Real Data Analysis Research Articles

Goodness of fit test for a skewed generalized normal distribution

ABSTRACT The skewed generalized normal (SGN) distribution with four parameters is a versatile distribution that can effectively model data with skewness and heavy or light tails. In this paper, we conduct two classes of goodness of fit tests for the SGN distribution based on the empirical distribution function (edf) and the sample correlation coefficient. The first class involves transforming the sample into approximately mixed gamma observations, and then applying five classical parametric bootstrap edf-based goodness of fit tests. The second class is based on the inverse probability transformation and utilizes the sample correlation coefficient as the test statistic. We compare the finite sample performances of the proposed tests for different sample sizes and alternative distributions by extensive numerical studies. The simulation results demonstrate that the proposed tests provide a valid alternative to the standard tests using the original data, and the analysis of real data illustrates its application.

Abstract PO4-16-12: Patient initiated centricity towards improved communication, side effect management and follow-up care by Real World Evidence Survey from patients for patients – Preview of the Breast BRIDGister Platform

Abstract Background Treatment adherence is crucial for outcome improvement. Still up to 50% of patients interrupt e.g. endocrine treatment or other effective treatment choices due to side effects or miscommunication. Reasons may include limited time spent for patient information in daily routine and the intrinsic barriers to frankly tell the truth to the attending physicians. Therefore, a patient initiated survey has been compiled to comprehensively address all clinical, diagnostic and critical aspects of importance for patients by decentral data capture from patients at home via smart devices in a pseudonymized & interactive fashion to refocus on challenges regarding patients needs, fears and wishes to support evidence based statistical analysis of Real World Data. Materials and Methods Establishing a cloud-based IT Platform enabling nationwide, decentral data entry using Climedo clinical trial software tools without the need of any commercial app utilities and therefore obeying GDPR conform standards to create an interconnected online platform integrating 36 interconnected surveys comprising >1.200 singular questions has been set up between 2020 and 2022 based on patients content elaboration & wishes. The questionnaires address all Real World experiences e.g. at initial diagnosis, during primary treatment and all subsequent lines as well as side effect management with complementary medicine and follow up care realities. After 100 patients a preplanned statistical analysis has been performed to evaluate the feasibility of the patient oriented ePRO approach and demonstrate relevance of proof-of-concept results before entering international trial expansion. Descriptive analysis by Excel as well as Correlation analysis; Chi square and Partitioning tests using the SAS JMP® 9.0.0 software were performed. Results Despite in depth complexity > 85% of patients completed the whole survey. Age distribution of the pilot cohort revealed comparably younger patient age (28% between 30-39 years, 40% between 40-49 years, 24% between 50-59 and 10% being above 60 years) and dominance of Triple Negative Patients (~39%) participating the online survey in the pilot cohort. 97% of patients were treated in certified breast centers (50% in larger centers of bigger towns,15% in central clinics, 23% in university sites, 5% in local hospitals and 7% at office based doctors). Interestingly, 25% of patients did not or only partially understand their diagnosis and upcoming treatment. Time spent for communicating initial breast cancer diagnosis was less than 10 minutes for 25% of patients, while 13% did not remember. Exclusively one treatment option was discussed with 86% of patients. 78% have been informed about their initial breast cancer diagnosis in personal meetings, while 21% were informed by phone calls. 37% of patients had been informed about their patient rights. At preplanned interim analysis 38% of patients were lymphnode positive and 6% metastasized at initial diagnosis. 56% of patients were satisfied with their guideline conform follow-up-care, while 42% did pay for additional follow up care on their own. 85% of patients wished to have a risk-adopted, more individualized follow-up care. Conclusion Despite complexity and lengthiness > 85% of patients did complete the survey. There is an apparent need to have more time for initial confrontation with diagnosis and treatment options. Patients are frequently not informed about alternative treatment options as well as patient rights. Patients frequently feel under pressure in their life threatening situation and suffer substantial lack of information as well as appropriate personal communication. Patient centricity should focus on elaborated communication and information systems that support treatment decision acceptance and adherence. Further details analysis will become available in upcoming subanalysis of the Breast BRIDGister platform survey. Citation Format: Cornelia Stuewe, Ralph Wirtz, Christa Gress, Lena Schmidt, Veronika Schweighart, Sebastian Eidt, Claudia Schumacher, Julia Habraschka, Heidi Lehmann, Katrin Schuere. Patient initiated centricity towards improved communication, side effect management and follow-up care by Real World Evidence Survey from patients for patients – Preview of the Breast BRIDGister Platform [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-16-12.

