Statistical Challenges Research Articles

Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study.

One of the most promising approaches for early and more precise disease prediction and diagnosis is through the inclusion of proteomics data augmented with clinical data. Clinical proteomics data is often characterized by its high dimensionality and extremely limited sample size, posing a significant challenge when employing machine learning techniques for extracting only the most relevant information. Although there is a wide array of statistical techniques and numerous analysis pipelines employed in proteomics data analysis, it is unclear which of these methods produce the most efficient, reproducible, and clinically meaningful results. In this study, we compared 9 unique analysis schemes comprised of different machine learning and dimensionality reduction methods for the analysis of simulated proteomics data consisting of 1317 proteins measured in 26 subjects (i.e., 13 controls and 13 cases). In scenarios where the sample size is extremely small (i.e., n < 30), all schemes resulted in an exceptionally high level of performance metrics, indicating potential overfitting. While performance metrics did not exhibit significant differences across schemes, the set of proteins selected to be discriminatory between groups demonstrated a substantial level of heterogeneity. However, despite heterogeneity in the selected proteins, their biological pathways and genetic diseases exhibited similarities. A sensitivity analysis conducted using varying sample sizes indicated that the stability of a set of selected biomarkers improves with larger sample sizes within a scheme. When the aim of the study is to identify a statistical model that best distinguishes between cohort groups using proteomics data and to uncover the biological pathways and disorders common among the selected proteins, the majority of widely used analysis pipelines perform similarly. However, if the main objective is to pinpoint a set of selected proteins that wield significant influence in discriminating cohort groups and utilize them for subsequent investigations, meticulous consideration is necessary when opting for statistical models, due to the possibility of heterogeneity in the sets of selected proteins.

Unraveling the Complexity of the N e/N c Ratio for Conservation of Large and Widespread Pelagic Fish Species: Current Status and Challenges.

Estimating and understanding the ratio between effective population size (N e) and census population size (N c) are pivotal in the conservation of large marine pelagic fish species, including bony fish such as tunas and cartilaginous fish such as sharks, given the challenges associated with obtaining accurate estimates of their abundance. The difficulties inherent in capturing and monitoring these species in vast and dynamic marine environments often make direct estimation of their population size challenging. By focusing on N e, it is conceivable in certain cases to approximate census size once the N e/N c ratio is known, although this ratio can vary and does not always increase linearly, as it is influenced by various ecological and evolutionary factors. Thus, this ratio presents challenges and complexities in the context of pelagic species conservation. To delve deeper into these challenges, firstly, we recall the diverse types of effective population sizes, including contemporary and historical sizes, and their implications in conservation biology. Secondly, we outline current knowledge about the influence of life history traits on the N e/N c ratio in the light of examples drawn from large and abundant pelagic fish species. Despite efforts to document an increasing number of marine species using recent technologies and statistical methods, establishing general rules to predict N e/N c remains elusive, necessitating further research and investment. Finally, we recall statistical challenges in relating N e and N c emphasizing the necessity of aligning temporal and spatial scales. This last part discusses the roles of generation and reproductive cycle effective population sizes to predict genetic erosion and guiding management strategies. Collectively, these sections underscore the multifaceted nature of effective population size estimation, crucial for preserving genetic diversity and ensuring the long-term viability of populations. By navigating statistical and theoretical complexities, and addressing methodological challenges, scientists should be able to advance our understanding of the N e/N c ratio.

Statistical challenges and solutions in multidisciplinary clinical research: Bridging the gap between

