Survival analysis with delayed entry in selected families with application to human longevity

• Published in: Statistical methods in medical research 2018-03, Vol.27 (3), p.933-954
• Main Authors: Rodríguez-Girondo, Mar, Deelen, Joris, Slagboom, Eline P, Houwing-Duistermaat, Jeanine J
• Format: Article
• Language: English
• Subjects: Aged, 80 and over; Aging; Apolipoprotein E2 - genetics; Apolipoprotein E4 - genetics; Bias; Biostatistics - methods; Cohort Studies; Computer Simulation; Delayed; Family; Female; Genome-Wide Association Study - statistics & numerical data; Humans; Likelihood Functions; Longevity; Longevity - genetics; Male; Models, Statistical; Monte Carlo Method; Netherlands; Probability; Relatives; Sampling; Siblings; Software; Survival; Survival Analysis; Weighting
• ISSN: 0962-2802; 1477-0334
• DOI: 10.1177/0962280216648356

In the field of aging research, family-based sampling study designs are commonly used to study the lifespans of long-lived family members. However, the specific sampling procedure should be carefully taken into account in order to avoid biases. This work is motivated by the Leiden Longevity Study, a family-based cohort of long-lived siblings. Families were invited to participate in the study if at least two siblings were ‘long-lived’, where ‘long-lived’ meant being older than 89 years for men or older than 91 years for women. As a result, more than 400 families were included in the study and followed for around 10 years. For estimation of marker-specific survival probabilities and correlations among life times of family members, delayed entry due to outcome-dependent sampling mechanisms has to be taken into account. We consider shared frailty models to model left-truncated correlated survival data. The treatment of left truncation in shared frailty models is still an open issue and the literature on this topic is scarce. We show that the current approaches provide, in general, biased estimates and we propose a new method to tackle this selection problem by applying a correction on the likelihood estimation by means of inverse probability weighting at the family level.