Statistical Foundation Research Articles

The Application of Knowledge Engineering via the Use of a Biomimetic Digital Twin Ecosystem, Phenotype-Driven Variant Analysis, and Exome Sequencing to Understand the Molecular Mechanisms of Disease

Applied Artificial Intelligence, particularly Large Language Models, in biomedical research is accelerating, but effective discovery and validation requires a toolset without limitations or bias. On January 30, 2023, the National Academies of Sciences, Engineering, and Medicine (NAS) appointed an ad hoc committee to identify needs and opportunities to advance the mathematical, statistical, and computational foundations of digital twins in applications across science, medicine, engineering, and society. On December 15, 2023, the NAS released a 164 page report, "Foundational Research Gaps and Future Directions for Digital Twins". This report described the importance of using digital twins in biomedical research. We developed an innovative method that incorporated phenotype ranking algorithms with knowledge engineering via a biomimetic digital twin ecosystem. This ecosystem applied real-world reasoning principles to non-normalized, raw data to identify hidden or "dark data". We performed a clinical exome sequencing study on patients with endometriosis and were able to identify four VUSs potentially associated with endometriosis-related disorders in nearly all patients analyzed. One VUS was identified in all patient samples and could be a biomarker for diagnostics. To the best of our knowledge, this is the first study to incorporate the recomandations of the NAS to biomedical research. This method can be used to understand the mechanisms of any disease, for virtual clinical trials, and to identify effective new therapies.

Introduction to Machine Learning

Machine learning has emerged as a transformative technology with wide-ranging applications across diverse domains, from healthcare and finance to autonomous systems and environmental monitoring. This paper provides a comprehensive review of the field of machine learning, encompassing foundational concepts, recent advancements, applications, ethical considerations, challenges, and future directions. The paper begins with an overview of machine learning, highlighting its historical evolution, importance, and applications in various sectors. It then delves into the foundational principles of machine learning, including different types of learning, model evaluation techniques, and statistical foundations. Advances in machine learning algorithms, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning architectures, are discussed in detail. The paper explores the wide-ranging applications of machine learning across different domains, including healthcare, finance, autonomous systems, natural language processing, image and video processing, and environmental monitoring. Ethical and societal implications of machine learning, such as bias and fairness, privacy concerns, automation and job displacement, responsible AI development, and regulatory frameworks, are examined. Challenges and future directions in machine learning, including data quality and quantity issues, interpretability and explainability of models, scalability and resource constraints, addressing ethical and societal concerns, advancements in interdisciplinary research, and opportunities for innovation and collaboration, are discussed. Overall, this paper provides a comprehensive overview of machine learning, highlighting its transformative potential, ethical considerations, challenges, and opportunities for future research and development. It serves as a valuable resource for researchers, practitioners, policymakers, and stakeholders interested in understanding the evolving landscape of machine learning and its impact on society

D. R. Cox: aspects of scientific inference

Abstract The year 2022 marked 100 years since R. A. Fisher’s landmark paper on the foundations of statistics, and 50 years since D. R. Cox’s seminal work on the proportional hazards model. At a 1-day meeting in recognition of the latter, held at the London School of Hygiene and Tropical Medicine, I attempted to place some of D. R. Cox’s most influential work in its appropriate historical context, reconstructing some of his ideas from unifying principles and providing a different exposition to that of the original papers. The present article is an elaboration of that talk. Additional material includes a geometric explanation of conditional inference in models with binary outcomes, relating the logistic analysis of matched comparisons presented by Cox (1958c, Two further applications of a model for binary regression. Biometrika, 45, 562–565) to the protracted and sometimes polemical discourse over the analysis of 2×2 contingency tables.

Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index

In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety of all kinds of large buildings, especially underground engineering buildings. As a quantitative classification index of rock mass, the rock block index is very common in the classification of borehole and dam foundation rock mass structures. However, there are few studies on the classification of underground engineering rock masses. Moreover, their classification criteria have disadvantages in spatial dimension. Therefore, this paper takes the long exploratory cave CPD1 in the water transmission and power generation system of the Qingtian pumped storage power station in Zhejiang Province as the research object and launches a study on the structural classification of the rock mass of a flat cave based on the 3D rock block index. According to the group distribution of joints, the sections are statistically homogeneous. Additionally, the Monte Carlo method is used to carry out random simulations to generate a three-dimensional joint network model. The virtual survey lines are arranged along the center of the shape of the three different orthogonal planes of the 3D joint network model to represent the boreholes, and the RBI values of the virtual survey lines on each orthogonal plane are counted to classify the rock mass structure of the flat cave in a refined manner using the rock block index of the rock mass in 3D. The above method realizes the application of the 3D rock block index in underground engineering and overcomes the limitations of traditional rock mass classification methods in terms of classification index and dimension. The results show that: (1) Three-dimensional joint network simulations built on statistical and probabilistic foundations can visualize the structure of the rock mass and more accurately reflect the structural characteristics of the actual rock mass. (2) Based on the 3D rock block index, the rock mass structure of the long-tunnel CPD1 is classified, from that of a continuous structure to a blocky structure, corresponding to the integrity of the rock mass from complete to relatively complete. The classification results are consistent with the evaluation results of horizontal tunnel seismic wave geophysical exploration. (3) Based on the 3D joint network model, it is reasonable and feasible to use the 3D rock block index as a quantitative evaluation index to determine the structure type of flat cave rock masses. The above method is helpful and significant in the classification of underground engineering rock mass structures.

Understanding Sample Size Determination In Research: A Practical Guide.

Sample size determination is a critical aspect of business research, influencing the validity, reliability, and generalizability of study findings. This paper provides a comprehensive overview of sample size determination in business research, addressing its significance, statistical foundations, methods, considerations, practical guidelines, and future research directions. Key findings highlight the importance of aligning sample size with research objectives, ensuring adequate statistical power, and considering practical constraints in sample size determination. Various methods, including power analysis, formula-based approaches, and online calculators, are discussed for calculating sample sizes, each with its advantages and considerations. Unique challenges in business research, such as population heterogeneity, sampling techniques, and practical constraints, are explored, along with strategies for addressing these challenges. Practical guidelines and recommendations are provided to assist researchers in determining appropriate sample sizes and improving the reliability and validity of research outcomes. Furthermore, future research directions are identified, including methodological advancements, integration of big data analytics, cross-cultural perspectives, ethical considerations, interdisciplinary collaborations, and validation studies. By addressing these research directions, researchers can advance the field of sample size determination in business research, enhance methodological rigor, and contribute to the generation of high-quality and impactful research outcomes.

Применение методологической базы исследования для определения кондуктивной низкочастотной электромагнитной помехи по коэффициенту n-й гармонической составляющей напряжения

With the help of the methodological base of the study (the foundations of probability theory and mathematical statistics), the processing of the experiment conducted at the research object is carried out. This object is the mechanical workshop of CJSC Sibgazstroydetal, which is engaged in providing for the needs of the gas, oil and energy industries. In this paper, the issue of determining the conductive low-frequency electromagnetic interference (EMI) by the coefficient of the n-th harmonic component of the voltage (K U(n) ) is studied in detail. Harmonic distortion is a constant phenomenon in electrical networks. Higher harmonics have a negative impact on the electrical network, including on electric consumers (reduced service life, productivity, etc.). The problem of non-sinusoidal voltages has been studied for quite a long time and the results obtained during these studies confirm the presence of a large number of damages to electricity consumers who are in operation. The probability of occurrence of conductive low-frequency EMF by K U(n) at the research site is determined. The paper analyzes the results obtained with the help of the conducted research. According to the results of the analysis, the relevant conclusions are presented.

Ordered probit Bayesian additive regression trees for ordinal data

Bayesian additive regression trees (BART) is a nonparametric model that is known for its flexibility and strong statistical foundation. To address a robust and flexible approach to analyse ordinal data, we extend BART into an ordered probit regression framework (OPBART). Further, we propose a semiparametric setting for OPBART (semi‐OPBART) to model covariates of interest parametrically and confounding variables nonparametrically. We also provide Gibbs sampling procedures to implement the proposed models. In both simulations and real data studies, the proposed models demonstrate superior performance over other competing ordinal models. We also highlight enhanced interpretability of semi‐OPBART in terms of inference through marginal effects.

