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Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi-level) structures of fixed and/or random effects. Yet, our experience suggests that most ecologists are not familiar with subtle but important problems that often arise with such models and with their implementation in popular software. Of foremost consideration for us is the notion of effect identifiability, which generally concerns how well data, models, or implementation approaches inform about, i.e., identify, quantities of interest. In this paper, we focus on implementation pitfalls that potentially misinform subsequent inference, despite otherwise informative data and models. We illustrate the aforementioned issues using random effects regressions on synthetic data. We show how to diagnose identifiability issues and how to remediate these issues with model reparameterization and computational and/or coding practices in popular software, with a focus on JAGS, OpenBUGS, and Stan. We also show how these solutions can be extended to more complex models involving multiple groups of nested, crossed, additive, or multiplicative effects, for models involving random and/or fixed effects. Finally, we provide example code (JAGS/OpenBUGS and Stan) that practitioners can modify and use for their own applications.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple (>40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.

A statistical approach for the characterization of Additive Manufacturing (AM) processes is presented in this paper. Design of Experiments (DOE) and ANalysis of VAriance (ANOVA), both based on Nested Effects Modeling (NEM) technique, are adopted to assess the effect of different laser exposure strategies on physical and mechanical properties of AlSi10Mg parts produced by Direct Metal Laser Sintering (DMLS). Due to the wide industrial interest in AM technologies in many different fields, it is extremely important to ensure high parts performances and productivity. For this aim, the present paper focuses on the evaluation of tensile properties of specimens built with different laser exposure strategies. Two optimal laser parameters settings, in terms of both process quality (part performances) and productivity (part build rate), are identified.

Technological advancements in the field of mobile devices and wearable sensors have helped overcome obstacles in the delivery of care, making it possible to deliver behavioral treatments anytime and anywhere. Here, we discuss our work on the design of a mobile health smoking cessation intervention study with the goal of assessing whether reminders, delivered at times of stress, result in a reduction/prevention of stress in the near-term and whether this effect changes with time in study. Multiple statistical challenges arose in this effort, leading to the development of the stratified micro-randomized trial design. In these designs each individual is randomized to treatment repeatedly at times determined by predictions of risk. These risk times may be impacted by prior treatment. We describe the statistical challenges and detail how they can be met.

Background Despite the implementation of a set of social and health policies, Brazil has experienced a slowdown in the decline of infant mortality, regional disparities and persistent high death levels, raising questions about the determinants of infant mortality after the implementation of these policies. The objective of this article is to propose a methodological approach aiming at identifying the determinants of infant mortality in Brazil after the implementation of those policies. Method A series of multilevel panel data with fixed effect nested within-clusters were conducted supported by the concept of health capabilities based on data from 26 Brazilian states between 2004 and 2015. The dependent variables were the neonatal, the infant and the under-five mortality rates. The independent variables were the employment rate, per capita income, Bolsa Família Program coverage, the fertility rate, educational attainment, the number of live births by prenatal visits, the number of health professionals per thousand inhabitants, and the access to water supply and sewage services. We also used different time lags of employment rate to identify the impact of employment on the infant mortality rates over time, and household income stratified by minimum wages to analyze their effects on these rates. Results The results showed that in addition to variables associated with infant mortality in previous studies, such as Bolsa Família Program, per capita income and fertility rate, other factors affect child mortality. Educational attainment, quality of prenatal care and access to health professionals are also elements impacting infant deaths. The results also identified an association between employment rate and different infant mortality rates, with employment impacting neonatal mortality up to 3 years and that a family income below 2 minimum wages increases the odds of infant deaths. Conclusion The results proved that the methodology proposed allowed the use of variables based on aggregated data that could hardly be used by other methodologies.