Abstract PO1-04-12: Palbociclib versus ribociclib in first-line treatment of patients with hormone-receptor positive HER2 negative advanced breast cancer – real world outcome data from the German registry platform OPAL

Abstract Introduction CDK4/6 inhibitors (CDKi) plus endocrine therapy (ET) are standard of care in first-line (1L) treatment of hormone receptor-positive (HR+) HER2-negative (HER2-) advanced breast cancer (ABC). In the pivotal trials the three CDK4/6i (palbociclib, ribociclib and abemaciclib) + ET showed similar progression-free survival (PFS) benefit over ET alone. However, other than ribociclib, palbociclib failed to demonstrate overall survival (OS) benefit. As patient (pts) characteristics differed between the trials, especially in the number of pts with treatment-free interval (TFI) of < 12 months after end of adjuvant ET, head-to-head comparisons of the CDKi are of clinical interest. Here we analyze the outcome of pts treated with palbociclib + ET or ribociclib + ET with real-world data from OPAL. Methods OPAL (NCT03417115) is a prospective observational, open, longitudinal multicenter cohort study (clinical registry) in Germany. Pts with ABC and early breast cancer can be recruited at start of their first systemic treatment. There are no treatment restrictions. Sites from all medical sectors can participate in OPAL (medical and gynecologic oncologists from outpatient centers and hospitals). Details on all (sequential) treatments, patient and tumor characteristics, biomarker testing, clinical and patient-reported outcomes are collected. Follow-Up is until death or up to 5 years. Between 01/2018 and 07/2021 1049 pts with HR+ HER2- ABC were recruited by 143 sites. Database cut was on 31/08/2022. 384 pts received palbociclib + ET and 231 pts received ribociclib + ET as 1L treatment. All endocrine combination partners including switch of ET during first line therapy was allowed, whereas switch of CDK4/6i (n=25) were excluded. PFS in registries can differ from PFS in clinical trials, since the RECIST criteria are usually not applied in routine care. PFS in registries represents the time to clinically relevant progression in routine care. PFS and OS were estimated using the Kaplan-Meier method. To adjust for confounding, inverse probability of treatment weighting (IPTW) by propensity score analysis was used to compare PFS and OS between 1L palbociclib+ET and ribociclib+ET. IPTW was performed for the following variables: age, menopausal status, ECOG, any comorbidity, Charlson comorbidity index, metastatic stage, type of metastasis, number of metastatic sites, estrogen-/progesterone status, IHC status, previous chemo-/endocrine therapy, kind of endocrine combination partner, disease-free interval, and treatment-free interval. Results From 2018 to 2021, the proportion of CDK4/6i increased from 68% to 86% in 1L of HR+HER2- ABC. Overall, palbociclib was used in 44% and ribociclib in 26% of all first-line treatments in OPAL. Median age of pts receiving palbociclib/ribociclib was 66/68 years and for 77/81% of pts at least one comorbidity was documented. 37/35% of pts already had metastasis at diagnosis (M1) and 25/26% of pts had a TFI < 12 months. After IPTW, the two treatment groups were comparable for all tested characteristics. 42/46% of patients had a progression (palbociclib/ribociclib group). IPTW-adjusted median PFS was 25.1 months (95% Confidence interval (CI) 20.1 – 30.1) for the palbociclib and 27.0 months (95%-CI 21.1 – 31.6) for the ribociclib group. Hazard ratio was 0.91 (0.71 – 1.17) for palbociclib versus ribociclib. 26/29% of patients had an OS event (palbociclib/ribociclib group). IPTW-adjusted median OS was 36.7 months (95%-CI 33.0 – NA) for the palbociclib and 36.6 months (95%-CI 33.1 – NA) for the ribociclib group. Hazard ratio was 0.95 (0.69 - 1.30) for palbociclib versus ribociclib. Conclusions This analysis of real word data in the OPAL registry platform showed similar PFS and OS for palbociclib + ET compared to ribociclib + ET, when adjusted for a wide range of potential confounding variables. Further analyses will investigate whether one drug showed favorable results in certain subgroups of pts. Citation Format: Marc Thill, Mark-Oliver Zahn, Anja Welt, Arnd Nusch, Matthias Zaiss, Kathrin Engelken, Gabriele Kaltenecker, Kai Ringwald, Katja Gratzke, Lisa Kruggel, Martina Jänicke, Holger Schulz, Christoph Losem, Volker Hagen, Roland Fricker, Elmar Stickeler, Nadia Harbeck, Achim Wöckel, Thomas Decker. Palbociclib versus ribociclib in first-line treatment of patients with hormone-receptor positive HER2 negative advanced breast cancer – real world outcome data from the German registry platform OPAL [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-04-12.