Construction of multilevel statistical models in health research: Foundations and generalities

This topic review aims to present a global vision of multilevel analysis models’ applicability to health research, explaining its theoretical, methodological, and statistical foundations. We describe the basic steps to build these models and examples of their application according to the data hierarchical structure. It ir worth noticing that before using these models, researchers must have a rationale for needing them, and a statistical evaluation accounting for the variance percentage explained by the observations grouping effect. The requirements to conduct this type of analysis depends on special conditions such as the type of variables, the number of units per level, or the type of hierarchical structure. We conclude that multilevel analysis models are a useful tool to integrate information, considering the complexity of the relationships and interactions involved in most health conditions, including the loss of independence between observation units.

Manifold Learning: What, How, and Why

Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to find the low-dimensional structure of data. Dimension reduction for large, high-dimensional data is not merely a way to reduce the data; the new representations and descriptors obtained by ML reveal the geometric shape of high-dimensional point clouds and allow one to visualize, denoise, and interpret them. This review presents the underlying principles of ML, its representative methods, and their statistical foundations, all from a practicing statistician's perspective. It describes the trade-offs and what theory tells us about the parameter and algorithmic choices we make in order to obtain reliable conclusions.

RegioneReloaded: evaluating the association of multiple genomic region sets.

Next-generation sequencing methods continue improving the annotation of genomes in part by determining the distribution of features such as epigenetic marks. Evaluating and interpreting the association between genomic regions and their features has become a common and challenging analysis in genomic and epigenomic studies. With regioneR we provided an R package allowing to assess the statistical significance of pairwise associations between genomic region sets using permutation tests. We now present the R package regioneReloaded that builds upon regioneR's statistical foundation and extends the functionality for the simultaneous analysis and visualization of the associations between multiple genomic region sets. Thus, we provide a novel discovery tool for the identification of significant associations that warrant to be tested for functional interdependence. regioneReloaded is an R package released under an Artistic-2.0 License. The source code and documentation are freely available through Bioconductor: http://www.bioconductor.org/packages/regioneReloaded.

Intraoperative Surgeon Assessment of Bone: Correlation to Bone Mineral Density, CT Hounsfield Units, and Vertebral Bone Quality.

Retrospective observational study of consecutive patients. The purpose of the study is to determine if a surgeon's qualitative assessment of bone intraoperatively correlates with radiologic parameters of bone strength. Preoperative radiologic assessment of bone can include modalities such as computed tomography (CT) Hounsfield units (HUs), dual-energy x-ray absorptiometry (DXA) bone mineral density with trabecular bone score (TBS) and magnetic resonance imaging vertebral bone quality (VBQ). Quantitative analysis of bone with screw insertional torque and pull-out strength measurement has been performed in cadaveric models and has been correlated to these radiologic parameters. However, these quantitative measurements are not routinely available for use in surgery. Surgeons anecdotally judge bone strength, but the fidelity of the intraoperative judgment has not been investigated. All adult patients undergoing instrumented posterior thoracolumbar spine fusion by one of seven surgeons at a single center over a 3-month period were included. Surgeons evaluated the strength of bone based on intraoperative feedback and graded each patient's bone on a 5-point Likert scale. Two independent reviewers measured preoperative CT HUs and magnetic resonance imaging VBQ. Bone mineral density, lowest T-score, and TBS were extracted from DXA within 2 years of surgery. Eighty-nine patients were enrolled and 16, 28, 31, 13, and 1 patients had Likert grade 1 (strongest bone), 2, 3, 4, and 5 (weakest bone), respectively. The surgeon assessment of bone correlated with VBQ (τ=0.15, P =0.07), CT HU (τ=-0.31, P <0.01), lowest DXA T-score (τ=-0.47, P <0.01), and TBS (τ=-0.23, P =0.06). Spine surgeons' qualitative intraoperative assessment of bone correlates with preoperative radiologic parameters, particularly in posterior thoracolumbar surgeries. This information is valuable to surgeons as this supports the idea that decisions based on feel in surgery have a statistical foundation.

