Modeling absolute differences in life expectancy with a censored skew-normal regression approach

PeerJ. 2015 Aug 6:3:e1162. doi: 10.7717/peerj.1162. eCollection 2015.


Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest.

Keywords: Censoring; Left-truncation; Life expectancy; Skew-normal regression; Survival regression.

Grants and funding

This work was supported by the Swiss National Science Foundation (grant nos. 3347CO-108806, 33CS30-134273 and 33CS30-148415). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.