Full Open Population Capture-Recapture Models with Individual Covariates

Traditional analyses of capture-recapture data are based on likelihood functions that explicitly integrate out all missing data. We use a complete data likelihood (CDL) to show how a wide range of capture-recapture models can be easily fitted using readily available software JAGS/BUGS even when there are individual-specific time-varying covariates. The models we describe extend those that condition on first capture to include abundance parameters, or parameters related to abundance, such as population size, birth rates or lifetime. The use of a CDL means that any missing data, including uncertain individual covariates, can be included in models without the need for customized likelihood functions. This approach also facilitates modeling processes of demographic interest rather than the complexities caused by non-ignorable missing data. We illustrate using two examples, (i) open population modeling in the presence of a censored time-varying individual covariate in a full robust-design, and (ii) full open population multi-state modeling in the presence of a partially observed categorical variable

Connecting the latent multinomial

Link et al. (2010) define a general framework for analyzing capture-recapture data with potential misidentifications. In this framework, the observed vector of counts, $y$, is considered as a linear function of a vector of latent counts, $x$, such that $y = A x$, with $x$ assumed to follow a multinomial distribution conditional on the model parameters, $\theta$. Bayesian methods are then applied by sampling from the joint posterior distribution of both $x$ and $\theta$. In particular, Link et al. (2010) propose a Metropolis-Hastings algorithm to sample from the full conditional distribution of $x$, where new proposals are generated by sequentially adding elements from a basis of the null space (kernel) of $A$. We consider this algorithm and show that using elements from a simple basis for the kernel of $A$ may not produce an irreducible Markov chain. Instead, we require a Markov basis, as defined by Diaconis and Sturmfels (1998). We illustrate the importance of Markov bases with three capture-recapture examples. We prove that a specific lattice basis is a Markov basis for a class of models including the original model considered by Link et al. (2010) and confirm that the specific basis used by Link et al. (2010) for their example with two sampling occasions is a Markov basis. The constructive nature of our proof provides an immediate method to obtain a Markov basis for any model in this class

Follow-up services for improving long-term outcomes in intensive care unit (ICU) survivors

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: Our main objective is to assess the effectiveness of follow-up services for ICU survivors that aim to identify and address unmet health needs related to the ICU period. We aim to assess the effectiveness in relation to health-related quality of life, mortality, depression and anxiety, post-traumatic stress disorder, physical function, cognitive function, ability to return to work or education and adverse events. Our secondary objectives are, in general, to examine both the various ways that follow-up services are provided and any major influencing factors. Specifically, we aim to explore: the effectiveness of service organisation (physician versus nurse led, face to face versus remote, timing of follow-up service); possible differences in services related to country (developed versus developing country); and whether participants had delirium within the ICU setting

Data Catalog Series for Space Science and Applications Flight Missions

The main purpose of the data catalog series is to provide descriptive references to data generated by space science flight missions. The data sets described include all of the actual holdings of the Space Science Data Center (NSSDC), all data sets for which direct contact information is available, and some data collections held and serviced by foreign investigators, NASA and other U.S. government agencies. This volume contains narrative descriptions of data sets from geostationary and high altitude scientific spacecraft and investigations. The following spacecraft series are included: Mariner, Pioneer, Pioneer Venus, Venera, Viking, Voyager, and Helios. Separate indexes to the planetary and interplanetary missions are also provided

Extending the Latent Multinomial Model with Complex Error Processes and Dynamic Markov Bases

The latent multinomial model (LMM) model of Link et al. (2010) provided a general framework for modelling mark-recapture data with potential errors in identification. Key to this approach was a Markov chain Monte Carlo (MCMC) scheme for sampling possible configurations of the counts true capture histories that could have generated the observed data. This MCMC algorithm used vectors from a basis for the kernel of the linear map between the true and observed counts to move between the possible configurations of the true data. Schofield and Bonner (2015) showed that a strict basis was sufficient for some models of the errors, including the model presented by Link et al. (2010), but a larger set called a Markov basis may be required for more complex models. We address two further challenges with this approach: 1) that models with more complex error mechanisms do not fit easily within the LMM and 2) that the Markov basis can be difficult or impossible to compute for even moderate sized studies. We address these issues by extending the LMM to separately model the capture/demographic process and the error process and by developing a new MCMC sampling scheme using dynamic Markov bases. Our work is motivated by a study of Queen snakes (Regina septemvittata) in Kentucky, USA, and we use simulation to compare the use of PIT tags, with perfect identification, and brands, which are prone to error, when estimating survival rates

Phylogenetics and sequence analysis--some problems for the unwary.

DNA sequence comparisons can provide deep insight into phylogenetic relationships, but can also present problems for the unwary. Alignment comparisons are not always as straightforward as they might seem, and comparative models applied to deduce relationships need to be carefully chosen with full regard to the assumptions on which they are based. Most importantly perhaps, genes are not organisms, so some sequence analyses can be poorly informative about relationships - especially if evolution of those organisms has involved significant epigenetic factors, for example, in controlling gene expression. This review highlights some of the most prevalent problems in sequence-based phylogenetic studies of parasite systems