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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280216648356