Article
Neurosciences
Marcio Braga de Melo, Dimitri Daldegan-Bueno, Maria Gabriela Menezes Oliveira, Altay Lino de Souza
Summary: In neuroscience research, the analysis of longitudinal data using ANOVA and MANOVA has special requirements and limitations. GEE and GLMM provide an alternative approach and offer advantages such as flexibility in modeling the dependent variable, better handling of different time lengths and missing data. GEE and GLMM may provide more reliable results compared to rmANOVA and rmMANOVA in neuroscience research, particularly in small sample sizes with unbalanced longitudinal designs with or without missing data.
EUROPEAN JOURNAL OF NEUROSCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Youjun Huang, Jianxin Pan
Summary: Modeling longitudinal binary data with constraints on the correlation coefficients is achieved by a novel joint GEE method, which shows good performance in simulation studies even under misspecified covariance structures. The proposed method allows for simultaneous modeling of mean and within-subject correlation coefficients, taking into account the upper bound of the correlation coefficients.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Cell Biology
Jessica Murphy, Nicholas E. Weaver, Audrey E. Hendricks
Summary: This article introduces the application of linear mixed effects models in longitudinal studies, focusing on the impact of microbiome transplantation on mouse growth trajectories. The comparison of two statistical models under different parameterizations reveals inconsistencies in results.
DISEASE MODELS & MECHANISMS
(2022)
Article
Psychology
Don van den Bergh, Eric-Jan Wagenmakers, Frederik Aust
Summary: Analysis of variance (ANOVA) is commonly used to assess experimental manipulations on continuous outcomes. Traditional frequentist ANOVA and Bayesian ANOVA can have different conclusions when there are multiple repeated-measures factors. The disagreement is due to different model specifications, and we argue that the Bayesian ANOVA should consider individual differences.
ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE
(2023)
Article
Mathematical & Computational Biology
Ariel Mundo, Timothy J. Muldoon, John R. Tipton
Summary: In biomedical research, traditional approaches such as repeated measures analysis of variance (rm-ANOVA) and linear mixed models (LMEMs) are often used to analyze longitudinal data. However, these methods assume a linear trend in the measured response, which may not accurately capture the true non-linear trend in biomedical research. This paper presents the advantages of using generalized additive models (GAMs) to analyze longitudinal data with non-linear trends and demonstrates their implementation using simulated data.
STATISTICS IN MEDICINE
(2022)
Article
Mathematical & Computational Biology
Xueqi Wang, Elizabeth L. Turner, Fan Li
Summary: Individually randomized group treatment (IRGT) trials are becoming more popular in public health research. This study proposes sample size procedures for continuous and binary outcomes in IRGT trials and validates them through simulations.
STATISTICS IN MEDICINE
(2023)
Article
Biochemical Research Methods
Han Sun, Xiaoyun Huang, Ban Huo, Yuting Tan, Tingting He, Xingpeng Jiang
Summary: The study developed a novel method called aGEEMIHC to detect sparse microbial association signals in longitudinal microbiome data using generalized estimating equations. Simulation experiments showed that aGEEMiHC achieved superior statistical power and stability for different types of host phenotypes.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Health Care Sciences & Services
Anita Brobbey, Samuel Wiebe, Alberto Nettel-Aguirre, Colin Bruce Josephson, Tyler Williamson, Lisa M. Lix, Tolulope T. Sajobi
Summary: This study investigates the accuracy of repeated measures discriminant analysis (RMDA) based on the multivariate generalized estimating equations (GEE) framework for classification in multivariate repeated measures designs with the same or different types of responses repeatedly measured over time. RMDA based on GEE exhibited higher average classification accuracy than RMDA based on maximum likelihood estimators (MLE) especially in multivariate non-normal distributions.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2022)
Article
Mathematical & Computational Biology
Youjun Huang, Jianxin Pan
Summary: The paper proposes a PJGEE method based on SCAD and LASSO for modeling the mean and correlations of longitudinal binary data, along with variable selection. Simulation studies show that the method outperforms existing PGEE methods in terms of variable selection consistency and parameter estimation accuracy. Analysis on a real data set further confirms the effectiveness of the method.
BIOMETRICAL JOURNAL
(2022)
Article
Health Care Sciences & Services
Yusuke Saigusa, Shinto Eguchi, Osamu Komori
Summary: The generalized linear mixed model (GLMM) is a common method for analyzing longitudinal and clustered data in biological sciences. However, issues of model complexity and misspecification can arise. This paper extends the standard GLMM to a nonlinear mixed-effects model based on quasi-linear modeling, providing an estimation algorithm and a conditional AIC for the proposed model. Performance under model misspecification is evaluated in simulation studies, and the proposed model is shown to capture heterogeneity in respiratory illness data.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2022)
Article
Psychology, Clinical
Yue Pan, Lisa R. Metsch, Lauren K. Gooden, Alejandro Max Antonio Mantero, Daniel J. Feaster
Summary: This study introduces a new approach to analyzing sexual behaviors by disaggregating them and examining interactions among different subtypes of sexual acts, shedding light on how individuals make choices among sexual behaviors. The study conducted on over 5000 patients in STD clinics across the U.S. demonstrates the applicability and benefits of this method in evaluating sexual risk behaviors and other predictors of interest.