Abstract PO5-17-08: Real world outcomes of neoadjuvant chemotherapy in ER-positive/PR-positive, HER2-negative breast cancer

Abstract Background: Neoadjuvant chemotherapy is utilized to downstage locally-advanced estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+), HER2-negative breast cancer despite modest pathological complete response (pCR) rates. The two most commonly utilized regimens include docetaxel plus cyclophosphamide (TC) or dose-dense doxorubicin and cyclophosphamide followed by paclitaxel (ddAC-T). Given few head-to-head comparisons of neoadjuvant TC with ddAC-T in this population, we performed a real world data analysis. Methods: 148 patients with ER+/PR+, HER2-negative breast cancer were included in the analysis. The patients were divided into two groups based on the neoadjuvant chemotherapy regimen used (TC vs ddAC-T) and the clinical responses to chemotherapy were categorized as complete, partial, no response/stable, or progressive disease. We examined the differences or associations in demographics, clinical characteristics, overall survival (OS), and invasive disease-free survival (IDFS) between TC and ddAC-T neoadjuvant chemotherapy. pCR was defined as the absence of invasive carcinoma in breast and lymph nodes (ypT0/ypTis and ypN0). Partial response was defined as a decrease in either or both T and/or N stage, compared to pre-treatment evaluation; no response was defined as no change in either T or N categories; and progressive disease was defined as an increase in either the T or N staging at time of pathologic evaluation. Results: The median age of participants was 55 years (range 28-78 years). 67% had clinical stage II disease, while 26% had stage III. 130 participants received neoadjuvant ddAC-T while 18 participants received TC. The ddAC-T group had 7% patients who achieved a pCR and 37% with a partial response, compared to the TC group with 0% of patients with a pCR and 33% with a partial response. The TC group had a higher rate of progressive disease, compared to the ddAC-T group (67% versus 20%). On univariate analysis, the choice of neoadjuvant chemotherapy was not predictive of pathologic response (p = 0.3864). As expected, premenopausal status was associated with a higher likelihood of a pathologic response with an odds ratio of 2.917 (95% CI: 1.465-5.809). On multivariate analysis, chemotherapy regimen was not predictive of pathologic response (p = 0.6047), after controlling the effects of age and menopausal status. No significant difference was noted in OS or IDFS at 24 and 48 months between the ddAC-T and TC groups. On multivariate analysis, controlling for BMI, ER, PR, race, and ECOG performance status, the choice of chemotherapy was not predictive of IDFS. Non-white participants appeared to have inferior IDFS (HR = 2.956, 95% CI: 1.208, 7.238, p-value = 0.0177). Conclusion: In this single-center retrospective analysis, we show real world outcomes of the use of neoadjuvant chemotherapy regimens among patients with ER+/PR+, HER2-negative breast cancer. Both ddAC-T and TC had low pCR rates, 5.98% and 0%, respectively. However, there was no statistical difference between IDFS or OS when comparing the two chemotherapy regimens. More data are needed to optimize neoadjuvant treatment in this patient population and to determine whether anthracyclines can be avoided. Table. Pathologic responses The results (summarized by odds ratio (OR) and its 95% CI) from logistic regression on the pathological responses (complete and partial). Citation Format: Matthew Kurian, Marcus Trybula, Kanchi Patell, Gregory Guzik, Seunghee Margevicius, Pingfu Fu, Alberto Montero, James Martin. Real world outcomes of neoadjuvant chemotherapy in ER-positive/PR-positive, HER2-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-17-08.

