Normality Assumption Research Articles

Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.

Parametric and nonparametric versions of adaptive CUSUM charts for monitoring the location of individual measurements

Control charts are customarily developed under the assumption of normal quality characteristics to be monitored. However, in many real dataset applications, the normality assumption is not easy to hold. The in-control (IC) robustness of the charting schemes has significant importance for the valid out-of-control (OOC) performance. This article intends to investigate the IC robustness of the unbiased-function-based adaptive cumulative sum (ACUSUM) chart and nonparametric ACUSUM (NPACUSUM) chart against the non-normal process distributions including symmetrical, skewed, and contaminated normal distributions. The OOC run length profiles of the ACUSUM and the proposed NPACUSUM charts for the individual measurements against the non-normal distributions are also a part of this study. The run length profiles of the proposed charting schemes have been computed using the Monte Carlo simulation method. The artificial datasets have been taken from some symmetric and skewed distributions to implement the proposals. An electrical engineering dataset has also been taken for the implementation of the proposal on a real dataset.

Travel Time Reliability Estimation in Urban Road Networks: Utilization of Statistics Distribution and Tensor Decomposition

The travel time reliability (TTR) is crucial for evaluating the reliability of road networks, but real traffic data is often incomplete and sparse. This study validates that road network TTR conforms to a normal distribution and devises a quantification approach for road network TTR. Two reliability estimation methods are tailored for two data sources: section detectors and mobile detectors. Simulation experiments have confirmed the effectiveness of these methods. The study emphasizes that the TTR estimation method using traffic section data (S-TTR), which is based on the verified normal distribution assumption, maintains average absolute errors below 10%. On the other hand, the TTR estimation method that utilizes sparse trajectory data (T-TTR), which relies on tensor decomposition, proficiently fills in all missing data with an average error of 0.0059.

A threshold mixed-effects Tobit model for treatment-sensitive subgroup identification based on longitudinal measures with floor and ceiling effects and a continuous covariate

In the era of personalized medicine, there is an increasing interest in the identification of patients who may benefit from or be sensitive to a specific type of treatment. Recently a threshold linear mixed model was proposed to identify treatment-sensitive subgroups based on a continuous covariate when longitudinal measurements are the outcomes of the study. This model assumes, however, a normal distribution for these measurements. In some studies, the longitudinal measurements are restricted in an interval and subject to floor and ceiling effects caused by a portion of subjects with measurements on the boundaries of the interval, which would violate the normality assumption. In this paper, a threshold mixed-effects Tobit model is introduced to overcome this problem. The proposed models and inference procedures are assessed through simulation studies, as well as an application to the analysis of data from a randomized clinical trial.

EWMA control charts based on robust estimators: A powerful tool for monitoring a process with a non-normal distribution

This paper presents a study on using memory-type control charts to detect small to moderate shifts in the process mean, particularly when the underlying distribution deviates from normal assumptions. To address this, robust estimators based on modified maximum likelihood estimation (MMLE) and Lloyd’s BLUE estimators using generalized least square estimation (GLSE) are proposed. The proposed estimators utilize auxiliary information to enhance sensitivity against abrupt changes in the mean. Additionally, they play a crucial role in refining the precision of the estimated mean, contributing to a more accurate and nuanced estimation of the true mean. The efficiency and robustness of these estimators are compared with conventional ones from the literature. The proposed robust estimators are integrated into the design of the exponentially weighted moving average (EWMA) control chart. Performance evaluation is based on average run lengths (ARLs), standard deviation, and percentiles of the run lengths. A real-life example illustrates the significance of the proposed estimators over conventional ones for non-normal distributions. The results demonstrate that the EWMA control chart with proposed estimators outperforms competitors in terms of ARLs, SDRLs, and percentiles. This study offers insights into using robust estimators to enhance memory-type control charts' performance for non-normal distributions.

A novel application of data-consistent inversion to overcome spurious inference in genome-wide association studies.