This paper delves into the intricate challenges and innovative solutions in applying statistical methodologies within clinical research, aiming to bridge the gap between biostatistics and medicine. The study meticulously examines fundamental biostatistical concepts, addressing the complexities of modern clinical trials and observational studies. Through a comprehensive review of advanced regression models, causal inference techniques, and machine learning algorithms, the paper illuminates the evolving landscape of biostatistics in handling high-dimensional data and confounding variables. The methods employed in this study involve an extensive analysis of current literature, case studies, and practical applications that demonstrate the utility of these advanced methodologies. Key findings reveal that traditional statistical approaches often fall short in capturing the complexities of clinical data, necessitating the adoption of more sophisticated techniques. The integration of non-linear regression models, robust causal inference methods, and machine learning has significantly enhanced the accuracy and reliability of research outcomes, offering deeper insights into patient outcomes and treatment efficacy. Conclusions drawn from this study underscore the critical need for a paradigm shift in clinical research, moving beyond the rigid reliance on p-values towards a more holistic approach that emphasizes effect sizes, confidence intervals, and practical significance. The paper recommends continued innovation in statistical methodologies, particularly the integration of big data analytics and machine learning, to address the growing complexities of biomedical data. Furthermore, it advocates for interdisciplinary collaboration and ethical considerations in the application of these advanced techniques to ensure that biostatistics continues to contribute meaningfully to the advancement of medical science.

Valid Inference After Causal Discovery

Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to “double dipping,” invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. Across empirical studies, we show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates; on the other hand, applying our method provides reliable coverage while achieving more accurate causal discovery than data splitting.

GLAMLE: inference for multiview network data in the presence of latent variables, with an application to commodities trading

The statistical analysis of multiview network data provides researchers with insights on the mechanism governing many complex systems of interconnected elements. A graph is routinely applied to model this type of data: this poses several statistical challenges. Among them, there is the need for considering that some unobservable factors may determine the network connections and the graph topology. This requirement entails both complex models and computationally heavy inference procedures. To cope with these inference issues, a novel and flexible class of latent variable models labeled Graph Generalized Linear Latent Variable Model (GGLLVM) is introduced. Then, its parameters are estimated via the maximization of the Laplace-approximated likelihood. This leads to the creation of an M-estimator which is named Graph Laplace-Approximated Maximum Likelihood Estimator (GLAMLE). The statistical properties of this new estimator are characterized, and numerical experiments on simulated networks are performed to illustrate that the GLAMLE yields fast and accurate inference, in comparison to other widely used approaches. A real data application to commodities trading in European countries illustrates how the use of GGLLVM and GLAMLE unveils the import/export propensity that each node of the network has toward other nodes, along with additional information specific to each traded commodity.

Intergenerational Educational Mobility and Cognitive Trajectories Among Middle-Aged and Older Chinese People: An Application of Growth Mixture and Mobility Contrast Models in Longitudinal Analysis.

Due to statistical challenges in disentangling the mobility effect (i.e., intergenerational educational mobility) from the position effect (i.e., parental and person's own education), the impact of intergenerational educational mobility on cognitive function remains unclear. We employed a novel approach to identify the mobility effect and investigate the net impact of intergenerational educational mobility on heterogeneous patterns of cognition among middle-aged and older adults in China. Participants aged 45 and older were recruited from the China Health and Retirement Longitudinal Study, a population-based prospective cohort study between 2011 and 2018. We identified cognitive trajectories using the growth mixture model (GMM) and subsequently employed the mobility contrast model (MCM) to examine the effects of intergenerational educational mobility on cognitive patterns stratified by gender. Almost two thirds of respondents experienced intergenerational educational mobility, and 55% experienced upward mobility. Men had a higher rate of upward mobility than women. Three population-based cognitive patterns were identified: the low cognitive function with decline group (28%), the moderate cognitive function group (47%), and the high cognitive function group (26%). MCM analysis revealed that both upward and downward intergenerational educational mobility negatively affected cognitive trajectory patterns, extending beyond the influence of individuals' current and parental education. In future research, the impact of mobility can be studied in longitudinal data sets by combining the GMM and MCM approaches. The net negative effect of intergenerational educational mobility on cognitive trajectory patterns indicates that it should be recognized as an independent predictor of cognitive decline.