Optimizing Machine Selection and Cost of Quality: A Comprehensive Approach for Enhanced Industrial Efficiency and Economic Growth

Abstract: In today's modern industrial economy, selecting suitable machinery and efficiently managing quality costs are critical for achieving sustainable growth and competitiveness. This research paper presents a comprehensive approach to optimizing machine selection and cost of quality (COQ) within industrial operations. The study commences by delineating the criteria for machine selection and determining their respective weights using the Analytic Hierarchy Process (AHP). Subsequently, the VIKOR method is applied to select the most suitable machine based on the established criteria and weights. Moreover, the paper explores the concept of COQ, underscoring its importance as a performance measurement tool for organizations. The research investigates various strategies for minimizing quality-related expenses and maximizing benefits, including defect prevention, quality assurance, and continuous improvement initiatives. A case study analysis, focusing on the selection between mechanical cutting CNC machines and laser cutting CNC machines, provides practical insights into the implementation of the proposed methodologies. Real data analysis of cost and quality metrics, coupled with formula-based calculations, offers valuable insights into the decision-making process. The research underscores the significance of market analysis and leveraging modern technology trends to inform machine selection decisions. Overall, the findings contribute to enhancing industrial efficiency and promoting economic growth by facilitating informed decision-making in machine selection and COQ management

Imply: improving cell-type deconvolution accuracy using personalized reference profiles

Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson’s disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.

A Diagnostic Facet Status Model (DFSM) for Extracting Instructionally Useful Information from Diagnostic Assessment.

Modern assessment demands, resulting from educational reform efforts, call for strengthening diagnostic testing capabilities to identify not only the understanding of expected learning goals but also related intermediate understandings that are steppingstones on pathways to learning goals. An accurate and nuanced way of interpreting assessment results will allow subsequent instructional actions to be targeted. An appropriate psychometric model is indispensable in this regard. In this study, we developed a new psychometric model, namely, the diagnostic facet status model (DFSM), which belongs to the general class of cognitive diagnostic models (CDM), but with two notable features: (1) it simultaneously models students' target understanding (i.e., goal facet) and intermediate understanding (i.e., intermediate facet); and (2) it models every response option, rather than merely right or wrong responses, so that each incorrect response uniquely contributes to discovering students' facet status. Given that some combination of goal and intermediate facets may be impossible due to facet hierarchical relationships, a regularized expectation-maximization algorithm (REM) was developed for model estimation. A log-penalty was imposed on the mixing proportions to encourage sparsity. As a result, those impermissible latent classes had estimated mixing proportions equal to 0. A heuristic algorithm was proposed to infer a facet map from the estimated permissible classes. A simulation study was conducted to evaluate the performance of REM to recover facet model parameters and to identify permissible latent classes. A real data analysis was provided to show the feasibility of the model.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.

A Mallows-type model averaging estimator for de-noise linear models

. This article is concerned with model averaging for de-noise linear models, in which some covariates are not observed, but their ancillary variables are available. The least-squares-based estimation procedure is used to estimate the unknown regression parameter in each candidate model after the calibrated error-prone covariates are obtained. Then a Mallows-type weight choice criterion is constructed. When all candidate models are misspecified, the model averaging estimator is asymptotically optimal in the sense that achieving the lowest possible squared error. On the other hand, when the true model is included in the set of candidate models, the model averaging estimator of the regression parameter is root n consistent. The finite sample performance of our model averaging estimator is evaluated by some simulation studies. The proposed procedure is further applied to real-data analysis.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Exponential family measurement error models for single-cell CRISPR screens.

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens-"thresholded regression"-exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV ("GLM-based errors-in-variables"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights.

Decomposition via elastic-band transform

In this paper, we propose a novel decomposition method using elastic-band transform (EBT), which mimics eye scanning and is utilized for multiscale analysis of signals. The proposed EBT-based method can efficiently extract the features of various signals with the following three advantages. First, it is a data-driven approach that extracts several important modes based solely on data without using predetermined basis functions. Second, it does not assume that the signal consists of (locally) sinusoidal intrinsic mode functions, which is a common assumption in existing methods. Therefore, the proposed method can handle a wide range of signals. Finally, it is robust to noise. A practical algorithm for decomposition is presented, along with some theoretical properties. Simulation examples and real data analysis results show promising empirical properties of the proposed method.

Distributed Pseudo-Likelihood Method for Community Detection in Large-Scale Networks

This paper proposes a distributed pseudo-likelihood method (DPL) to conveniently identify the community structure of large-scale networks. Specifically, we first propose a block-wise splitting method to divide large-scale network data into several subnetworks and distribute them among multiple workers. For simplicity, we assume the classical stochastic block model. Then, the DPL algorithm is iteratively implemented for the distributed optimization of the sum of the local pseudo-likelihood functions. At each iteration, the worker updates its local community labels and communicates with the master. The master then broadcasts the combined estimator to each worker for the new iterative steps. Based on the distributed system, DPL significantly reduces the computational complexity of the traditional pseudo-likelihood method using a single machine. Furthermore, to ensure statistical accuracy, we theoretically discuss the requirements of the worker sample size. Moreover, we extend the DPL method to estimate degree-corrected stochastic block models. The superior performance of the proposed distributed algorithm is demonstrated through extensive numerical studies and real data analysis.

