Partial‐Likelihood Analysis of Spatio‐Temporal Point‐Process Data

Summary We investigate the use of a partial likelihood for estimation of the parameters of interest in spatio‐temporal point‐process models. We identify an important distinction between spatially discrete and spatially continuous models. We focus our attention on the spatially continuous case, which has not previously been considered. We use an inhomogeneous Poisson process and an infectious disease process, for which maximum‐likelihood estimation is tractable, to assess the relative efficiency of partial versus full likelihood, and to illustrate the relative ease of implementation of the former. We apply the partial‐likelihood method to a study of the nesting pattern of common terns in the Ebro Delta Natural Park, Spain.     

Development and testing of a genetic marker‐based pedigree reconstruction system ‘PR‐genie’ incorporating size‐class data

For wildlife populations, it is often difficult to determine biological parameters that indicate breeding patterns and population mixing, but knowledge of these parameters is essential for effective management. A pedigree encodes the relationship between individuals and can provide insight into the dynamics of a population over its recent history. Here, we present a method for the reconstruction of pedigrees for wild populations of animals that live long enough to breed multiple times over their lifetime and that have complex or unknown generational structures. Reconstruction was based on microsatellite genotype data along with ancillary biological information: sex and observed body size class as an indicator of relative age of individuals within the population. Using body size‐class data to infer relative age has not been considered previously in wildlife genealogy and provides a marked improvement in accuracy of pedigree reconstruction. Body size‐class data are particularly useful for wild populations because it is much easier to collect noninvasively than absolute age data. This new pedigree reconstruction system, PR‐genie, performs reconstruction using maximum likelihood with optimization driven by the cross‐entropy method. We demonstrated pedigree reconstruction performance on simulated populations (comparing reconstructed pedigrees to known true pedigrees) over a wide range of population parameters and under assortative and intergenerational mating schema. Reconstruction accuracy increased with the presence of size‐class data and as the amount and quality of genetic data increased. We provide recommendations as to the amount and quality of data necessary to provide insight into detailed familial relationships in a wildlife population using this pedigree reconstruction technique.     

Nondetection sampling bias in marked presence‐only data

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Using expert knowledge to incorporate uncertainty in cause‐of‐death assignments for modeling of cause‐specific mortality

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause‐specific mortality provide an example of implicit use of expert knowledge when causes‐of‐death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause‐specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause‐of‐death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event‐time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause‐of‐death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause‐of‐death assignment in modeling of cause‐specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause‐specific survival data for white‐tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause‐of‐death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.     

A likelihood‐based approach for assessment of extra‐pair paternity and conspecific brood parasitism in natural populations

Genotypes are frequently used to assess alternative reproductive strategies such as extra‐pair paternity and conspecific brood parasitism in wild populations. However, such analyses are vulnerable to genotyping error or molecular artefacts that can bias results. For example, when using multilocus microsatellite data, a mismatch at a single locus, suggesting the offspring was not directly related to its putative parents, can occur quite commonly even when the offspring is truly related. Some recent studies have advocated an ad‐hoc rule that offspring must differ at more than one locus in order to conclude that they are not directly related. While this reduces the frequency with which true offspring are identified as not directly related young, it also introduces bias in the opposite direction, wherein not directly related young are categorized as true offspring. More importantly, it ignores the additional information on allele frequencies which would reduce overall bias. In this study, we present a novel technique for assessing extra‐pair paternity and conspecific brood parasitism using a likelihood‐based approach in a new version of program cervus . We test the suitability of the technique by applying it to a simulated data set and then present an example to demonstrate its influence on the estimation of alternative reproductive strategies.     

Analysis of interval‐censored recurrent event processes subject to resolution

Interval‐censored recurrent event data arise when the event of interest is not readily observed but the cumulative event count can be recorded at periodic assessment times. In some settings, chronic disease processes may resolve, and individuals will cease to be at risk of events at the time of disease resolution. We develop an expectation‐maximization algorithm for fitting a dynamic mover‐stayer model to interval‐censored recurrent event data under a Markov model with a piecewise‐constant baseline rate function given a latent process. The model is motivated by settings in which the event times and the resolution time of the disease process are unobserved. The likelihood and algorithm are shown to yield estimators with small empirical bias in simulation studies. Data are analyzed on the cumulative number of damaged joints in patients with psoriatic arthritis where individuals experience disease remission.     

