than the hazard function. In this study, AFT models were used to analyze data from 16 survivorship experiments that evaluated the effects of one or more genetic manipulations on mouse lifespan. Most genetic manipulations were found to have a multiplicative effect on survivorship that is independent of age and well-characterized by the AFT model "deceleration factor". AFT model deceleration factors also provided a more intuitive measure of treatment effect than the hazard ratio, and were robust to departures from modeling assumptions. Age-dependent treatment effects, when present, were investigated using quantile regression modeling. These results provide an informative and quantitative summary of survivorship data associated with currently known long-lived mouse models. In addition, from the standpoint of aging research, these statistical approaches have appealing properties and provide valuable tools for the analysis of survivorship data.
KW - AFT model
KW - Cox
KW - Insulin-like growth factor
KW - Proportional hazard
KW - Survival analysis
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U2 - 10.1016/j.exger.2008.10.005
DO - 10.1016/j.exger.2008.10.005
M3 - Article
C2 - 19007875
AN - SCOPUS:59749084724
SN - 0531-5565
VL - 44
SP - 190
EP - 200
JO - Experimental Gerontology
JF - Experimental Gerontology
IS - 3
ER -