An Empirical Study of Hot-Extruded Recycled Aluminium Alloy Chips in 2014

It has been discovered that the conventional method of recycling aluminum, which entails remelting the garbage, leads to several environmental problems, material inefficiencies, and energy resource depletion. The purpose of this research is to develop a way of recycling AA2014 aluminum that is both efficient and kind on the environment. Turning waste chips were collected, crushed into billets, and heated before being extruded. The design of the tests was carried out in accordance with the DOE methodology. Extrusion temperature and ratio were the distinguishing features of the recycling procedure. Researchers used tools like RA and ANOVA to develop mathematical models with solid statistical foundations. Both the ultimate tensile strength and the yield strength of the extruded aluminum are shown to be affected by the extrusion parameters using the equations used in this study. The investigation found that the tensile and yield strengths of the specimens were significantly affected by both the extrusion temperature and the extrusion ratio. To achieve the highest possible levels of the extrudates' aforementioned mechanical characteristics, an optimization procedure was carried out.

A novel subspace outlier detection method by entropy-based clustering algorithm

Subspace outlier detection has emerged as a practical approach for outlier detection. Classical full space outlier detection methods become ineffective in high dimensional data due to the “curse of dimensionality”. Subspace outlier detection methods have great potential to overcome the problem. However, the challenge becomes how to determine which subspaces to be used for outlier detection among a huge number of all subspaces. In this paper, firstly, we propose an intuitive definition of outliers in subspaces. We study the desirable properties of subspaces for outlier detection and investigate the metrics for those properties. Then, a novel subspace outlier detection algorithm with a statistical foundation is proposed. Our method selectively leverages a limited set of the most interesting subspaces for outlier detection. Through experimental validation, we demonstrate that identifying outliers within this reduced set of highly interesting subspaces yields significantly higher accuracy compared to analyzing the entire feature space. We show by experiments that the proposed method outperforms competing subspace outlier detection approaches on real world data sets.

Stock price prediction using a novel approach in Gaussian mixture model-hidden Markov model

PurposeThis study aims to provide the best estimate of a stock's next day's closing price for a given day with the help of the hidden Markov model–Gaussian mixture model (HMM-GMM). The results were compared with Hassan and Nath’s (2005) study using HMM and artificial neural network (ANN).Design/methodology/approachThe study adopted an initialization approach wherein the hidden states of the HMM are modelled as GMM using two different approaches. Training of the HMM-GMM model is carried out using two methods. The prediction was performed by taking the closest closing price (having a log-likelihood within the tolerance range) to that of the present one as the closing price for the next day. Mean absolute percentage error (MAPE) has been used to compare the proposed GMM-HMM model against the models of the research study (Hassan and Nath, 2005).FindingsComparing this study with Hassan and Nath (2005) reveals that the proposed model outperformed in 66 out of the 72 different test cases. The results affirm that the model can be used for more accurate time series prediction. Further, compared with the results of the ANN model from Hassan's study, the proposed HMM model outperformed 24 of the 36 test cases.Originality/valueThe study introduced a novel initialization and two training/prediction approaches for the HMM-GMM model. It is to be noted that the study has introduced a GMM-HMM-based closing price estimator for stock price prediction. The proposed method of forecasting the stock prices using GMM-HMM is explainable and has a solid statistical foundation.