The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants may result in spurious conclusions about the associations between a variant and a trait. We propose an assumption-free model built upon data-consistent inversion (DCI), which is a recently developed measure-theoretic framework utilized for uncertainty quantification. This proposed DCI-derived model builds a nonparametric distribution on model inputs that propagates to the distribution of observed data without the required normality assumption of residuals in the regression model. This characteristic enables the proposed DCI-derived model to cover all genetic variants without emphasizing on additivity of the classic-GWAS model. Simulations and a replication GWAS with data from the COPDGene demonstrate the ability of this model to control the Type-I error rate at least as well as the classic-GWAS (additive linear model) approach while having similar or greater power to discover variants in different genetic modes of transmission.

Modified Levey-Jennings Chart with Robust Estimator: A Case of Semiconductor Manufacturing Process

In the era of Industrial Revolution 4.0 and smart manufacturing, the development and deployment of control charts used in the semiconductor industry need to be automated. Consequently, artificial intelligence-based automation methods typically encompass the deployment of statistical software such as JMP. Automation involves frequent dataset updates; the control limits are recalculated as the parameters change (non-stationary behaviour). This requires the user to define the control chart type before its deployment on the production floor. An initially normally distributed dataset may be skewed during the process owing to the influence of outliers. If a user selects a chart based on normality assumptions, detecting a process-mean shift may be impossible if the recalculated limit fluctuates. In the semiconductor industry, a process-mean shift occurs owing to special cause variations in the process. This signals process deterioration, which may affect the quality of the product. It is unknown when outliers will affect the equilibrium of normality assumptions; therefore, it is important to develop an automated, robust control chart that can detect special cause variations under non-stationary conditions. This study proposes the use of Huber’s M-estimators in the Levey-Jennings chart to detect special cause variations in a semiconductor manufacturing process. This study computes the robust M-estimates of all available samples to calculate the new limits in the Levey-Jennings chart. This new chart is referred to as the modified Levey-Jennings with a robust Huber M-estimator (MLVHM). Using production data from Dominant Opto Technologies Sdn. Bhd., Malaysia, a statistical comparison of the MLVHM and Levey-Jennings charts was performed. While the MLVHM is stable, the absolute difference in dispersion between the two charts ranges between 25.26% and 47.91% owing to standard deviation variation in the Levey-Jennings chart in non-stationary situations with outliers. The study concludes that the MLVHM chart is robust and suitable for industrial automatic flow applications.

Lamb feeding preference for maize, bean, and broad bean stubble treated with silage effluent, urea-molasses, or phosphoric acid

molasses (MaUM), silage effluent with phosphoric acid (MaEflAcP), and control (MaTes); bean stubble treated with silage effluent (FrEfl), effluent silage with urea-molasses (FrEflUM), and control (FrTes); and broad bean stubble treated with silage effluent and urea-molasses (HaEflUM), urea-molasses and phosphoric acid (HaUMAcP), and control (HaTes). Design/Methodology/Approach: Six Dorper/Katahdin lambs of 21 kg (LW) were used to carry out the feeding preference test. In the morning, lambs were fed with a mixture of the three treatments of each stubble (70% of the dry matter (DM) requirement/40 min); meanwhile, in the evenings, they were offered each treatment in independent feeders (10% of the DM requirement/20 min). This treatment lasted 7 days. Intake was measured in grams (offer minus rejection). Once the ordinal qualitative variables of consumption frequency (1=low, 2=medium, and 3=high) were classified, the data were analyzed using the ordinal logistic regression model. The results were expressed as the estimated proportion of incidence of the frequencies (1, 2, or 3). Results: The proportion estimated for the high consumption frequency recorded higher results for MaUM, HaUMAcP, and FrEflUM, reaching 95.24, 73.44, and 43.49%, respectively. The proportion estimated for the low consumption frequency for HaTes, MaTes, and FrTes was 95.12, 88.11, and 48.77%, respectively. Study Limitations/Implications: The diets with different quality and flavors did not meet the assumptions of normality and homoscedasticity; however, the results provide valuable information for the improvement of dry matter intake and animal production. Finding/Conclusions: Urea-molasses (UM) promoted consumption preference both alone or in combination with other ingredients, in all the stubbles tested.