Next-generation statistical methodology: Advances health science research

Accurately modeling health science data is crucial for advancing medical research and improving patient outcomes. Traditional statistical analysis methods face significant challenges due to the complexity and diversity of health sciences data. This article introduces a groundbreaking statistical framework designed to overcome these challenges by developing a next-generation family of distributions, with a special focus on the versatility of the Weibull distribution. The data used in this study has been thoroughly authenticated to ensure reliability and validity. Comprehensive Monte Carlo simulations revealed that the Maximum Product of Spacing Estimator is the most effective among seven-point estimation methods, according to standard metrics. Additionally, the study identifies optimal methods for analyzing various types of lifetime data, including the Maximum Product of Spacing Estimator for pharmaceutical efficacy (ED50), the Least Squares Estimator for psychiatric treatment durations, the Cramer-von Mises Estimator for data on 43 leukemia patients and for survival periods of 20 leukemia patients, and the Right Tail Anderson-Darling Estimator for remission times of 128 bladder cancer patients. The adaptability and flexibility of the next-generation Weibull distribution set it apart as the best match among its contemporaries.

A Federated Learning-Based Industrial Health Prognostics for Heterogeneous Edge Devices Using Matched Feature Extraction

Data-driven industrial health prognostics require rich training data to develop accurate and reliable predictive models. However, stringent data privacy laws and the abundance of edge industrial data necessitate decentralized data utilization. Thus, the industrial health prognostics field is well suited to significantly benefit from federated learning (FL), a decentralized and privacy-preserving learning technique. However, FL-based health prognostics tasks have hardly been investigated due to the complexities of meaningfully aggregating model parameters trained from heterogeneous data to form a high performing federated model. Specifically, data heterogeneity among edge devices, stemming from dissimilar degradation mechanisms and unequal dataset sizes, poses a critical statistical challenge for developing accurate federated models. We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm to discriminatingly learn from heterogeneous edge data. The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters. As the algorithm only averages similar neurons, as opposed to conventional naive averaging of coordinate-wise neurons, the distinct feature extractors of local models are carried over with less dilution to the resultant federated model. Using both cyclic degradation data of Li-ion batteries and non-cyclic data of turbofan engines, we demonstrate that the proposed method yields accuracy improvements as high as 44.5\% and 39.3\% for state-of-health estimation and remaining useful life estimation, respectively.

Statistical Challenges and Opportunities in Quantum Computing: A Review

Statistical Challenges and Opportunities in Quantum Computing: A Review

A modeling framework for detecting and leveraging node-level information in Bayesian network inference.

Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximization algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.

High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application.

Causal mediation analysis provides a systematic approach to explore the causal role of one or more mediators in the association between exposure and outcome. In omics or imaging data analysis, mediators are often high-dimensional, which brings new statistical challenges. Existing methods either violate causal assumptions or fail in interpretable variable selection. Additionally, mediators are often highly correlated, presenting difficulties in selecting and prioritizing top mediators. To address these issues, we develop a framework using Partial Sum Statistic and Sample Splitting Strategy, namely PS5, for high-dimensional causal mediation analysis. The method provides a powerful global mediation test satisfying causal assumptions, followed by an algorithm to select and prioritize active mediators with quantification of individual mediation contributions. We demonstrate its accurate type I error control, superior statistical power, reduced bias in mediation effect estimation, and accurate mediator selection using extensive simulations of varying levels of effect size, signal sparsity, and mediator correlations. Finally, we apply PS5 to an imaging genetics dataset of chronic obstructive pulmonary disease (COPD) patients ( N =8,897) in the COPDGene study to examine the causal mediation role of lung images ( p =5,810) in the associations between polygenic risk score and lung function and between smoking exposure and lung function, respectively. Both causal mediation analyses successfully estimate the global indirect effect and detect mediating image regions. Collectively, we find a region in the lower lobe of the right lung with a strong and concordant mediation effect for both genetic and environmental exposures. This suggests that targeted treatment toward this region might mitigate the severity of COPD due to genetic and smoking effects.

