Integrated survival analysis using an event-time approach in a Bayesian framework

Event-time or continuous-time statistical approaches have been applied throughout the bio-statistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. this has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed in integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. this provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest \u3c5 days old and were lower for chicks with larger birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data

Genetic susceptibility to chronic wasting disease in free-ranging white-tailed deer: Complement component C1q and Prnp polymorphisms

The genetic basis of susceptibility to chronic wasting disease (CWD) in free-ranging cervids is of great interest. Association studies of disease susceptibility in free-ranging populations, however, face considerable challenges including: the need for large sample sizes when disease is rare, animals of unknown pedigree create a risk of spurious results due to population admixture, and the inability to control disease exposure or dose. We used an innovative matched case–control design and conditional logistic regression to evaluate associations between polymorphisms of complement C1q and prion protein (Prnp) genes and CWD infection in white-tailed deer from the CWD endemic area in southcentral Wisconsin. To reduce problems due to admixture or disease-risk confounding, we used neutral genetic (microsatellite) data to identify closely related CWD-positive (n = 68) and CWD-negative (n = 91) female deer to serve as matched cases and controls. Cases and controls were also matched on factors (sex, location, age) previously demonstrated to affect CWD infection risk. For Prnp, deer with at least one Serine (S) at amino acid 96 were significantly less likely to be CWD-positive relative to deer homozygous for Glycine (G). This is the first characterization of genes associated with the complement system in white-tailed deer. No tests for association between any C1q polymorphism and CWD infection were significant at p \u3c 0.05. After controlling for Prnp, we found weak support for an elevated risk of CWD infection in deer with at least one Glycine (G) at amino acid 56 of the C1qC gene. While we documented numerous amino acid polymorphisms in C1q genes none appear to be strongly associated with CWD susceptibility

Modeling Routes of Chronic Wasting Disease Transmission: Environmental Prion Persistence Promotes Deer Population Decline and Extinction

Chronic wasting disease (CWD) is a fatal disease of deer, elk, and moose transmitted through direct, animal-to-animal contact, and indirectly, via environmental contamination. Considerable attention has been paid to modeling direct transmission, but despite the fact that CWD prions can remain infectious in the environment for years, relatively little information exists about the potential effects of indirect transmission on CWD dynamics. In the present study, we use simulation models to demonstrate how indirect transmission and the duration of environmental prion persistence may affect epidemics of CWD and populations of North American deer. Existing data from Colorado, Wyoming, and Wisconsin's CWD epidemics were used to define plausible short-term outcomes and associated parameter spaces. Resulting long-term outcomes range from relatively low disease prevalence and limited host-population decline to host-population collapse and extinction. Our models suggest that disease prevalence and the severity of population decline is driven by the duration that prions remain infectious in the environment. Despite relatively low epidemic growth rates, the basic reproductive number, R0, may be much larger than expected under the direct-transmission paradigm because the infectious period can vastly exceed the host's life span. High prion persistence is expected to lead to an increasing environmental pool of prions during the early phases (i.e. approximately during the first 50 years) of the epidemic. As a consequence, over this period of time, disease dynamics will become more heavily influenced by indirect transmission, which may explain some of the observed regional differences in age and sex-specific disease patterns. This suggests management interventions, such as culling or vaccination, will become increasingly less effective as CWD epidemics progress

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Appendix A. State probability solutions for the three-state irreversible disease model.

State probability solutions for the three-state irreversible disease model