The effect of the COVID-19 pandemic on TOM in the housing market

Purpose The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations. Design/methodology/approach Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present. Findings This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated. Originality/value This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

Statistical considerations in model-based dose finding for binary responses under model uncertainty.

The statistical methodology for model-based dose finding under model uncertainty has attracted increasing attention in recent years. While the underlying principles are simple and easy to understand, developing and implementing an efficient approach for binary responses can be a formidable task in practice. Motivated by the statistical challenges encountered in a phase II dose finding study, we explore several key design and analysis issues related to the hybrid testing-modeling approaches for binary responses. The issues include candidate model selection and specifications, optimal design and efficient sample size allocations, and, notably, the methods for dose-response testing and estimation. Specifically, we consider a class of generalized linear models suited for the candidate set and establish D-optimal designs for these models. Additionally, we propose using permutation-based tests for dose-response testing to avoid asymptotic normality assumptions typically required for contrast-based tests. We perform trial simulations to enhance our understanding of these issues.

Feasibility of diabetes self-management coaching program for individuals with type 2 diabetes in the Ethiopian primary care setting: a protocol for a feasibility mixed-methods parallel-group randomized controlled trial.

Diabetes mellitus is the third most prevalent chronic metabolic disorder and a significant contributor to disability and impaired quality of life globally. Diabetes self-management coaching is an emerging empowerment strategy for individuals with type 2 diabetes, enabling them to achieve their health and wellness goals. The current study aims to determine the feasibility of a diabetes self-management coaching program and its preliminary effectiveness on the clinical and psychosocial outcomes in the Ethiopian primary healthcare context. The study will employ a mixed-method feasibility randomized controlled trial design. Forty individuals with type 2 diabetes will be randomly allocated to treatment and control groups using block randomization. The primary feasibility outcomes include acceptability, eligibility, recruitment, and participant retention rates, which will be computed using descriptive analysis. The secondary outcomes are self-efficacy, self-care activity, quality of life, and glycated hemoglobin A1c. For normally distributed continuous variables, the mean difference within and between the groups will be determined by paired sample Student t-test and independent sample Student t-test, respectively. Non-parametric tests such as the Mann-Whitney U test, the Wilcoxon signed rank test, and the Friedman analysis of variance test will determine the median difference for variables that violated the normality assumption. A repeated measure analysis of variance will be considered to estimate the variance between the baseline, post-intervention, and post-follow-up measurements. A sample of 10 volunteers in the treatment group will participate in the qualitative interview to explore their experience with the diabetes self-management coaching program and overall feasibility. The study will follow a qualitative content analysis approach to analyze the qualitative data. Qualitative and quantitative findings will be integrated using a joint display technique. Evidence reveals diabetes self-management coaching programs effectively improve HbA1c, self-efficacy, self-care activity, and quality of life. This study will determine the feasibility of a future large-scale randomized controlled trial on diabetes self-management coaching. The study will also provide evidence on the preliminary outcomes and contribute to improving the diabetes self-management experience and quality of life of individuals with type 2 diabetes. The trial was registered online at ClinicalTrials.gov on 12/04/2022 and received a unique registration number, NCT05336019, and the URL of the registry is https://beta. gov/study/NCT05336019 .

Areas of professional life and job satisfaction of nurses.

