Statistical Challenges Research Articles

Principal microbial groups: compositional alternative to phylogenetic grouping of microbiome data.

Statistical and machine learning techniques based on relative abundances have been used to predict health conditions and to identify microbial biomarkers. However, high dimensionality, sparsity and the compositional nature of microbiome data represent statistical challenges. On the other hand, the taxon grouping allows summarizing microbiome abundance with a coarser resolution in a lower dimension, but it presents new challenges when correlating taxa with a disease. In this work, we present a novel approach that groups Operational Taxonomical Units (OTUs) based only on relative abundances as an alternative to taxon grouping. The proposed procedure acknowledges the compositional data making use of principal balances. The identified groups are called Principal Microbial Groups (PMGs). The procedure reduces the need for user-defined aggregation of $\textrm{OTU}$s and offers the possibility of working with coarse group of $\textrm{OTU}$s, which are not present in a phylogenetic tree. PMGs can be used for two different goals: (1) as a dimensionality reduction method for compositional data, (2) as an aggregation procedure that provides an alternative to taxon grouping for construction of microbial balances afterward used for disease prediction. We illustrate the procedure with a cirrhosis study data. PMGs provide a coherent data analysis for the search of biomarkers in human microbiota. The source code and demo data for PMGs are available at: https://github.com/asliboyraz/PMGs.

Iterative spatial leave-one-out cross-validation and gap-filling based data augmentation for supervised learning applications in marine remote sensing

ABSTRACT In marine remote sensing, supervised learning can link variables measured in-situ near the ocean surface to variables that can be measured from space. However, the in-situ data used for training and validating such empirical satellite algorithms are often spatially auto-correlated and clustered, giving rise to various statistical challenges such as overfitting to spatial structures. Furthermore, co-located in-situ and satellite measurements are rare in the oceans because of the cost of data collection from research vessels and frequent cloud cover. We propose two methods to mitigate these challenges. The first method builds on spatial leave-one-out cross-validation (SLOOCV), an approach designed to provide sound error estimates when data are spatially auto-correlated by enforcing a minimum separation distance between training and test observations. However, estimating this distance may be impossible with sparse and spatially clustered data. We hence propose to iterate and integrate error estimates over a range of separation distances (iSLOOCV). To address the often-small size of labeled data sets based on marine in-situ data, we tested if increasing the number of observations for algorithm training by means of cloud-filling algorithms for marine satellite data improved predictions. The potential of these two methods is demonstrated by developing empirical algorithms for mapping the proportions of seven diagnostic pigments (DPs) that serve as proxies for phytoplankton community composition in the northern Gulf of Mexico. We estimated the prediction accuracy of 13 algorithms with iSLOOCV, using various sets of satellite data products as input, and found adequate algorithms for 4 of the 7 DPs. Random forests combining ocean color and environmental variables as input had the lowest prediction errors overall. Correlations between predictions and observations estimated by iSLOOCV ranged from 0.69 to 0.85 and mean absolute errors from 0.02 to 0.13. Daily maps and longer-term composites of these DPs were broadly consistent with previously published results. Overall, errors increased when extrapolating over larger distances, highlighting how iSLOOCV can illuminate changes in algorithm performance based on sub-regional data coverage. Generating larger training sets by prior gap-filling substantially improved all error measures for 3 of the 7 DPs, with mixed results for the others. Therefore, data augmentation by gap-filling of satellite data should not be used as a default approach but can be a useful tool when supervised learning applications are suspected to be limited by the size of the training set.

Delivering spatially comparable inference on the risks of multiple severities of respiratory disease from spatially misaligned disease count data.