THE LATEST TRENDS IN GEORGIA’S BUSINESS REGISTER

Lia Dzebisauri i &#x0D; E-mail: lia.dzebisauri@tsu.ge &#x0D; Associate Professor, Ivane Javakhishvili Tbilisi State University &#x0D; Tbilisi, Georgia&#x0D; https://orcid.org/0009-0002-2604-9323&#x0D; &#x0D; Marine (Maia) Mindorashvili &#x0D; E-mail: maia.mindorashvili@tsu.ge &#x0D; Associate Professor, Ivane Javakhishvili Tbilisi State University &#x0D; Tbilisi, Georgia&#x0D; https://orcid.org/0009-0001-7177-9870&#x0D; Abstract .In this paper, we examine the Statistical Business Register (SBR) in Georgia, which serves as the foundation for economic statistics. To keep up with the demands of the modern digital economy, the SBR requires ongoing capacity-building efforts. It's a critical component in generating official statistics that inform evidence-based decision-making. In light of the ongoing globalization process, particularly in the wake of the COVID-19 pandemic, there's a growing demand for high-quality, comparable SBR and economic statistics at the international level. Geostat's BR is unique in that it allows for determining an entity's activity status. However, the current globalization process presents a challenge to creating a perfect SBR, increasing the need for an internationally comparable SBR in the post-COVID-19 pandemic world. Our paper highlights the significance of the SBR and investigates registered and active business entities in Georgia according to various parameters, such as economic activities, legal status, and territorial distribution by regions, and statistical trends of active companies during pre and pandemic periods. We also identify areas where COVID-19 had the most detrimental effect and where further recovery is necessary for the sustainable development of Georgia's economy. As the backbone for economic statistics, the SBR needs continued capacity-building activities in the modern digital economy. It plays a central role in producing official statistics, both in terms of how the statistics are produced and the quality of the statistics that draw on information from them. Our article, in particular, is devoted to improving the quality and modernizing the indicators of the statistical business register, which further contributes to producing high level business and macroeconomic statistics.

Parsimonious item response theory modeling with the negative log-log link: The role of inflection point shift.

In item response theory (IRT) modeling, the magnitude of the lower and upper asymptote parameters determines the degree to which the inflection point shifts above or below P = 0.50. The current study examines the one-parameter negative log-log model (NLLM), which is characterized by a downward shift in the inflection point, among other distinctive psychometric properties. After detailing the statistical foundations of the NLLM, we present a series of simulation studies to establish item and person parameter estimation accuracy and to demonstrate that this parsimonious model addresses the "slipping" effect (i.e., unexpectedly incorrect answers) via an inflection point < 0.50 rather than through computationally difficult estimation of the upper asymptote. We then provide further support for these simulation results through empirical data analysis. Finally, we discuss how the NLLM contributes to recent methodological literature on the utility of asymmetric IRT models.

Phase 3 randomized study for evaluation of physician choice Rx and triple metronomic as second-line therapy in head and neck cancer (CRSF 2021-HN-001).

LBA6004 Background: There are multiple options of treatment in second-line therapy in locally advanced head and neck squamous cell carcinoma (LAHNSCC) with recurrent or metastatic disease. However triple metronomic chemotherapy is oral, affordable, and requires minimal resources. Hence in this study, we compared NCCN-recommended physician choice of standard systemic therapy versus triple metronomic therapy in the second-line treatment of head and neck cancer. Methods: This was a phase 3, multicentric (16 sites), randomized study with a superiority design that was approved by the institutional ethics committees and registered with CTRI (CTRI/2021/08/036002) conducted in India under the aegis of Cancer Research Statistics Foundation. The study recruited LAHNSCC with a recurrent and metastatic disease that either was platinum-refractory or was planned for second-line chemotherapy. The key inclusion criteria were: Age ≥18 years, ECOG PS 0-2, and the presence of normal hematological and biochemical parameters. These patients underwent 1:1 central stratified randomization (Stratification - site of disease &amp; ECOG PS) to either triple metronomic chemotherapy (methotrexate 9 mg/m2 PO weekly, celecoxib 200 mg PO twice daily and erlotinib 150 mg PO once daily) or physician choice therapy ( nivolumab or pembrolizumab or cetuximab or taxane or afatinib or 5-FU or capecitabine). The study drugs were administered either till disease progression or the development of intolerable side effects. The primary endpoint of the study is overall survival (OS). The secondary endpoints are adverse events (CTCAE version 5.0), progression-free survival (PFS), and quality of life (EORTC). The sample size required was 114. The OS and PFS were estimated using Kaplan-Meier method and were compared using the log-rank test. Cox proportional hazard model was constructed for the calculation of the hazard ratio. The adverse events were compared using Fisher's test. A p-value of 0.05 was considered significant. Results: At a median follow-up of 258 (95% CI 209-306) days.The median overall survival of the triple metronomic chemotherapy was 181 days (95%CI 142.7-219.2) versus 123 days (95%CI 94-152) in the physician choice therapy arm (P=.0.002). The corresponding hazard ratio of death was 0.58 (95%CI 0.33-0.79, P=0.003). The 6-month OS was 52.9% (95%CI 36.9-65.1) versus 14.8% (95%CI 6.4-26.4). The median progression-free survival was 120 days (95%CI 89.2-150.8) versus 70 days (95% CI 58.2-81.8) in metronomic chemotherapy and in the physician choice therapy arms respectively (P=0.000). The corresponding hazard ratio of progression was 0.5 (95%CI 0.33-0.74, P=0.001). Conclusions: In this phase 3 multicentric study, triple metronomic chemotherapy as second-line therapy had an overall survival and progression-free survival advantage over NCCN-recommended physician choice therapy. Clinical trial information: CTRI/2021/08/036002 .