Job satisfaction among nurses is closely related to work environment as well as organizational and professional commitment. Satisfaction is a concept derived from Latin, where "satis" means "enough," as much as is needed to fully satisfy expectations, needs, aspirations, in such a way that there is no room for complaint. Job satisfaction, on the other hand, is formulated as a positive attitude of employees toward the duties of the job, the work environment and other employees. The aim of this paper was to demonstrate how the different areas of nurses' professional life, i.e., workload, control, rewards, community, sense of justice and values, correlate with their perceived job satisfaction. A cross-sectional study was conducted in a group of 509 nurses working in a public hospital in Poland. Data were collected using a survey questionnaire, which consisted of a section containing sociodemographic data and standardized instruments: The Minnesota Satisfaction Questionnaire (MSQ) and The Areas of Worklife Survey (AWS) developed by Maslach and Leiter. Correlations were made using Spearman's rho coefficient. The calculations also used stepwise linear regression analysis after checking certain assumptions, including checking the assumption of normality of residuals and the Durbin-Watson Test. The mean score for the 20 items of the MSQ questionnaire ranged from 3.05 to 3.43 on a 5-point Likert scale. Support from the interdisciplinary team, which concerned assessing the quality of the social environment in the workplace, cooperation and showing positive feelings received the highest rating among respondents (3.51 ± 0.76). The sense of fair treatment at work averaged 3.26 ± 0.58. The area of value conflict within the organization itself or between the employee's values and those of the organization, respondents rated an average of 3.26 ± 0.65. The mean score for all areas of professional work in the surveyed group was 3.09 ± 0.45. As satisfaction in particular areas of work life increases, so does the level of satisfaction in such aspects of work as achievement and a sense of fairness. The higher the level of satisfaction in the area of control, the more the sense of satisfaction with independence increases. The higher the satisfaction of respondents in the areas of values, workload and control, the higher the level of satisfaction with working conditions occurs.

Effect of the Healthy Dish Method to Reduce Waist Circumference in Teachers with Abdominal Obesity of a Peruvian University.

Obesity has increased steadily in recent decades, becoming a worldwide pandemic. To determine the effect of the "Healthy Dish" method to reduce waist circumference in teachers with abdominal obesity in a Peruvian University. Quasi-experimental, prospective study in 43 teachers, divided into 21 in the control group and 22 in the experimental group, who received a two-session workshop on abdominal obesity and the "Healthy Dish" method. The latter was applied for 2 months, monitored 5 days a week, the waist circumference of both groups was measured at one month and at two months and compared. For the statistical analysis, descriptive and inferential statistics or nonparametric statistics were applied (Mann-Whitney U-test, according to the assumption of normality). The predominant age range was 40 to 49 years, women, married, overweight, with a family history of hypertension, cancer and diabetes mellitus. After the intervention, 40.9% of teachers in the experimental group decreased their waist circumference and 54.5% normalized it, while the control group decreased it by 23.8% and 47.6% normalized it. Only 4.54% of teachers in the experimental group showed no change in their waist circumference, compared to 19.0% in the control group, and 9.52% of teachers in the control group increased their waist circumference. The healthy dish method has a positive effect on decreasing waist circumference in teachers with abdominal obesity.

Note on singular Sturm comparison theorem and strict majorant condition

In this note we present a singular Sturm comparison theorem for two linear Hamiltonian systems satisfying a standard majorant condition and the identical normality assumption. Both endpoints of the considered interval may be singular. We identify the exact form of the strict majorant condition, which is necessary and sufficient for the property that every solution (conjoined basis) of the majorant system has more focal points than the solutions of the minorant system. We provide a formula for the exact number of focal points of any solution of the majorant system, depending on the number of focal points of solutions of the minorant system and on the number of right focal points of a solution of a certain transformed linear Hamiltonian system. This transformed system may be in general abnormal. Our result extends the previous Sturm comparison theorems for linear Hamiltonian systems by Kratz (1995) on a compact interval and by the authors (2020) on an open or unbounded interval. The main result is also new for the second order differential equations, where it extends the singular comparison theorem by Aharonov and Elias (2010).