Population-level disease risk varies between communities, and public health professionals are interested in mapping this spatial variation to monitor the locations of high-risk areas and the magnitudes of health inequalities. Almost all of these risk maps relate to a single severity of disease outcome, such as hospitalization, which thus ignores any cases of disease of a different severity, such as a mild case treated in a primary care setting. These spatially-varying risk maps are estimated from spatially aggregated disease count data, but the set of areal units to which these disease counts relate often varies by severity. Thus, the statistical challenge is to provide spatially comparable inference from multiple sets of spatially misaligned disease count data, and an additional complexity is that the spatial extents of the areal units for some severities are partially unknown. This paper thus proposes a novel spatial realignment approach for multivariate misaligned count data, and applies it to the first study delivering spatially comparable inference for multiple severities of the same disease. Inference is via a novel spatially smoothed data augmented MCMC algorithm, and the methods are motivated by a new study of respiratory disease risk in Scotland in2017.

Statistical Integration of Heterogeneous Omics Data: Probabilistic Two-Way Partial Least Squares (PO2PLS)

AbstractThe availability of multi-omics data has revolutionized the life sciences by creating avenues for integrated system-level approaches. Data integration links the information across datasets to better understand the underlying biological processes. However, high dimensionality, correlations and heterogeneity pose statistical and computational challenges. We propose a general framework, probabilistic two-way partial least squares (PO2PLS), that addresses these challenges. PO2PLS models the relationship between two datasets using joint and data-specific latent variables. For maximum likelihood estimation of the parameters, we propose a novel fast EM algorithm and show that the estimator is asymptotically normally distributed. A global test for the relationship between two datasets is proposed, specifically addressing the high dimensionality, and its asymptotic distribution is derived. Notably, several existing data integration methods are special cases of PO2PLS. Via extensive simulations, we show that PO2PLS performs better than alternatives in feature selection and prediction performance. In addition, the asymptotic distribution appears to hold when the sample size is sufficiently large. We illustrate PO2PLS with two examples from commonly used study designs: a large population cohort and a small case–control study. Besides recovering known relationships, PO2PLS also identified novel findings. The methods are implemented in our R-package PO2PLS.

MCCLESKEY ACCUSED Justice Powell and The Moral Price of Institutional Pride

<i>MCCLESKEY</i> ACCUSED Justice Powell and The Moral Price of Institutional Pride

Modeling Determinants of Challenge in Learning Statistics in Time of the COVID-19 Pandemic

During the COVID-19 pandemic, there were a lot of challenges encountered by students in learning statistics online that affected their cognitive attitudes. This study aims to evaluate the different determinants that significantly influenced the engineering students' level of challenge in learning statistics in the new normal with the aid of a structured questionnaire by means of a Google form survey. Descriptive statistics and multiple linear regression analysis were employed to extract meaningful information from the gathered data. Results showed that the students' perception score for the level of challenge in learning statistics is 7.37 (1.99), which can be interpreted as "challenging." This implies that students face challenges as they learn statistics lessons amid the pandemic setup. The regression models constructed have revealed that "age", "sex", "learning environment", "money spent on internet load", "physical health", and "creativity of statistics lessons" are the significant causal factors of the level of challenge in learning statistics. Conclusively, statistics teachers must adjust and be considerate to their students in regard to their learning needs in line with the pandemic setup. The government also must provide a budget for seminars and training to college teachers concerning distance education. Furthermore, it is recommended that statistics teachers must create an interesting learning environment to fully catch students' attention despite challenges.

Meta-analyzing individual participant data from studies with complex survey designs: A tutorial on using the two-stage approach for data from educational large-scale assessments.

Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences. Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational large-scale assessments (ELSAs) or social, health, and economic survey and panel studies. The meta-analytic integration of these results offers unique and novel research opportunities to provide strong empirical evidence of the consistency and generalizability of important phenomena and trends. Using ELSAs as an example, this tutorial offers methodological guidance on how to use the two-stage approach to IPD meta-analysis to account for the statistical challenges of complex survey designs (e.g., sampling weights, clustered and missing IPD), first, to conduct descriptive analyses (Stage 1), and second, to integrate results with three-level meta-analytic and meta-regression models to take into account dependencies among effect sizes (Stage 2). The two-stage approach is illustrated with IPD on reading achievement from the Programme for International Student Assessment (PISA). We demonstrate how to analyze and integrate standardized mean differences (e.g., gender differences), correlations (e.g., with students' socioeconomic status [SES]), and interactions between individual characteristics at the participant level (e.g., the interaction between gender and SES) across several PISA cycles. All the datafiles and R scripts we used are available online. Because complex social, health, or economic survey and panel studies share many methodological features with ELSAs, the guidance offered in this tutorial is also helpful for synthesizing research evidence from these studies.

