Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHAN... by Jiayi Chen

“…Overfitting using kernel function, long calculation period, the definition of prior distribution and lack of rank of heavy metal will affect the performance of the statistical model. Optimal model on this topic still remains controversy. …”
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Assessing the financial burden of multimorbidity among patients aged 30 and above in India by Sudheer Kumar Shukla, Pratheeba John, Sakshi Khemani, Ankur Shaji Nair, Nishikant Singh, Rajeev Sadanandan

“…We assessed multimorbidity prevalence, OOPE, CHE incidence, and CHE intensity. Statistical models, including linear, log-linear, and logistic regressions, were used to examine the financial risk, with a focus on non-communicable diseases (NCDs), healthcare facility choice, and socioeconomic status and Epidemiological Transition Levels (ETLs). …”
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Predictive modeling to evaluate long-term treatment effectiveness of darvadstrocel in patients with complex perianal fistulas in Crohn’s disease by Chitra Karki, Gary Hantsbarger, Erika Turkstra, Elisabetta Fenu, Ken Genenz, Inmaculada Gilaberte, Julián Panés

“…Methods Data were pooled from a clinical trial (ADMIRE-CD) and two retrospective chart review studies (INSPECT and PREFACE). Predictive statistical models extrapolated clinical outcomes beyond observed follow-up using parametric curves, which were implemented into a semi-Markov model to obtain the number of patients in remission. …”
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Association between estimated glucose disposal rate and cardiovascular diseases in patients with diabetes or prediabetes: a cross-sectional study by Jinhao Liao, Linjie Wang, Lian Duan, Fengying Gong, Huijuan Zhu, Hui Pan, Hongbo Yang

“…Model evaluation showed better performance in fully adjusted models than basic models[C-statistics(Model 3 vs. Model 1): CVD(0.683 vs. 0.814), CAD(0.672 vs. 0.807), CHF(0.714 vs. 0.839) and stroke(0.660 vs. 0.790)]. …”
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A multi-omics strategy to understand PASC through the RECOVER cohorts: a paradigm for a systems biology approach to the study of chronic conditions by Jun Sun, Masanori Aikawa, Hassan Ashktorab, Noam D. Beckmann, Noam D. Beckmann, Michael L. Enger, Joaquin M. Espinosa, Xiaowu Gai, Xiaowu Gai, Benjamin D. Horne, Benjamin D. Horne, Paul Keim, Paul Keim, Paul Keim, Jessica Lasky-Su, Rebecca Letts, Cheryl L. Maier, Meisha Mandal, Lauren Nichols, Nadia R. Roan, Nadia R. Roan, Mark W. Russell, Jacqueline Rutter, George R. Saade, George R. Saade, Kumar Sharma, Kumar Sharma, Stephanie Shiau, Stephen N. Thibodeau, Samuel Yang, Lucio Miele, NIH Researching COVID to Enhance Recovery (RECOVER) Consortium

“…This approach will minimize lab-to-lab technical variability, maximize sample size for class discovery, and enable the incorporation of as many relevant variables as possible into statistical models. Many of our recommendations have already been considered by the NIH through the peer-review process, resulting in the creation of a systems biology panel that is currently designing the studies we proposed. …”
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Effect of supplementing phytase on piglet performance, nutrient digestibility and bone mineralisation by C. De Cuyper, L. Nollet, M. Aluwé, J. De Boever, L. Douidah, E. Vanderbeke, N. Outchkourov, S. Petkov, S. Millet

“…Incremental increases of phytase improved final body weight (BW) (P<0.001), average daily gain (ADG) (P<0.001) and feed conversion ratio (FCR) (P<0.01). The statistical model predicted a linear increase for daily feed intake (DFI), ADG and BW with increased phytase increments. …”
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Synergistic antimicrobial activity of essential oils mixture of Moringa oleifera, Cinnamomum verum and Nigella sativa against Staphylococcus aureus using L-optimal mixture design by Samah H. Abu-Hussien, Antony R. Nasry, Ziad Samy, Salwa M. El-Sayed, Ashraf Bakry, Naglaa Ebeed, Hesham Elhariry, Thanaa ElNoby

“…The present study aims to evaluate the antimicrobial potential of the essential oils (EOs) of Moringa oleifera (moringa), Cinnamomum verum (cinnamon), and Nigella sativa (black seed) and the synergistic effect of the mixture of these oils against Staphylococcus aureus MCC 1351. Statistical modeling revealed cinnamon oil had the highest individual antimicrobial potency, followed by black seed oil. …”
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Proximity or Directional Model of Voting for the Turkish Voter? by Murat İnan, Gül Arıkan Akdağ

“…Comparing explanatory powers of the statistical models showed that, contrary to the findings of MacDonald and his colleagues, the proximity model of voting is a more appropriate tool than the directional model to explain voting behavior in Türkiye. …”
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A novel model-based meta-analysis to indirectly estimate the comparative efficacy of two medications: an example using DPP-4 inhibitors, sitagliptin and linagliptin, in treatment o... by Yan Gong, James Rogers, Christian Friedrich, Sanjay Patel, Alexander Staab, Jorge Luiz Gross, Daniel Polhamus, William Gillespie, Brigitta Ursula Monz, Silke Retlich

“…Objectives To develop a longitudinal statistical model to indirectly estimate the comparative efficacies of two drugs, using model-based meta-analysis (MBMA). …”
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Psychometric characteristics and feasibility of microinteraction-based Ecological Momentary Assessment in audiology research by Yu-Hsiang Wu, Elizabeth Stangl, Samuel Smith, Jacob Oleson, Christi Miller, Octav Chipara

“…Participants provided feedback on their experience with micro-EMA.ResultsIn the dual-task experiment, participants reported improved hearing performance in micro-EMA as SNRs and speech recognition scores increased across all three response formats, supporting the tool's construct validity. Statistical models indicated that the 5-point and 10-point scales yielded larger relative changes between SNRs, suggesting higher responsiveness, compared to the 2-point scale. …”
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Large-scale spatio-temporal variation in vital rates and population dynamics of an alpine bird by Nater, Chloé R., Frassinelli, Francesco, Martin, James A., Nilsen, Erlend B.