Understanding the picture: the promise and challenges of in-situ imagery data in the study of plankton ecology

Abstract Planktons are a fundamental piece of all ocean ecosystems yet, sampling plankton at the high resolution required to understand their dynamics remains a challenge. In-situ imaging tools offer an approach to sample plankton at fine scales. Advances in technology and methodology provide the ability to make in-situ imaging a common tool in plankton ecology. Despite the massive potential of in-situ imaging tools, there are no standard approaches for analyzing the associated data. Consequently, studies are inconsistent in analyzing in-situ imaging data, even for similar questions. This introduces challenges in comparing across studies and sampling devices. In this review, we briefly summarize the increasing use, potential and novel applications of in-situ imaging tools in plankton ecology. Then, we synthesize the common analyses used across these studies. Finally, we address the major statistical challenges associated with the unique sampling mechanisms of in-situ imaging tools and discuss the theoretical uncertainties, which arise from the low-sampling volumes of many in-situ imaging tools. To fully unlock the power of in-situ imaging tools in plankton ecological studies, researchers must carefully consider how to analyze their data. We provide recommendations for processing and analyzing data while also acknowledging a large need for developing new statistical tool.

A model for accurate quantification of CRISPR effects in pooled FACS screens.

CRISPR screens are powerful tools to identify key genes that underlie biological processes. One important type of screen uses fluorescence activated cell sorting (FACS) to sort perturbed cells into bins based on the expression level of marker genes, followed by guide RNA (gRNA) sequencing. Analysis of these data presents several statistical challenges due to multiple factors including the discrete nature of the bins and typically small numbers of replicate experiments. To address these challenges, we developed a robust and powerful Bayesian random effects model and software package called Waterbear. Furthermore, we used Waterbear to explore how various experimental design parameters affect statistical power to establish principled guidelines for future screens. Finally, we experimentally validated our experimental design model findings that, when using Waterbear for analysis, high power is maintained even at low cell coverage and a high multiplicity of infection. We anticipate that Waterbear will be of broad utility for analyzing FACS-based CRISPR screens.

A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.

This article seeks to investigate the impact of aging on functional connectivity across different cognitive control scenarios, particularly emphasizing the identification of brain regions significantly associated with early aging. By conceptualizing functional connectivity within each cognitive control scenario as a graph, with brain regions as nodes, the statistical challenge revolves around devising a regression framework to predict a binary scalar outcome (aging or normal) using multiple graph predictors. Popular regression methods utilizing multiplex graph predictors often face limitations in effectively harnessing information within and across graph layers, leading to potentially less accurate inference and predictive accuracy, especially for smaller sample sizes. To address this challenge, we propose the Bayesian Multiplex Graph Classifier (BMGC). Accounting for multiplex graph topology, our method models edge coefficients at each graph layer using bilinear interactions between the latent effects associated with the two nodes connected by the edge. This approach also employs a variable selection framework on node-specific latent effects from all graph layers to identify influential nodes linked to observed outcomes. Crucially, the proposed framework is computationally efficient and quantifies the uncertainty in node identification, coefficient estimation, and binary outcome prediction. BMGC outperforms alternative methods in terms of the aforementioned metrics in simulation studies. An additional BMGC validation was completed using an fMRI study of brain networks in adults. The proposed BMGC technique identified that sensory motor brain network obeys certain lateral symmetries, whereas the default mode network exhibits significant brain asymmetries associated with early aging.

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment.

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a new, more powerful method for evaluating FDR control in this setting, and we then employ that method, along with an existing lower bounding technique, to characterize a variety of popular search tools. We find that the search tools for analysis of data-dependent acquisition (DDA) data generally seem to control the FDR at the peptide level, whereas none of the DIA search tools consistently controls the FDR at the peptide level across all the datasets we investigated. Furthermore, this problem becomes much worse when the latter tools are evaluated at the protein level. These results may have significant implications for various downstream analyses, since proper FDR control has the potential to reduce noise in discovery lists and thereby boost statistical power.

Fifty shades of QE revisited

Fabo et al. (2021) use OLS regression to show that central bankers report quantitatively larger effects of QE on output and inflation than do academic researchers. We reject the null hypothesis of a Gaussian distribution of the residuals in many of these specifications, except for the language regressions. We then repeat the analysis with regression estimators that are robust to a non-Gaussian residual distribution where this is feasible. We use the median regression and the MS regression estimator. With these robust regression approaches, the null hypothesis that central bank and academic researchers report the same quantitative effect of QE on output and inflation cannot be rejected, with point estimates which are less than half as large. This statistical challenge suggests that more research is required to understand better whether central bank researchers report different QE multipliers or not.