Computationally efficient methods for large-scale atmospheric inverse modeling

Abstract. Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges for standard inverse modeling frameworks that were often originally designed with much smaller data sets in mind. In this article, we discuss computationally efficient methods for large-scale atmospheric inverse modeling and focus on addressing some of the main computational and practical challenges. We develop generalized hybrid projection methods, which are iterative methods for solving large-scale inverse problems, and specifically we focus on the case of estimating surface fluxes. These algorithms confer several advantages. They are efficient, in part because they converge quickly, they exploit efficient matrix–vector multiplications, and they do not require inversion of any matrices. These methods are also robust because they can accurately reconstruct surface fluxes, they are automatic since regularization or covariance matrix parameters and stopping criteria can be determined as part of the iterative algorithm, and they are flexible because they can be paired with many different types of atmospheric models. We demonstrate the benefits of generalized hybrid methods with a case study from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. We then address the more challenging problem of solving the inverse model when the mean of the surface fluxes is not known a priori; we do so by reformulating the problem, thereby extending the applicability of hybrid projection methods to include hierarchical priors. We further show that by exploiting mathematical relations provided by the generalized hybrid method, we can efficiently calculate an approximate posterior variance, thereby providing uncertainty information.

Challenges in dental statistics: survey methodology topics

&#x0D; This paper gathers some contributions concerning survey methodology in dental research, as discussed during the first Workshop of the SISMEC STATDENT working group on statistical methods and applications in dentistry, held in Ancona on the 28th September 2011.&#x0D; The first contribution deals with the European Global Oral Health Indicators Development (EGOHID) Project which proposed a comprehensive and standardized system of epidemiological tools (questionnaires and clinical forms) for national data collection on oral health in Europe. The second contribution regards the design and conduct of trials to evaluate the clinical efficacy and safety of toothbrushes and mouthrinses. Finally, a flexible and effective tool used to trace dental age reference charts tailored to Italian children is presented.&#x0D;

Data Poisoning Attacks on Federated Machine Learning

Federated machine learning which enables resource-constrained node devices (e.g., Internet of Things (IoT) devices and smartphones) to establish a knowledge-shared model while keeping the raw data local, could provide privacy preservation, and economic benefit by designing an effective communication protocol. However, this communication protocol can be adopted by attackers to launch data poisoning attacks for different nodes, which has been shown as a big threat to most machine learning models. Therefore, we in this article intend to study the model vulnerability of federated machine learning, and even on IoT systems. To be specific, we here attempt to attacking a popular federated multitask learning framework, which uses a general multitask learning framework to handle statistical challenges in the federated learning setting. The problem of calculating optimal poisoning attacks on federated multitask learning is formulated as a bilevel program, which is adaptive to the arbitrary selection of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target</i> nodes and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source attacking</i> nodes. We then propose a novel systems-aware optimization method, called as attack on federated learning (AT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FL), to efficiently derive the implicit gradients for poisoned data, and further attain optimal attack strategies in the federated machine learning. This is an earlier work, to our knowledge, that explores attacking federated machine learning via data poisoning. Finally, experiments on several real-world data sets demonstrate that when the attackers directly poison the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target</i> nodes or indirectly poison the related nodes via using the communication protocol, the federated multitask learning model is sensitive to both poisoning attacks.

A first step towards best practice recommendations for the design and statistical analyses of pragmatic clinical trials: A modified Delphi approach.