A hybrid omnibus test for generalized semiparametric single‐index models with high‐dimensional covariate sets

Numerous statistical methods have been developed for analyzing high‐dimensional data. These methods often focus on variable selection approaches but are limited for the purpose of testing with high‐dimensional data. They are often required to have explicit‐likelihood functions. In this article, we propose a “hybrid omnibus test” for high‐dicmensional data testing purpose with much weaker requirements. Our hybrid omnibus test is developed under a semiparametric framework where a likelihood function is no longer necessary. Our test is a version of a frequentist‐Bayesian hybrid score‐type test for a generalized partially linear single‐index model, which has a link function being a function of a set of variables through a generalized partially linear single index. We propose an efficient score based on estimating equations, define local tests, and then construct our hybrid omnibus test using local tests. We compare our approach with an empirical‐likelihood ratio test and Bayesian inference based on Bayes factors, using simulation studies. Our simulation results suggest that our approach outperforms the others, in terms of type I error, power, and computational cost in both the low‐ and high‐dimensional cases. The advantage of our approach is demonstrated by applying it to genetic pathway data for type II diabetes mellitus.     

Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records

Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous‐time process potentially as a function of time‐varying covariates. We develop a continuous‐time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation‐maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates.     

Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application

In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high‐throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping‐by‐sequencing (GBS) methods include over a dozen reduced‐representation sequencing (RRS) approaches and at least four whole‐genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best‐suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker‐assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small‐ to moderate‐sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost‐effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost‐effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease.     

Multivariate statistical monitoring as applied to clean‐in‐place (CIP) and steam‐in‐place (SIP) operations in biopharmaceutical manufacturing

Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning‐in‐place (CIP) and steaming‐in‐place (SIP, also known as sterilization‐in‐place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real‐time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:505–515, 2014     

On Combining Family‐Based and Population‐Based Case–Control Data in Association Studies

Summary Combining data collected from different sources can potentially enhance statistical efficiency in estimating effects of environmental or genetic factors or gene–environment interactions. However, combining data across studies becomes complicated when data are collected under different study designs, such as family‐based and unrelated individual‐based case–control design. In this article, we describe likelihood‐based approaches that permit the joint estimation of covariate effects on disease risk under study designs that include cases, relatives of cases, and unrelated individuals. Our methods accommodate familial residual correlation and a variety of ascertainment schemes. Extensive simulation experiments demonstrate that the proposed methods for estimation and inference perform well in realistic settings. Efficiencies of different designs are contrasted in the simulation. We applied the methods to data from the Colorectal Cancer Family Registry.     

Living on the edge: life‐history of olive baboons at Gashaka‐Gumti National Park,Nigeria

Baboons are the most successful and ubiquitous African primates, renowned for their behavioral and reproductive flexibility, which enable them to inhabit a wide variety of habitat types. Owing to a number of long‐term field studies, comparative behavioral, developmental, demographic, and life‐history data are available from several populations, but study sites show a heavy bias toward South and East African savannahs, with little research in West or Central Africa. Life‐history data from such areas are important if we are fully to understand the nature of the environmental factors that limit baboon distribution. Here, we present demographic data for olive baboons at Gashaka‐Gumti National Park (GGNP), Nigeria, collected from December 2000–February 2006, and use these data to test comparative models of baboon life‐history. The GGNP habitat, which includes large areas of rainforest, is an environment in which baboons are little studied, and rainfall is much higher than at previous study sites. GGNP troop size data are presented from censuses, as well as life‐history data for two troops, one of which is within the park and wild‐feeding (Kwano troop), whereas the other dwells at the park edge, and supplements its diet by crop‐raiding (Gamgam troop). Troop sizes at GGNP are small compared with other field sites, but fit within previously suggested ranges for baboons under these climatic conditions. Inter‐birth intervals in Kwano troop were long compared with most studied populations, and values were not as predicted by comparative models. Consistent with known effects of food enhancement, Gamgam troop experienced shorter inter‐birth intervals and lower infant mortality than Kwano troop. We indicate some possible factors that exclude baboons from true rainforest, and suggest that the clearing of forests in Central and West Africa for agricultural land may allow baboons to extend their range into regions from which they are currently excluded. Am. J. Primatol. 71:293–304, 2009. © 2009 Wiley‐Liss, Inc.     