Analysis Of Low Self-Efficacy Among Students Using Creative Problem-Solving Techniques

Self-efficacy of students in understanding educational statistics material is the main focus of this analysis. Through the Finding Facts stage, the complexity of the material, lack of support from lecturers, and ineffective curriculum/methods of teaching are identified as key factors influencing the low self-efficacy of students. The Finding Acceptance stage confirms a high acceptance of these findings as issues needing to be addressed. The implications of this analysis emphasize the need for holistic action to enhance student self-efficacy, through simplifying materials, increasing lecturer support, and revolutionizing curriculum and teaching methods. Thus, the article proposes concrete steps to create a supportive and empowering learning environment for students facing challenges in learning educational statistics.

The effects of local economic development on female obesity (overweight) in sub‐Saharan Africa

AbstractObesity (overweight) is a widespread concern not only in high‐income nations but also in low‐income countries across sub‐Saharan Africa (SSA). Although many studies attribute this trend to economic development triggering a shift in nutrition patterns within SSA, they tend overlook a critical factor: the level at which these determinants are measured. Assessing them nationally while drawing comparisons with individual‐level obesity data introduces a statistical challenge known as the ecological fallacy. To address this, we utilize local‐level night light data as a proxy for local economic development. Analyzing demographic and health surveys from 44 SSA countries spanning the period 1992–2019, we find that local development is associated with a 0.002% increase in the body mass index of women. In addition, we find that night light intensity is associated with 0.2%–0.3% increases in probabilities of a woman being overweight and obese. Our results remain robust when we employ an instrumental variable approach by using a control function based on peer effect. In terms of policy implication, our research highlights that local development may entail potential health costs, emphasizing the need for African governments to invest in healthcare and also build physical infrastructure that can promote active lifestyles.

Challenges and Lessons Learned in Autologous Chimeric Antigen Receptor T-Cell Therapy Development from a Statistical Perspective.

Chimeric antigen receptor (CAR) T-cell therapy is a human gene therapy product where T cells from a patient are genetically modified to enable them to recognize desired target antigen(s) more effectively. In recent years, promising antitumor activity has been seen with autologous CAR T cells. Since 2017, six CAR T-cell therapies for the treatment of hematological malignancies have been approved by the Food and Drug Administration (FDA). Despite the rapid progress of CAR T-cell therapies, considerable statistical challenges still exist for this category of products across all phases of clinical development that need to be addressed. These include (but not limited to) dose finding strategy, implementation of the estimand framework, use of real-world data in contextualizing single-arm CAR T trials, analysis of safety data and long-term follow-up studies. This paper is the first step in summarizing and addressing these statistical hurdles based on the development of the six approved CAR T-cell products.

Opportunities and Challenges in Health Insurance Statistics in the Era of Big Data

With the rapid advancement of big data technology, health insurance statistics are undergoing unprecedented transformations. This study explores the opportunities and challenges of health insurance statistics in the context of big data, offering theoretical frameworks and strategic recommendations. Through a comprehensive literature review and theoretical analysis, the study systematically examines related research on the application of big data in health insurance statistics globally. It analyzes how big data technology enhances the efficiency of data collection, processing, and analysis in health insurance, while also addressing issues like data security, privacy protection, and talent shortages. The research involves an in-depth analysis of big data technology characteristics and a reevaluation of health insurance statistical workflows. The findings indicate that big data offers opportunities for improved data processing capabilities and enhanced decision support in health insurance statistics, while simultaneously posing challenges in data governance, privacy protection, and talent development. To fully leverage the advantages of big data, an improved data management system, strengthened data security and privacy measures, and the cultivation of professionals skilled in big data analysis are necessary. This study provides theoretical support and practical guidance for the development of health insurance statistics in the big data context.