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Causal inference plays a crucial role in biomedical studies and social sciences. Over the years, researchers have devised various methods to facilitate causal inference, particularly in observational studies. Among these methods, the doubly robust estimator distinguishes itself through a remarkable feature: it retains its consistency even when only one of the two components-either the propensity score model or the outcome mean model-is correctly specified, rather than demanding correctness in both simultaneously. In this paper, we focus on scenarios where semiparametric models are employed for both the propensity score and the outcome mean. Semiparametric models offer a valuable blend of interpretability akin to parametric models and the adaptability characteristic of nonparametric models. In this context, achieving correct model specification involves both accurately specifying the unknown function and consistently estimating the unknown parameter. We introduce a novel concept: the relaxed doubly robust estimator. It operates in a manner reminiscent of the traditional doubly robust estimator but with a reduced requirement for double robustness. In essence, it only mandates the consistent estimate of the unknown parameter, without requiring the correct specification of the unknown function. This means that it only necessitates a partially correct model specification. We conduct a thorough analysis to establish the double robustness and semiparametric efficiency of our proposed estimator. Furthermore, we bolster our findings with comprehensive simulation studies to illustrate the practical implications of our approach.

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The instrumental variable approaches have been demonstrated effective for semiparametrically modeling the propensity function in analyzing data that may be missing not at random. A model specification test is considered for a class of parsimonious semiparametric propensity models. The test is constructed based on assessing an over-identification so as to detect possible incompatibility in the moment conditions when the model and/or instrumental variables are misspecified. Validity of the test under the null hypothesis is established; and its power is studied when the model is misspecified. A data analysis and simulations are presented to demonstrate the effectiveness of our methods.

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Efficient resource use requires substantial evaluation and re-evaluation of production processes. Since not all production details can be properly and timely monitored and adjusted, improvement of resource allocation has long been a critical issue in commodity production, technique selection, and sustainable development. The progress of artificial intelligence (AI) offers a possibility. For example, with deep learning techniques and extensive data analysis, most previously unincorporated or unknown information associated with a firm's activities can be appropriately reflected and calculated. Once the firms take the report, a more efficient and economically friendly production strategy could be made. The central theme of this special collection is to invite studies on how the design and application of AI benefit not only the fields of computer science and information engineering but also the interdisciplinary fields, including renewable energy development, environmental protection, and economic analysis. Fourteen papers are published in this special collection.

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Knowledge-intensive service industry has become an important part of social economy, which not only promotes the innovation of modern service industry, but also promotes the upgrading and transformation of manufacturing industry. The agglomeration of knowledge-intensive service industry is not only the inevitable result of economic growth, but also the premise of sustained economic growth. Moreover, China's national economy is transforming from an industrialized economy to a service-oriented and knowledge-based economy, and the importance of knowledge-intensive service industry is increasing day by day. This paper constructs a theoretical framework of the influence of population agglomeration on the economic development of urban agglomerations. The article holds that population agglomeration promotes the economic development of urban agglomerations through the upgrading of industrial structure and the promotion of human capital. In the future, we need to focus on the similarities and differences between population agglomeration and economic development of urban agglomerations in central China, explore the factors of economic development of urban agglomerations, and provide reasonable suggestions for the government to formulate relevant economic policies. This paper, from an innovative research perspective, based on the perspective of population agglomeration, studies the path of its role in regional economic development, which can make up for this defect to some extent. Moreover, the research object has been innovated and the research method has been improved. On the basis of the above, the panel data of the Yangtze River Delta from 2012 to 2021 is selected as the research sample, the location entropy index is taken as the explanatory variable, the regional gross domestic product (GDP) is taken as the explanatory variable, and the rationality of economic theory and the significance of econometric test are considered. Finally, the data are analyzed and tested by regression. The empirical analysis of the Yangtze River Delta urban agglomeration proves the importance of knowledge-intensive service industry agglomeration to regional economic development. Finally, based on the contribution of knowledge-intensive service industry agglomeration to regional economy, the development of knowledge-intensive service industry and the promotion of industrial agglomeration are studied.