Pragmatic clinical trials (PCTs) are randomized trials implemented through routine clinical practice, where design parameters of traditional randomized controlled trials are modified to increase generalizability. However, this may introduce statistical challenges. We aimed to identify these challenges and discuss possible solutions leading to best practice recommendations for the design and analysis of PCTs. A modified Delphi method was used to reach consensus among a panel of 11 experts in clinical trials and statistics. Statistical issues were identified in a focused literature review and aggregated with insights and possible solutions from experts collected through a series of survey iterations. Issues were ranked according to their importance. Twenty-seven articles were included and combined with experts' insight to generate a list of issues categorized into participants, recruiting sites, randomization, blinding and intervention, outcome (selection and measurement) and data analysis. Consensus was reached about the most important issues: risk of participants' attrition, heterogeneity of "usual care" across sites, absence of blinding, use of a subjective endpoint and data analysis aligned with the trial estimand. Potential issues should be anticipated and preferably be addressed in the trial protocol. The experts provided solutions regarding data collection and data analysis, which were considered of equal importance. A set of important statistical issues in PCTs was identified and approaches were suggested to anticipate and/or minimize these through data analysis. Any impact of choosing a pragmatic design feature should be gauged in the light of the trial estimand.

Statistical perspectives on reliability of artificial intelligence systems

Artificial intelligence (AI) systems are increasingly popular in many applications. Nevertheless, AI technologies are still developing, and many issues need to be addressed. Among those, the reliability of AI systems needs to be demonstrated so that AI systems can be used with confidence by the general public. In this paper, we provide statistical perspectives on the reliability of AI systems, focusing on the time dimension. That is, the system can perform its designed functionality for the intended period of time. We introduce a so-called “SMART” statistical framework for AI reliability research, which includes five components: Structure of the system, Metrics of reliability, Analysis of failure causes, Reliability assessment, and Test planning. We review traditional methods in reliability data analysis and software reliability, and discuss how those existing methods can be transformed for reliability modeling and assessment of AI systems. Different from traditional reliability studies, the focus of AI reliability is on the software system to include the training data. Thus, we describe recent developments in modeling and analysis of AI reliability for software systems. The paper outlines statistical research challenges in this area, including out-of-distribution detection, the effect of the training set, adversarial attacks, model accuracy, and uncertainty quantification. We discuss how those topics can be related to AI reliability, with illustrative examples. The final element of SMART (test planning), is critical for the demonstration of AI reliability. Therefore we discuss data collection and testing planning, highlighting methods for improving system design in order to achieve higher AI reliability. The paper closes with some concluding remarks.

SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data

Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy concerns, regulation restrictions, and commercial competition. Federated Learning is the de-facto standard for collaborative training of machine learning models over many distributed edge devices without the need for centralization. Nevertheless, training graph neural networks in a federated setting is vaguely defined and brings statistical and systems challenges. This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature. We provide convergence guarantees and empirically demonstrate the efficacy of our framework on a variety of non-I.I.D. distributed graph-level molecular property prediction datasets with partial labels. Our results show that SpreadGNN outperforms GNN models trained over a central server-dependent federated learning system, even in constrained topologies.

Separable expansions for covariance estimation via the partial inner product

Summary The nonparametric estimation of covariance lies at the heart of functional data analysis, whether for curve or surface-valued data. The case of a two-dimensional domain poses both statistical and computational challenges, which are typically alleviated by assuming separability. However, separability is often questionable, sometimes even demonstrably inadequate. We propose a framework for the analysis of covariance operators of random surfaces that generalizes separability while retaining its major advantages. Our approach is based on the expansion of the covariance into a series of separable terms. The expansion is valid for any covariance over a two-dimensional domain. Leveraging the key notion of the partial inner product, we generalize the power iteration method to general Hilbert spaces, and show how the aforementioned expansion can be efficiently constructed in practice at the level of the surface observations. Truncation of the expansion and retention of the leading terms automatically induces a nonparametric estimator of the covariance, whose parsimony is dictated by the truncation level. The resulting estimator can be calculated, stored and manipulated with little computational overhead relative to separability. Consistency and rates of convergence are derived under mild regularity assumptions, illustrating the trade-off between bias and variance regulated by the truncation level. The merits and practical performance of the proposed methodology are demonstrated in a comprehensive simulation study.

