A discrete-time multilevel mixture model for event history data with long-term survivors, with an application to an analysis of contraceptive sterilization in Bangladesh.
Event history models typically assume that the entire population is at risk of experiencing the event of interest throughout the observation period. However, there will often be individuals, referred to as long-term survivors, who may be considered a priori to have a zero hazard throughout the study period. In this paper, a discrete-time mixture model is proposed in which the probability of long-term survivorship and the timing of event occurrence are modelled jointly. Another feature of event history data that often needs to be considered is that they may come from a population with a hierarchical structure. For example, individuals may be nested within geographical regions and individuals in the same region may have similar risks of experiencing the event of interest due to unobserved regional characteristics. Thus, the discrete-time mixture model is extended to allow for clustering in the likelihood and timing of an event within regions. The model is further extended to allow for unobserved individual heterogeneity in the hazard of event occurrence. The proposed model is applied in an analysis of contraceptive sterilization in Bangladesh. The results show that a woman's religion and education level affect her probability of choosing sterilization, but not when she gets sterilized. There is also evidence of community-level variation in sterilization timing, but not in the probability of sterilization.
Bedford Group for Lifecourse and Statistical Studies, Institute of Education, University of London, 20 Bedford Way, London WC1H 0AL, UK. F.Steele@ioe.ac.uk
Fiona
F
Steele
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001459:Bangladesh / Q000453:epidemiology; D005260:Female; D005843:Geography; D006801:Humans; D015993:Life Tables; D008297:Male; D008875:Middle Aged; D011336:Probability; D016016:Proportional Hazards Models; D013029:Space-Time Clustering; D013245:Sterilization, Reproductive / Q000706:statistics & numerical data*; D017741:Survivors / Q000706:statistics & numerical data*