The impact of natural constraints in linear regression of log transformed response variables

Abstract In linear regression, log transforming the response variable is the usual workaround regarding departures from the assumption of normality. However, the response variable is often subject to natural constraints, which can result in a truncated distribution of the residual errors on the log scale. In forestry, allometric relationships and tree growth are two typical examples a natural constraint; the response variable cannot be negative. Traditional least squares estimators do not account for constrained response variables. For this study, a modified maximum likelihood (MML) estimator that takes natural constraints into account was developed. This estimator was tested through a simulation study and showcased with black spruce tree diameter increment data. Results show that the ordinary least squares estimator underestimated large conditional expectations of the response variable on the original scale. In contrast, the MML estimator showed no evidence of bias for large sample sizes. Departures from distributional assumptions cannot be overlooked when the model is used for predictive purposes. Both Monte Carlo error propagation and prediction intervals rely on these assumptions. In this context, the MML estimator developed for this study can be used to properly propagate the errors and produce reliable prediction intervals.

중국 예비유아교사 직업정체성 척도 개발 및 타당화

The purpose of this study is to explore the components of preservice early childhood teachers' professional identity in China and to develop a valid and reliable scale to measure the level of preservice early childhood teachers' professional identity.
 To identify the factors that constitute preservice early childhood teachers' professional identity, 13 subscales and 62 preliminary items were extracted through literature analysis and in-depth interviews. A Delphi survey was conducted to verify the appropriateness of the items by experts, and exploratory factor analysis and reliability analysis were conducted to determine the final five components and 33 items.
 The final item of the preservice early childhood teacher professional identity Scale was used to test the item favorability, validity, and reliability. The results of the item validity analysis showed that all items met the assumption of normality and showed good item discrimination. Confirmatory factor analysis (CFA) was conducted to verify validity, and the model fit of the scale (RMSEA .059) was found to be adequate. In addition, the standardization coefficient (.636 or higher) and average variance extracted (AVE) values of each construct's convergent validity were checked and verified through the covariance correlation between the discriminant validity components. Due to the high correlation of the constructs, multicollinearity among the constructs was tested to confirm their independence. The factor reliability of the scale is more than .867, and the total item reliability is .964, which is very high.
 The preservice early childhood teachers' professional identity scale developed in this study proved to be a valid and reliable scale to measure the level of preservice early childhood teachers' professional identity in various training programs in China.

Supplemental Material for Normality Assumption in Latent Interaction Models

Supplemental Material for Normality Assumption in Latent Interaction Models

A redescending M-estimator approach for outlier-resilient modeling

The OLS model is built on the assumption of normality in the distribution of error terms. However, this assumption can be easily violated, especially when there are outliers in the data. A single outlier can disrupt the normality assumption of error terms, making the OLS model less effective. In such situations, M-estimators (MEs) come into play to obtain reliable estimates. We introduce a redescending M-estimators (RME) for robust regression to handle datasets with outliers. The proposed RME produces more robust estimates by effectively managing the influence of outliers, even at lower values of the tuning constant. We compared the performance of this estimator with existing RMEs using real-life data examples and an extensive simulation study. The results show that our suggested RME is more efficient than the compared ME in various situations.

Application of Six Sigma methodology in the analysis of variance: process shift versus inflation coefficient