“…Simultaneously, there is much ongoing development of statistical models that are increasingly more powerful and able to make more efficient use of field data. …”
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Accounting for observation biases associated with counts of young when estimating fecundity: case study on the arboreal-nesting red kite (Milvus milvus) by Sollmann, Rahel, Adenot, Nathalie, Spakovszky, Peter, Windt, Jendrik, Mattsson, Brady J.

“…These counts can, however, suffer from over and undercounting, which can lead to biased estimates of fecundity (average number of nestlings per brood). Statistical model development to account for observation bias has focused on false negatives (undercounts), yet it has been shown that these models are sensitive to the presence of false positives (overcounts) when they are not accounted for. …”
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Management Control System and Financial Performance of Micro Finance Institutions in Central Region Uganda by Arthur, Sunday, Caleb, Tamwesigire, Caroline Masiko, Murezi, George Stanley, Kinyata, Godfrey, Barigye, Keneth, Nuwagira, Moses, Agaba

“…Structural Equations Modeling with Analysis of Moment Structures were used to for statistical modeling Besides, Hierarchical regression was used to test the predictive power of the variables and indicate precisely what happens to the model as different predictor variables are introduced in the model fit. …”
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A satellite-based analysis of semi-direct effects of biomass burning aerosols on fog and low-cloud dissipation in the Namib Desert by A. Mass, A. Mass, H. Andersen, H. Andersen, J. Cermak, J. Cermak, P. Formenti, E. Pauli, E. Pauli, J. Quinting

“…A statistical learning framework is used to quantify meteorological and BBA influences on FLC dissipation time. The statistical model is able to reproduce the observed differences in FLC dissipation time between high- and low-BBA days and attributes these differences mainly to differences in circulation, boundary layer moisture and near-surface air temperature along the coastline. …”
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Lipids as key biomarkers in unravelling the pathophysiology of obesity-related metabolic dysregulation by Anis Adibah Osman, Siok-Fong Chin, Lay-Kek Teh, Noraidatulakma Abdullah, Nor Azian Abdul Murad, Rahman Jamal

“…The predictive model underwent evaluation across four machine learning algorithms consistently demonstrated the highest predictive accuracy of 0.821, aligning with the findings from the classical logistic regression statistical model. Notably, the presence of 4′-apo-beta,psi-caroten-4′-al showed a statistically significant difference between the lean and obese individuals among the metabolites included in the model. …”
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Employment status and information needs of patients with breast cancer: a multicentre cross-sectional study of first oncology consultations by Lidia Del Piccolo, Maria Angela Mazzi, Cinzia Perlini, Giuseppe Deledda, Alberto Ghilardi, Chiara Buizza, Alessandro Bottacini, Claudia Goss

“…When age and education were included in the statistical model, the significance of employment status was rearranged among all the variables and was no more significant (b=1.2, p=0.44). …”
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Determinants of fertility in timed artificial insemination programs in beef cattle: predictive ability and risk factors from almost 2 million data points by R.E.F. Assis, F. Baldi, L.B. Temp, R. Ungerfeld, M.F. de Sá Filho

“…A total of 1 832 999 TAIs conducted on 2 002 farms across South American countries were considered for the analysis, including 15 main fixed effects or interactions in the statistical model, in addition to five random effects. …”
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Effect of supplementing epinephrine in maturation media on the developmental competence of buffalo oocytes by Ali Husnain, Abdul Khaliq, Talha Ashraf, Abdul Rehman, Muhammad Yasir Zahoor, Muhammad Binyameen, Amjad Riaz

“…The data was analyzed using the GLIMMIX procedures of SAS. The statistical model included the fixed effect of treatment and random replication. …”
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Stochastic Modeling of Adaptive Trait Evolution in Phylogenetics: A Polynomial Regression and Approximate Bayesian Computation Approach by Dwueng-Chwuan Jhwueng, Chia-Hua Chang

“…Assume that the main trait value <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>y</mi><mi>t</mi></msub></semantics></math></inline-formula> is a random variable of the Ornstein–Uhlenbeck (OU) process and that its optimal adaptive value <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msubsup><mi>θ</mi><mi>t</mi><mi>y</mi></msubsup></semantics></math></inline-formula> has a polynomial relationship with other traits <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>x</mi><mi>t</mi></msub></semantics></math></inline-formula> for statistical modeling, where <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>x</mi><mi>t</mi></msub></semantics></math></inline-formula> can be a random variable of Brownian motion (BM) or OU process. …”
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The relationships of metals exposure and disturbance of the vaginal microbiota with the risk of PROM: Results from a birth cohort study by Dongyang Zhang, Rui Dong, Tingting Jiang, Shiwei Ren, Xinyu Yue, Muxin Zhai, Siyu Jiang, Bingxia He, Ran Tang, Yujie Deng, Wenjie Lyu, Baojing Zhao, Fangbiao Tao, Yuanyuan Yang, Zongzhi Yin, Zhen Yu, Dongmei Ji, Chunmei Liang

“…The metals that significantly associated with the risk of PROM were screened out via four statistical models, the top three were barium (Ba), chromium (Cr) and thallium (Tl) according to their weight indices. …”
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