Life‐history strategy varies with the strength of competition in a food‐limited ungulate population

Fluctuating population density in stochastic environments can contribute to maintain life‐history variation within populations via density‐dependent selection. We used individual‐based data from a population of Soay sheep to examine variation in life‐history strategies at high and low population density. We incorporated life‐history trade‐offs among survival, reproduction and body mass growth into structured population models and found support for the prediction that different life‐history strategies are optimal at low and high population densities. Shorter generation times and lower asymptotic body mass were selected for in high‐density environments even though heavier individuals had higher probabilities to survive and reproduce. In contrast, greater asymptotic body mass and longer generation times were optimal at low population density. If populations fluctuate between high density when resources are scarce, and low densities when they are abundant, the variation in density will generate fluctuating selection for different life‐history strategies, that could act to maintain life‐history variation.     

Genotyping‐by‐sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias

There has been remarkably little attention to using the high resolution provided by genotyping‐by‐sequencing (i.e., RADseq and similar methods) for assessing relatedness in wildlife populations. A major hurdle is the genotyping error, especially allelic dropout, often found in this type of data that could lead to downward‐biased, yet precise, estimates of relatedness. Here, we assess the applicability of genotyping‐by‐sequencing for relatedness inferences given its relatively high genotyping error rate. Individuals of known relatedness were simulated under genotyping error, allelic dropout and missing data scenarios based on an empirical ddRAD data set, and their true relatedness was compared to that estimated by seven relatedness estimators. We found that an estimator chosen through such analyses can circumvent the influence of genotyping error, with the estimator of Ritland (Genetics Research, 67, 175) shown to be unaffected by allelic dropout and to be the most accurate when there is genotyping error. We also found that the choice of estimator should not rely solely on the strength of correlation between estimated and true relatedness as a strong correlation does not necessarily mean estimates are close to true relatedness. We also demonstrated how even a large SNP data set with genotyping error (allelic dropout or otherwise) or missing data still performs better than a perfectly genotyped microsatellite data set of tens of markers. The simulation‐based approach used here can be easily implemented by others on their own genotyping‐by‐sequencing data sets to confirm the most appropriate and powerful estimator for their data.     

Joint regression analysis for survival data in the presence of two sets of semi‐competing risks

In many clinical trials, multiple time‐to‐event endpoints including the primary endpoint (e.g., time to death) and secondary endpoints (e.g., progression‐related endpoints) are commonly used to determine treatment efficacy. These endpoints are often biologically related. This work is motivated by a study of bone marrow transplant (BMT) for leukemia patients, who may experience the acute graft‐versus‐host disease (GVHD), relapse of leukemia, and death after an allogeneic BMT. The acute GVHD is associated with the relapse free survival, and both the acute GVHD and relapse of leukemia are intermediate nonterminal events subject to dependent censoring by the informative terminal event death, but not vice versa, giving rise to survival data that are subject to two sets of semi‐competing risks. It is important to assess the impacts of prognostic factors on these three time‐to‐event endpoints. We propose a novel statistical approach that jointly models such data via a pair of copulas to account for multiple dependence structures, while the marginal distribution of each endpoint is formulated by a Cox proportional hazards model. We develop an estimation procedure based on pseudo‐likelihood and carry out simulation studies to examine the performance of the proposed method in finite samples. The practical utility of the proposed method is further illustrated with data from the motivating example.     