1. Fecundity estimates for demographic modelling are difficult to acquire at the regional spatial scales that correspond to climate shifts, land use impacts or habitat management programmes. Yet they are important for evaluating such effects. While waterfowl managers have historically used harvest-based age ratios to assess fecundity at continental scales, widely available age ratios from late summer banding (ringing) data present an underutilized opportunity to examine a regional fecundity index with broad temporal replication. 2. We used age ratios from banding data and hierarchical mixed-effect models to examine how fecundity of five North American dabbling duck species was affected by temporal variation in hydrological cycles, intra- and interspecific density dependence and alternate prey availability, and whether those relationships were consistent across a broad geographic area. 3. Model-estimated fecundity was within the range of traditional harvest-based fecundity estimates for each species. Ecological covariates explained between 16% and 53% of the temporal variation in fecundity, dependent on species. Increasing wetland inundation and an indicator of vole population irruptions were consistent predictors of increasing fecundity across all species. Species exhibited mixed positive and negative responses to interspecific and intraspecific breeding pair densities hypothesized to affect nest and brood survival respectively, highlighting the importance of integrating brood survival into fecundity metrics for precocial species. 4. Declines in fecundity over time and across space at more northern latitudes may reflect stronger policies for grassland and wetland protection in the U.S. versus Canadian portions of the prairies over the time period of our study. Maintaining the capacity of less permanent basins to rehydrate in wetter periods through easement protection benefits fecundity, particularly for late-nesting species that acquire a greater proportion of their reproductive energy on the breeding grounds. 5. Synthesis and applications. Age ratios derived from postbreeding banding operations allowed us to attribute variation in waterfowl fecundity to temporal ecological variables. Effects of habitat management for waterfowl may be masked unless analysts account for this temporal variation. Postbreeding-pulse age ratios at capture could be useful as fecundity metrics in integrated population models and for evaluating population dynamics of extensively banded nongame species, especially if adjusted for capture vulnerability using within-season recapture data.

Inferring time-varying networks is important to understand the development and evolution of interactions over time. However, the vast majority of currently used models assume direct measurements of node states, which are often difficult to obtain, especially in fields like cell biology, where perturbation experiments often only provide indirect information of network structure. Here we propose hidden Markov nested effects models (HM-NEMs) to model the evolving network by a Markov chain on a state space of signalling networks, which are derived from nested effects models (NEMs) of indirect perturbation data. To infer the hidden network evolution and unknown parameter, a Gibbs sampler is developed, in which sampling network structure is facilitated by a novel structural Metropolis–Hastings algorithm. We demonstrate the potential of HM-NEMs by simulations on synthetic time-series perturbation data. We also show the applicability of HM-NEMs in two real biological case studies, in one capturing dynamic crosstalk during the progression of neutrophil polarisation, and in the other inferring an evolving network underlying early differentiation of mouse embryonic stem cells.

Disease-specific quality indicators are used to compare institutions and health care providers in terms of processes or outcomes relevant to treatment of a particular condition. In the context of surgical cancer treatments, the performance variations can be due to hospital and/or surgeon level differences, creating a hierarchical clustering. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance, surgeon performance within hospital, patient case-mix, and unexplained (residual) variation. For this purpose, we derive a four-way variance decomposition, with particular attention to the causal interpretation of the components. For estimation, we use inputs from a mixed-effect model with nested random hospital/surgeon-specific effects, and a multinomial logistic model for the hospital/surgeon-specific patient populations. We investigate the performance of our methods in a simulation study and demonstrate them through analysis of administrative data on kidney cancer care in Ontario.

The case‐cohort design for longitudinal data consists of a subcohort sampled at the beginning of the study that is followed repeatedly over time, and a case sample that is ascertained through the course of the study. Although some members in the subcohort may experience events over the study period, we refer to it as the “control‐cohort.” The case sample is a random sample of subjects not in the control‐cohort, who have experienced at least one event during the study period. Different correlations among repeated observations on the same individual are accommodated by a two‐level random‐effects model. This design allows consistent estimation of all parameters estimable in a cohort design and is a cost‐effective way to study the effects of covariates on repeated observations of relatively rare binary outcomes when exposure assessment is expensive. It is an extension of the case‐cohort design (Prentice, 1986, Biometrika73, 1–11) and the bidirectional case‐crossover design (Navidi, 1998, Biometrics54, 596–605). A simulation study compares the efficiency of the longitudinal case‐cohort design to a full cohort analysis, and we find that in certain situations up to 90% efficiency can be obtained with half the sample size required for a full cohort analysis. A bootstrap method is presented that permits testing for intra‐subject homogeneity in the presence of unidentifiable nuisance parameters in the two‐level random‐effects model. As an illustration we apply the design to data from an ongoing study of childhood asthma.

This paper focuses on a three-dimensional model that combines two different types of spatial interaction effects, i.e. endogenous interaction effects via a spatial lag on the dependent variable and interaction effects among the disturbances via a spatial moving average (SMA) nested random effects errors. A three-stage procedure is proposed to estimate the parameters. In a first stage, the spatial lag panel data model is estimated using an instrumental variable (IV) estimator. In a second stage, a generalized moments (GM) approach is developed to estimate the SMA parameter and the variance components of the disturbance process using IV residuals from the first stage. In a third stage, to purge the equation of the specific structure of the disturbances a Cochrane–Orcutt-type transformation is applied combined with the IV principle. This leads to the GM spatial IV estimator and the regression parameter estimates. Monte Carlo simulations show that our estimators are not very different in terms of root mean square error from those produced by maximum likelihood. The approach is applied to European Union regional employment data for regions nested within countries.