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.

Methods of analysis for survival outcomes with time-updated mediators, with application to longitudinal disease registry data.

Mediation analysis is a useful tool to illuminate the mechanisms through which an exposure affects an outcome but statistical challenges exist with time-to-event outcomes and longitudinal observational data. Natural direct and indirect effects cannot be identified when there are exposure-induced confounders of the mediator-outcome relationship. Previous measurements of a repeatedly-measured mediator may themselves confound the relationship between the mediator and the outcome. To overcome these obstacles, two recent methods have been proposed, one based on path-specific effects and one based on an additive hazards model and the concept of exposure splitting. We investigate these techniques, focusing on their application to observational datasets. We apply both methods to an analysis of the UK Cystic Fibrosis Registry dataset to identify how much of the relationship between onset of cystic fibrosis-related diabetes and subsequent survival acts through pulmonary function. Statistical properties of the methods are investigated using simulation. Both methods produce unbiased estimates of indirect and direct effects in scenarios consistent with their stated assumptions but, if the data are measured infrequently, estimates may be biased. Findings are used to highlight considerations in the interpretation of the observational data analysis.

Design and Analysis of Studies Based on Hierarchical Composite Endpoints: Insights from the DARE-19 Trial.

Background/Aim DARE-19 (NCT04350593) was a randomized trial studying the effects of dapagliflozin, an SGLT2 inhibitor, in hospitalized patients with COVID-19 pneumonia and cardiometabolic risk factors. The conduct of DARE-19 offered the opportunity to define an innovative and clinically meaningful endpoint in a new disease that would best reflect the known profile of dapagliflozin, accompanied by the statistical challenges of analysis and interpretation of such a novel endpoint.MethodsHierarchical composite endpoints (HCEs) are based on clinical outcomes which, unlike traditional composite endpoints incorporate ranking of components according to clinical importance. Design of an HCE requires the clinical considerations specific to the therapeutic area under study and the mechanism of action of the investigational treatment. Statistical aspects for the clinical endpoints include the proper definition of the estimand as suggested by ICH E9(R1) for the precise specification of the treatment effect measured by an HCE.ResultsWe describe the estimand of the DARE-19 trial, where an HCE was constructed to capture the treatment effect of dapagliflozin in hospitalized patients with COVID-19, and was analyzed using a win odds. Practical aspects of designing new studies based on an HCE are described. These include sample size, power, and minimal detectable effect calculations for an HCE based on the win odds analysis, as well as handling of missing data and the clinical interpretability of the win odds in relation to the estimand.ConclusionsHCEs are flexible endpoints that can be adapted for use in different therapeutic areas, with win odds as the analysis method. DARE-19 is an example of a COVID-19 trial with an HCE as one of the primary endpoints for estimating a clinically interpretable treatment effect in the COVID-19 setting.

A scalable quasi-Newton estimation algorithm for dynamic generalised linear models

ABSTRACT This research develops a scalable computing method based on quasi-Newton algorithm for several estimation problems in dynamic generalised linear models (DGLMs). The new method is developed by applying the principle of maximising a pointwise penalised quasi-likelihood (PPQ) for a DGLM for observed data often of massive size. Statistical and computational challenges involved in this development have been effectively tackled by exploiting the specific block structure and sparsity involved in the underlying projection matrix. The obtained maximum PPQ estimator of the state vector in the DGLM has been shown to be consistent and asymptotically normal under regularity conditions. Numerical studies and real data applications are conducted to assess the performance of the developed method.