Expert Algorithm for Substance Identification Using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory.

This study aims to resolve one of the longest-standing problems in mass spectrometry, which is how to accurately identify an organic substance from its mass spectrum when a spectrum of the suspected substance has not been analyzed contemporaneously on the same instrument. Part one of this two-part report describes how Rice-Ramsperger-Kassel-Marcus (RRKM) theory predicts that many branching ratios in replicate electron-ionization mass spectra will provide approximately linear correlations when analysis conditions change within or between instruments. Here, proof-of-concept general linear modeling is based on the 20 most abundant fragments in a database of 128 training spectra of cocaine collected over 6 months in an operational crime laboratory. The statistical validity of the approach is confirmed through both analysis of variance (ANOVA) of the regression models and assessment of the distributions of the residuals of the models. General linear modeling models typically explain more than 90% of the variance in normalized abundances. When the linear models from the training set are applied to 175 additional known positive cocaine spectra from more than 20 different laboratories, the linear models enabled ion abundances to be predicted with an accuracy of <2% relative to the base peak, even though the measured abundances vary by more than 30%. The same models were also applied to 716 known negative spectra, including the diastereomers of cocaine: allococaine, pseudococaine, and pseudoallococaine, and the residual errors were larger for the known negatives than for known positives. The second part of the manuscript describes how general linear regression modeling can serve as the basis for binary classification and reliable identification of cocaine from its diastereomers and all other known negatives.

Discerning of isatin-based monoamine oxidase (MAO) inhibitors for neurodegenerative disorders by exploiting 2D, 3D-QSAR modelling and molecular dynamics simulation

Almost a billion people worldwide suffer from neurological disorders, which pose public health challenges. An important enzyme that is well-known for many neurodegenerative illnesses is monoamine oxidase (MAO). Although several promising drugs for the treatment of MAO inhibition have recently been examined, it is still necessary to identify the precise structural requirements for robust efficacy. Atom-based, field-based, and GA-MLR (genetic algorithm multiple linear regression) models were created for this investigation. All of the models have strong statistical (R 2 and Q 2) foundations because of both internal and external validation. Our dataset’s molecule has a higher docking score than safinamide, a well-known and co-crystallized MAO-B inhibitor, as we also noticed. Using the SwissSimilarity platform, we further inquired which of our docked molecules would be the best for screening. We chose ZINC000016952895 as the screen molecule with the best binding docking score (XP score = −13.3613). Finally, the 100 ns for the ZINC000016952895-MAO-B complex in our MD investigations is stable. For compounds that we hit, also anticipate ADME properties. Our research revealed that the successful compound ZINC000016952895 might pave the way for the future development of MAO inhibitors for the treatment of neurological disease. Communicated by Ramaswamy H. Sarma

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

BackgroundIn high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.MethodsAdvances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 “High-dimensional data” of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.ResultsThe paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.ConclusionsThis review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.