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Metaheuristics are optimization algorithms that efficiently solve a variety of complex combinatorial problems. In psychological research, metaheuristics have been applied in short-scale construction and model specification search. In the present study, we propose a bee swarm optimization (BSO) algorithm to explore the structure underlying a psychological measurement instrument. The algorithm assigns items to an unknown number of nested factors in a confirmatory bifactor model, while simultaneously selecting items for the final scale. To achieve this, the algorithm follows the biological template of bees' foraging behavior: Scout bees explore new food sources, whereas onlooker bees search in the vicinity of previously explored, promising food sources. Analogously, scout bees in BSO introduce major changes to a model specification (e.g., adding or removing a specific factor), whereas onlooker bees only make minor changes (e.g., adding an item to a factor or swapping items between specific factors). Through this division of labor in an artificial bee colony, the algorithm aims to strike a balance between two opposing strategies diversification (or exploration) versus intensification (or exploitation). We demonstrate the usefulness of the algorithm to find the underlying structure in two empirical data sets (Holzinger-Swineford and short dark triad questionnaire, SDQ3). Furthermore, we illustrate the influence of relevant hyperparameters such as the number of bees in the hive, the percentage of scouts to onlookers, and the number of top solutions to be followed. Finally, useful applications of the new algorithm are discussed, as well as limitations and possible future research opportunities.

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Pre-specification of the primary analysis model is a pre-requisite to control the family-wise type-I-error rate (T1E) at the intended level in confirmatory clinical trials. However, mixed models for repeated measures (MMRM) have been shown to be poorly specified in study protocols. The magnitude of a resulting T1E rate inflation is still unknown. This investigation aims to quantify the magnitude of the T1E rate inflation depending on the type and number of unspecified model items as well as different trial characteristics. We simulated a randomized, double-blind, parallel group, phase III clinical trial under the assumption that there is no treatment effect at any time point. The simulated data was analysed using different clusters, each including several MMRMs that are compatible with the imprecise pre-specification of the MMRM. T1E rates for each cluster were estimated. A significant T1E rate inflation could be shown for ambiguous model specifications with a maximum T1E rate of 7.6% [7.1%; 8.1%]. The results show that the magnitude of the T1E rate inflation depends on the type and number of unspecified model items as well as the sample size and allocation ratio. The imprecise specification of nuisance parameters may not lead to a significant T1E rate inflation. However, the results of this simulation study rather underestimate the true T1E rate inflation. In conclusion, imprecise MMRM specifications may lead to a substantial inflation of the T1E rate and can damage the ability to generate confirmatory evidence in pivotal clinical trials.

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Checking the models about the ongoing Coronavirus Disease 2019 (COVID-19) pandemic is an important issue. Some famous ordinary differential equation (ODE) models, such as the SIR and SEIR models have been used to describe and predict the epidemic trend. Still, in many cases, only part of the equations can be observed. A test is suggested to check possibly partially observed ODE models with a fixed design sampling scheme. The asymptotic properties of the test under the null, global and local alternative hypotheses are presented. Two new propositions about U-statistics with varying kernels based on independent but non-identical data are derived as essential tools. Some simulation studies are conducted to examine the performances of the test. Based on the available public data, it is found that the SEIR model, for modeling the data of COVID-19 infective cases in certain periods in Japan and Algeria, respectively, maybe not be appropriate by applying the proposed test.

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In the first half of 2020, much excitement in news media and some peer reviewed scientific articles was generated by the discovery that fine particulate matter (PM2.5) concentrations and COVID-19 mortality rates are statistically significantly positively associated in some regression models. This article points out that they are non-significantly negatively associated in other regression models, once omitted confounders (such as latitude and longitude) are included. More importantly, positive regression coefficients can and do arise when (generalized) linear regression models are applied to data with strong nonlinearities, including data on PM2.5, population density, and COVID-19 mortality rates, due to model specification errors. In general, statistical modeling accompanied by judgments about causal interpretations of statistical associations and regression coefficients - the current weight-of-evidence (WoE) approach favored in much current regulatory risk analysis for air pollutants - is not a valid basis for determining whether or to what extent risk of harm to human health would be reduced by reducing exposure. The traditional scientific method based on testing predictive generalizations against data remains a more reliable paradigm for risk analysis and risk management.