An ensemble penalized regression method for multi-ancestry polygenic risk prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{{\\mathscr{L}}}}}}}_{1}$$\\end{document} (lasso) and L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{{\\mathscr{L}}}}}}}_{2}$$\\end{document} (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

Role of future SNIa data from Rubin LSST in reinvestigating cosmological models

ABSTRACT We study how future Type Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models. We use a realistic 3-yr SNIa simulated data set generated by the LSST Dark Energy Science Collaboration time domain pipeline, which includes a mix of spectroscopic and photometrically identified candidates. We combine these data with cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) measurements to estimate the dark energy model parameters for two models – the baseline Lambda cold dark matter (ΛCDM) and Chevallier–Polarski–Linder (CPL) dark energy parametrization. We compare them with the current constraints obtained from the joint analysis of the latest real data from the Pantheon SNIa compilation, CMB from Planck 2018 and BAO. Our analysis finds tighter constraints on the model parameters along with a significant reduction of correlation between H0 and σ8,0. We find that LSST is expected to significantly improve upon the existing SNIa data in the critical analysis of cosmological models.

Multiple phenotype association tests based on sliced inverse regression

BackgroundJoint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes.ResultsWe conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors.ConclusionSeveral scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document}.

Likelihood ratio test for the analysis of germination percentages

Abstract The germination percentage (GP) is commonly employed to estimate the viability of a seed population. Statistical methods such as analysis of variance (ANOVA) and logistic regression are frequently used to analyse GP data. While ANOVA has a long history of usage, logistic regression is considered more suitable for GP data due to its binomial nature. However, both methods have inherent issues that require attention. In this study, we address previously unexplored challenges associated with these methods and propose the utilization of a likelihood ratio test as a solution. We demonstrate the advantages of employing the likelihood ratio test for GP data analysis through simulations and real data analysis.

MRBEE: A bias-corrected multivariable Mendelian randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies (GWAS). However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using Bias corrected Estimating Equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.

Mapping QTL controlling count traits with excess zeros and ones using a zero-and-one-inflated generalized Poisson regression model.

The research on the quantitative trait locus (QTL) mapping of count data has aroused the wide attention of researchers. There are frequent problems in applied research that limit the application of the conventional Poisson model in the analysis of count phenotypes, which include the overdispersion and excess zeros and ones. In this article, a novel model, that is, the zero-and-one-inflated generalized Poisson (ZOIGP) model, is proposed to deal with these problems. Based on the proposed model, a score test is performed for the inflation parameter, in which the ZOIGP model with a constant proportion of excess zeros and ones is compared with a standard generalized Poisson model. To illustrate the practicability of the ZOIGP model, we extend it to the QTL interval mapping application that underpins count phenotype with excess zeros and excess ones. The genetic effects are estimated utilizing the expectation-maximization algorithm embedded with the Newton-Raphson algorithm, and the genome-wide scan and likelihood ratio test is performed to map and test the potential QTLs. The statistical properties exhibited by the proposed method are investigated through simulation. Finally, a real data analysis example is used to illustrate the utility of the proposed method for QTLmapping.

Privacy preserving support vector machine based on federated learning for distributed IoT‐enabled data analysis

AbstractIn a smart city, IoT devices are required to support monitoring of normal operations such as traffic, infrastructure, and the crowd of people. IoT‐enabled systems offered by many IoT devices are expected to achieve sustainable developments from the information collected by the smart city. Indeed, artificial intelligence (AI) and machine learning (ML) are well‐known methods for achieving this goal as long as the system framework and problem statement are well prepared. However, to better use AI/ML, the training data should be as global as possible, which can prevent the model from working only on local data. Such data can be obtained from different sources, but this induces the privacy issue where at least one party collects all data in the plain. The main focus of this article is on support vector machines (SVM). We aim to present a solution to the privacy issue and provide confidentiality to protect the data. We build a privacy‐preserving scheme for SVM (SecretSVM) based on the framework of federated learning and distributed consensus. In this scheme, data providers self‐organize and obtain training parameters of SVM without revealing their own models. Finally, experiments with real data analysis show the feasibility of potential applications in smart cities. This article is the extended version of that of Hsu et al. (Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. ACM; 2020:904‐906).