County-Level Spatiotemporal Patterns of New HIV Diagnoses and Pre-exposure Prophylaxis (PrEP) Use in Mississippi, 2014–2018: A Bayesian Analysis of Publicly Accessible Censored Data

In the South region of the United States, HIV is disproportionately high and levels of pre-exposure prophylaxis (PrEP) use, which is highly effective in reducing the risk of acquiring HIV, are among the lowest across the country. Simultaneously examining the geographical distributions of both new HIV diagnoses and PrEP use as well as how they evolve over time at the county level is valuable for developing locally tailored intervention programs to target areas most in need of help. There is scant research on this topic using publicly accessible data sets, however, partly because of statistical challenges in modeling censored spatiotemporal data. This study fills this gap by applying a Bayesian spatiotemporal model to analyze interval-censored new HIV diagnoses and left-censored PrEP user data sets in Mississippi at the county level between 2014 and 2018. Suppressed values were modeled with Poisson distributions restricted to ranges where the possible values lie within. A simulation study indicates that the proposed model performs well in estimating censored values and regression coefficients as well as detecting hot spots. At the state level, new HIV diagnoses had a stable trend and PrEP use sharply increased during the study period. DeSoto and Hinds counties warrant special attention because their trends in new HIV diagnoses departed from the state-level trend. We demonstrate that publicly accessible, censored new HIV diagnosis and PrEP user data could be analyzed in ways that yield robust results, which can help health departments and other stakeholders more confidently identify areas that should be prioritized for aggressive HIV prevention.

DEMOGRAPHIC FACTORS AFFECTING NOCTURNAL BLOOD PRESSURE VARIABILITY IN THE GENERAL POPULATION: RESULTS OF A LARGE DIGITAL STUDY USING AN OPTICAL BLOOD PRESSURE MONITOR

Objective: The association between blood pressure variability (BPV) and increased cardiovascular risk is complex, and robust analyses have been limited by methodologic and statistical challenges. Short-term BVP has generally been assessed by ambulatory monitoring, and longer-term by repeated office measures. Recent advancements in optical sensors providing frequent and non-disruptive BP readings around the clock allow first characterization of night-to-night nocturnal BPV in a large population. Design and method: We nalysed 210’000 nocturnal systolic and diastolic BP (SBP, DBP) readings from 1’538 adults who wore the CE-marked Aktiia BP monitor for two weeks between April and November 2021 in Europe: mean age 56 ± 11 years, BMI 27 ± 5 Kg/m2, SBP 132 ± 14 mmHg, DBP 82 ± 10 mmHg; 15% females. For each user, we studied the variability of nocturnal BP over two weeks: we first averaged all BP readings from each night (midnight to 6am), and then calculated the standard deviation of the averaged values across nights. We finally adjusted a multivariate logistic regression to identify factors associated with high or low nocturnal BPV (defined as BPV above or below the mean of our population) based on gender, age, body mass index (BMI), and daytime home BP (measured twice by an upper-arm oscillometric cuff on day one of the monitoring period). This analysis was performed for both systolic and diastolic BPV. Results: High nocturnal systolic BPV was more likely in males (OR 1.37, CI:1.02–1.83, p = 0.035), and subjects younger than 55 years (OR 1.51, CI:1.23–1.86, p &lt; 0.001). High nocturnal diastolic BPV was more likely in females (OR 1.63, CI:1.23–2.16, p = 0.001), and subjects with a BMI below 30 kg/m2 (OR 1.71, CI:1.32–2.20, p &lt; 0.001). Daytime upper-arm cuff BP (systolic &gt; = 135mmHg or diastolic &gt; = 85mmHg) did not show a statistically significant effect in either of the two models (all p&gt; 0.2). Conclusions: This digital investigation showed greater night-to-night variability of nocturnal systolic BP in male and young subjects, and greater variability of nocturnal diastolic BP in females and non-obese subjects. The large-scale deployment of wearable medical devices that continuously record BP enables observations that may help elucidate adverse cardiovascular risk profiles in the general population.