Purpose The purpose of this article is to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the analysis of variance (ANOVA) application after the preliminary test on the model specification.Design/methodology/approach A new approach is proposed to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the ANOVA application. First, we determine this relationship from the general perspective of Six Sigma methodology under the normality assumption. Then, the approach is extended to a balanced two-stage nested design with a random effects model in which a preliminary test is used to fix the main test statistic.Findings The features of mean-shifted and inflated (but centred) processes with the same specification limits from the perspective of Six Sigma are studied. The shift and inflation coefficients are derived for the two-stage balanced ANOVA model. We obtained good predictions for the process shift, given the inflation coefficient, which has been demonstrated using numerical results and applied to case studies. It is understood that the proposed method may be used as a tool to obtain an efficient variance estimator under mean shift.Research limitations/implications In this work, as a new research approach, we studied the link between mean shift and inflation coefficients when the underlying null hypothesis is rejected in the ANOVA. Derivations for these coefficients are presented. The results when the null hypothesis is accepted are also studied. This needs the help of preliminary tests to decide on the model assumptions, and hence the researchers are expected to be familiar with the application of preliminary tests.Practical implications After studying the proposed approach with extensive numerical results, we have provided two practical examples that demonstrate the significance of the approach for real-time practitioners. The practitioners are expected to take additional care before deciding on the model assumptions by applying preliminary tests.Originality/value The proposed approach is original in the sense that there have been no similar approaches existing in the literature that combine Six Sigma and preliminary tests in ANOVA applications.

Abstract 2140: Untargeted lipidomics reveals racial differences in lipid species among women

Abstract Introduction: Lipids are important for many cellular processes and have been implicated in cancer development. Adopting a lipidomic approach has revealed sex-specific differences in lipid species but whether there are racial differences in lipid profiles remain unexplored. As a result, we investigated differences in lipid species between Non-Hispanic Black (NHB) vs Non-Hispanic White (NHW) women. Methods: We performed lipidomic profiling on 705 premenopausal women who were recruited during their annual screening mammogram at Washington University School of Medicine, St. Louis, MO. A total of 982 lipid species were profiled at Metabolon (Durham, NC®). 125 lipid species missing in 300 or more women were excluded from the analysis. For the remaining missing values, we employed the nearest neighbor method for imputation. We investigated the race-differentiating lipid species concentrations using linear regression models, adjusting for age and body mass index (BMI). To satisfy the assumptions of normality and homoscedasticity, we log-transformed the lipid concentrations before performing regression analyses. The linear regression coefficients were then back-transformed as percentage differences. We accounted for multiple testings by applying the Bonferroni correction. Statistical significance was defined as a Bonferroni-adjusted p-value < 10−5. Pathway enrichment analyses was conducted for differential lipid species for each sub pathway. Results: Of the 705 women, 506 were NHW and 163 were NHB. In analysis adjusted for age and BMI, 278 lipid species were significantly lower in NHB, and one lipid specie (TAG58:10-FA20:4) was higher in NHB. These species belonged to triacylglycerols (TAG, n=213), phosphatidylcholines (PC, n=25), diacylglycerols (DAG, n=22), cholesteryl esters (CE, n=4), phosphatidylethanolamines (PE, n=4), lysophosphatidylcholines (LPC, n=3), lysophosphatidylethanolamines (LPE, n=3), sphingomyelins (SM, n=2), ceramides (CER, n=1), lactosylceramides (LCER, n=1) and phosphatidylinositols (PI, n=1) sub-pathways. The TAG sub-pathway was enriched with differential lipid species (213 significant out of 518 species, p = 1.26 × 10−11). Forty-three lipid species (42 TAGs and one DAG) exhibited an absolute percentage difference > 50% between NHB and NHW women. The top 3 TAG species with the largest absolute percentage differences were TAG44:0-FA14:0 (60.6% lower in NHB women, p = 6.25 × 10−8), TAG46:2-FA16:1 (59.8% lower in NHB women, p = 1.11 × 10−14) and TAG47:2-FA16:1 (59.4% lower in NHB women, p = 8.66 × 10−20). Conversely, TAG58:10-FA20:4 was 47.1% (p = 5.79 × 10−8) higher in NHB women. Conclusions: Our study reveals novel substantial racial differences in lipid species among women, mostly in the TAG sub-pathway, highlighting the influence of race on the lipidome. Validation in a different study population is needed. Citation Format: ang Li, Ghazaleh Pourali, Kayla R. Getz, Myung Sik Jeon, Jingqin Luo, Chongliang Luo, Adetunji T. Toriola. Untargeted lipidomics reveals racial differences in lipid species among women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2140.