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Recent advances in understanding of biological mechanisms and adverse outcome pathways for many exposure-related diseases show that certain common mechanisms involve thresholds and nonlinearities in biological exposure concentration-response (C-R) functions. These range from ultrasensitive molecular switches in signaling pathways, to assembly and activation of inflammasomes, to rupture of lysosomes and pyroptosis of cells. Realistic dose-response modeling and risk analysis must confront the reality of nonlinear C-R functions. This paper reviews several challenges for traditional statistical regression modeling of C-R functions with thresholds and nonlinearities, together with methods for overcoming them. Statistically significantly positive exposure-response regression coefficients can arise from many non-causal sources such as model specification errors, incompletely controlled confounding, exposure estimation errors, attribution of interactions to factors, associations among explanatory variables, or coincident historical trends. If so, the unadjusted regression coefficients do not necessarily predict how or whether reducing exposure would reduce risk. We discuss statistical options for controlling for such threats, and advocate causal Bayesian networks and dynamic simulation models as potentially valuable complements to nonparametric regression modeling for assessing causally interpretable nonlinear C-R functions and understanding how time patterns of exposures affect risk. We conclude that these approaches are promising for extending the great advances made in statistical C-R modeling methods in recent decades to clarify how to design regulations that are more causally effective in protecting human health.

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In this paper, I will argue that the generality of the Price equation comes at a cost, and that is that the terms in it become meaningless. There are simple linear models that can be written in a Price equation-like form, and for those the terms in them have a meaningful interpretation. There are also models for which that is not the case, and in general, when no assumptions on the shape of the fitness function are made, and all possible models are allowed for, the regression coefficients in the Price equation do not allow for a meaningful interpretation. The failure to recognize that the Price equation, although general, only has a meaningful interpretation under restrictive assumptions, has done real damage to the field of social evolution, as will be illustrated by looking at an application of the Price equation to group selection. This article is part of the theme issue 'Fifty years of the Price equation'.

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The researchers examined the factor structure and model specifications of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) with confirmatory tetrad analysis (CTA) using partial least squares-structural equation modeling (PLS-SEM) with a sample of adult clients (N = 298) receiving individual therapy at a university-based counseling research center. The CTA and PLS-SEM results identified the formative nature of the WHODAS 2.0 subscale scores, supporting an alternative measurement model of the WHODAS 2.0 scores as a second-order formative-formative model.

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In this study we examined the different functions of text and pictures during text-picture integration in multimedia learning. In Study 1, 144 secondary school students (age = 11 to 14 years; 72 females, 72 males) received six text-picture units under two conditions. In the delayed-question condition, students first read the units without a specific question (no-question phase), to stimulate initial coherence-oriented mental model construction. Afterward the question was presented (question-answering phase), to stimulate task-adaptive mental model specification. In the preposed-question condition, students received a specific question from the beginning, stimulating both kinds of processing. Analyses of the participants' eye movement patterns confirmed the assumption that students allocated a higher percentage of available resources to text processing during the initial mental model construction than during adaptive model specification. Conversely, students allocated a higher percentage of available resources to picture processing during adaptive mental model specification than during the initial mental model construction. In Study 2 (N = 12, age = 12 to 16; seven females, five males), we ruled out that these findings were due to the effect of rereading, by implementing a no-question phase either once or twice. To sum up, texts seem to provide more explicit conceptual guidance in mental model construction than pictures do, whereas pictures support mental model adaptation more than text does, by providing flexible access to specific information for task-oriented updates.

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Composite likelihood functions are often used for inference in applications where the data have a complex structure. While inference based on the composite likelihood can be more robust than inference based on the full likelihood, the inference is not valid if the associated conditional or marginal models are misspecified. In this paper, we propose a general class of specification tests for composite likelihood inference. The test statistics are motivated by the fact that the second Bartlett identity holds for each component of the composite likelihood function when these components are correctly specified. We construct the test statistics based on the discrepancy between the so-called composite information matrix and the sensitivity matrix. As an illustration, we study three important cases of the proposed tests and establish their limiting distributions under both null and local alternative hypotheses. Finally, we evaluate the finite-sample performance of the proposed tests in several examples.

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[This corrects the article DOI: 10.3389/fmicb.2019.01022.].

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In confirmatory clinical trials, the prespecification of the primary analysis model is a universally accepted scientific principle to allow strict control of the type I error. Consequently, both the ICH E9 guideline and the European Medicines Agency (EMA) guideline on missing data in confirmatory clinical trials require that the primary analysis model is defined unambiguously. This requirement applies to mixed models for longitudinal data handling missing data implicitly. To evaluate the compliance with the EMA guideline, we evaluated the model specifications in those clinical study protocols from development phases II and III submitted between 2015 and 2018 to the Ethics Committee at Hannover Medical School under the German Medicinal Products Act, which planned to use a mixed model for longitudinal data in the confirmatory testing strategy. Overall, 39 trials from different types of sponsors and a wide range of therapeutic areas were evaluated. While nearly all protocols specify the fixed and random effects of the analysis model (95%), only 77% give the structure of the covariance matrix used for modeling the repeated measurements. Moreover, the testing method (36%), the estimation method (28%), the computation method (3%), and the fallback strategy (18%) are given by less than half the study protocols. Subgroup analyses indicate that these findings are universal and not specific to clinical trial phases or size of company. Altogether, our results show that guideline compliance is to various degrees poor and consequently, strict type I error rate control at the intended level is not guaranteed.