A comparison of abundance estimates from extended batch‐marking and Jolly–Seber‐type experiments

Little attention has been paid to the use of multi‐sample batch‐marking studies, as it is generally assumed that an individual's capture history is necessary for fully efficient estimates. However, recently, Huggins et al. ( 2010 ) present a pseudo‐likelihood for a multi‐sample batch‐marking study where they used estimating equations to solve for survival and capture probabilities and then derived abundance estimates using a Horvitz–Thompson‐type estimator. We have developed and maximized the likelihood for batch‐marking studies. We use data simulated from a Jolly–Seber‐type study and convert this to what would have been obtained from an extended batch‐marking study. We compare our abundance estimates obtained from the Crosbie–Manly–Arnason–Schwarz (CMAS) model with those of the extended batch‐marking model to determine the efficiency of collecting and analyzing batch‐marking data. We found that estimates of abundance were similar for all three estimators: CMAS, Huggins, and our likelihood. Gains are made when using unique identifiers and employing the CMAS model in terms of precision; however, the likelihood typically had lower mean square error than the pseudo‐likelihood method of Huggins et al. ( 2010 ). When faced with designing a batch‐marking study, researchers can be confident in obtaining unbiased abundance estimators. Furthermore, they can design studies in order to reduce mean square error by manipulating capture probabilities and sample size.     

On the occurrence of false positives in tests of migration under an isolation‐with‐migration model

The population genetic study of divergence is often carried out using a Bayesian genealogy sampler, like those implemented in ima2 and related programs, and these analyses frequently include a likelihood ratio test of the null hypothesis of no migration between populations. Cruickshank and Hahn (2014, Molecular Ecology, 23, 3133–3157) recently reported a high rate of false‐positive test results with ima2 for data simulated with small numbers of loci under models with no migration and recent splitting times. We confirm these findings and discover that they are caused by a failure of the assumptions underlying likelihood ratio tests that arises when using marginal likelihoods for a subset of model parameters. We also show that for small data sets, with little divergence between samples from two populations, an excellent fit can often be found by a model with a low migration rate and recent splitting time and a model with a high migration rate and a deep splitting time.     

INVESTIGATING EVOLUTIONARY TRADE‐OFFS IN WILD POPULATIONS OF ATLANTIC SALMON (SALMO SALAR): INCORPORATING DETECTION PROBABILITIES AND INDIVIDUAL HETEROGENEITY

Evolutionary trade‐offs among demographic parameters are important determinants of life‐history evolution. Investigating such trade‐offs under natural conditions has been limited by inappropriate analytical methods that fail to address the bias in demographic estimates that can result when issues of detection (uncertain detection of individual) are ignored. We propose a new statistical approach to quantify evolutionary trade‐offs in wild populations. Our method is based on a state‐space modeling framework that focuses on both the demographic process of interest as well as the observation process. As a case study, we used individual mark–recapture data for stream‐dwelling Atlantic salmon juveniles in the Scorff River (Southern Brittany, France). In freshwater, juveniles face two life‐history choices: migration to the ocean and sexual maturation (for males). Trade‐offs may appear with these life‐history choices and survival, because all are energy dependent. We found a cost of reproduction on survival for fish staying in freshwater and a survival advantage associated with the “decision” to migrate. Our modeling framework opens up promising prospects for the study of evolutionary trade‐offs when some life‐history traits are not, or only partially, observable.     

Application of high‐throughput mini‐bioreactor system for systematic scale‐down modeling,process characterization,and control strategy development

High‐throughput systems and processes have typically been targeted for process development and optimization in the bioprocessing industry. For process characterization, bench scale bioreactors have been the system of choice. Due to the need for performing different process conditions for multiple process parameters, the process characterization studies typically span several months and are considered time and resource intensive. In this study, we have shown the application of a high‐throughput mini‐bioreactor system viz. the Advanced Microscale Bioreactor (ambr15^TM), to perform process characterization in less than a month and develop an input control strategy. As a pre‐requisite to process characterization, a scale‐down model was first developed in the ambr system (15 mL) using statistical multivariate analysis techniques that showed comparability with both manufacturing scale (15,000 L) and bench scale (5 L). Volumetric sparge rates were matched between ambr and manufacturing scale, and the ambr process matched the pCO₂ profiles as well as several other process and product quality parameters. The scale‐down model was used to perform the process characterization DoE study and product quality results were generated. Upon comparison with DoE data from the bench scale bioreactors, similar effects of process parameters on process yield and product quality were identified between the two systems. We used the ambr data for setting action limits for the critical controlled parameters (CCPs), which were comparable to those from bench scale bioreactor data. In other words, the current work shows that the ambr15^TM system is capable of replacing the bench scale bioreactor system for routine process development and process characterization. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1623–1632, 2015