Lay abstract for CJS paper: Estimation of the additive hazards model with interval‐censored data and missing covariates

Each week, we will be publishing lay abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from the Canadian Journal of Statistics, with the full article now available to read in early view here.

Li, H., Zhang, H., Zhu, L., Li, N. and Sun, J. (2020), Estimation of the additive hazards model with interval‐censored data and missing covariates. Can J Statistics, 48: 499-517. doi:10.1002/cjs.11544

This paper discusses regression analysis of time-to-event data or the determination of the relationship between a time-to-event variable and a group of predictors when the observed data are incomplete or the time-to-event variable can be only observed to belong to an interval. Furthermore, there may exist some missing values for the predictors, and the situation considered can often occur in many areas including demographical, epidemiological, financial, medical and sociological studies. To deal with the problem, the paper developed some statistical methods or tools by employing the so-called inverse probability weighting and reweighting techniques. Also the authors showed through both numerical and theoretical studies that the methods are valid and work well in reality. They provide important techniques to both statisticians and practitioners who face such problems and allow them to carry out efficient and valid analysis.