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13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.

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Marginal structural models (MSMs) allow estimating the causal effect of a time-varying exposure on an outcome in the presence of time-dependent confounding. The parameters of MSMs can be estimated utilizing an inverse probability of treatment weight estimator under certain assumptions. One of these assumptions is that the proposed causal model relating the outcome to exposure history is correctly specified. However, in practice, the true model is unknown. We propose a test that employs the observed data to attempt validating the assumption that the model is correctly specified. The performance of the proposed test is investigated with a simulation study. We illustrate our approach by estimating the effect of repeated exposure to psychosocial stressors at work on ambulatory blood pressure in a large cohort of white-collar workers in Québec City, Canada. Code examples in SAS and R are provided to facilitate the implementation of the test.

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The Si elegans platform targets the complete virtualization of the nematode Caenorhabditis elegans, and its environment. This paper presents a suite of unified web-based Graphical User Interfaces (GUIs) as the main user interaction point, and discusses their underlying technologies and methods. The user-friendly features of this tool suite enable users to graphically create neuron and network models, and behavioral experiments, without requiring knowledge of domain-specific computer-science tools. The framework furthermore allows the graphical visualization of all simulation results using a worm locomotion and neural activity viewer. Models, experiment definitions and results can be exported in a machine-readable format, thereby facilitating reproducible and cross-platform execution of in silico C. elegans experiments in other simulation environments. This is made possible by a novel XML-based behavioral experiment definition encoding format, a NeuroML XML-based model generation and network configuration description language, and their associated GUIs. User survey data confirms the platform usability and functionality, and provides insights into future directions for web-based simulation GUIs of C. elegans and other living organisms. The tool suite is available online to the scientific community and its source code has been made available.

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Reproducible results define the very core of scientific integrity in modern research. Yet, legitimate concerns have been raised about the reproducibility of research findings, with important implications for the advancement of science and for public support. With statistical practice increasingly becoming an essential component of research efforts across the sciences, this review article highlights the compelling role of statistics in ensuring that research findings in the animal sciences are reproducible-in other words, able to withstand close interrogation and independent validation. Statistics set a formal framework and a practical toolbox that, when properly implemented, can recover signal from noisy data. Yet, misconceptions and misuse of statistics are recognized as top contributing factors to the reproducibility crisis. In this article, we revisit foundational statistical concepts relevant to reproducible research in the context of the animal sciences, raise awareness on common statistical misuse undermining it, and outline recommendations for statistical practice. Specifically, we emphasize a keen understanding of the data generation process throughout the research endeavor, from thoughtful experimental design and randomization, through rigorous data analysis and inference, to careful wording in communicating research results to peer scientists and society in general. We provide a detailed discussion of core concepts in experimental design, including data architecture, experimental replication, and subsampling, and elaborate on practical implications for proper elicitation of the scope of reach of research findings. For data analysis, we emphasize proper implementation of mixed models, in terms of both distributional assumptions and specification of fixed and random effects to explicitly recognize multilevel data architecture. This is critical to ensure that experimental error for treatments of interest is properly recognized and inference is correctly calibrated. Inferential misinterpretations associated with use of P-values, both significant and not, are clarified, and problems associated with error inflation due to multiple comparisons and selective reporting are illustrated. Overall, we advocate for a responsible practice of statistics in the animal sciences, with an emphasis on continuing quantitative education and interdisciplinary collaboration between animal scientists and statisticians to maximize reproducibility of research findings.

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Gambling is an important public health issue, with recent estimates ranking it as the third largest contributor of disability adjusted life years lost to ill-health. However, no studies to date have estimated the spatial distribution of gambling-related harm in small areas on the basis of surveys of problem gambling. This study extends spatial microsimulation approaches to include a spatially-referenced measure of health behaviour as a constraint variable in order to better estimate the spatial distribution of problem gambling. Specifically, this study allocates georeferenced electronic gaming machine expenditure data to small residential areas using a Huff model. This study demonstrates how the incorporation of auxiliary spatial data on health behaviours such as gambling expenditure can improve spatial microsimulation estimates of health outcomes like problem gambling.

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