ARCHIVES OF SEXUAL BEHAVIOR
(2021)
Article
Plant Sciences
Paulo Pagliari, Fernando Shintate Galindo, Jeffrey Strock, Carl Rosen
Summary: Field studies often neglect the correlation between measurements taken on the same subject over time, leading to potential inference errors. Combining fixed, random, and repeated measurement effects in the same statistical model requires a more in-depth understanding of modeling error terms.
Article
Mathematics, Interdisciplinary Applications
Sun-Joo Cho, Duane Watson, Cassandra Jacobs, Matthew Naveiras
Summary: This paper presents a statistical model to investigate syntactic priming effects and concludes that the evidence of self-priming is consistent with activation-based theories. The model is evaluated using Bayesian analysis through simulation studies to assess parameter estimates and precision.
MULTIVARIATE BEHAVIORAL RESEARCH
(2021)
Article
Mathematical & Computational Biology
Masahiko Gosho, Ryota Ishii, Hisashi Noma, Kazushi Maruo
Summary: Using GEE can lead to biased regression coefficients for small samples or sparse data. BCGEE and PGEE were proposed as solutions to correct the bias in small samples. Modified covariance estimators have also been proposed to address the bias in standard error. This study reviewed the performance of modified GEEs and covariance estimators in sparse binary data from small-sample longitudinal studies.
STATISTICS IN MEDICINE
(2023)
Review
Health Care Sciences & Services
Nelson Alirio Cruz Gutierrez, Oscar Orlando Melo, Carlos Alberto Martinez
Summary: A novel model for cross-over designs with repeated measures was developed, which incorporates both parametric and non-parametric components to capture treatment, time, and carry-over effects. Simulation study showed that the proposed model outperforms standard models in the presence of carry-over or functional temporal effects. The solution is analogous to weighted least squares, allowing model diagnostics similar to multiple regression.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2023)
Article
Zoology
Caroline M. Hostetler, Katherine Hinde, Nicole Maninger, Sally P. Mendoza, William A. Mason, Douglas J. Rowland, Guobao B. Wang, David Kukis, Simon R. Cherry, Karen L. Bales
AMERICAN JOURNAL OF PRIMATOLOGY
(2017)
Article
Neurosciences
Nicole Maninger, Katie Hinde, Sally P. Mendoza, William A. Mason, Rebecca H. Larke, Benjamin J. Ragen, Michael R. Jarcho, Simon R. Cherry, Douglas J. Rowland, Emilio Ferrer, Karen L. Bales
Article
Multidisciplinary Sciences
Cary R. Allen-Blevins, Xiaomeng You, Katie Hinde, David A. Sela
Article
Multidisciplinary Sciences
Tanya M. Smith, Christine Austin, Katie Hinde, Erin R. Vogel, Manish Arora
Article
Zoology
Carly R. Muletz-Wolz, Naoko P. Kurata, Elizabeth A. Himschoot, Elizabeth S. Wenker, Elizabeth A. Quinn, Katie Hinde, Michael L. Power, Robert C. Fleischer
AMERICAN JOURNAL OF PRIMATOLOGY
(2019)
Article
Anthropology
Lauren Petrullo, Katie Hindle, Amy Lu
AMERICAN JOURNAL OF HUMAN BIOLOGY
(2019)
Article
Nutrition & Dietetics
Meghan B. Azad, Nathan C. Nickel, Lars Bode, Meredith Brockway, Amy Brown, Christina Chambers, Camie Goldhammer, Katie Hinde, Michelle McGuire, Daniel Munblit, Aloka L. Patel, Rafael Perez-Escamilla, Kathleen M. Rasmussen, Natalie Shenker, Bridget E. Young, Luisa Zuccolo
Summary: This paper summarizes a workshop that focused on breastfeeding and human milk research, identifying key research areas, barriers, and proposing an action plan. Priority research areas include increasing breastfeeding rates, raising awareness of benefits, studying different modes of milk feeding, and understanding the health effects of breastfeeding.
MATERNAL AND CHILD NUTRITION
(2021)
Article
Biology
Katie Hinde, Carlos Eduardo G. Amorim, Alyson F. Brokaw, Nicole Burt, Mary C. Casillas, Albert Chen, Tara Chestnut, Patrice K. Connors, Mauna Dasari, Connor Fox Ditelberg, Jeanne Dietrick, Josh Drew, Lara Durgavich, Brian Easterling, Charon Henning, Anne Hilborn, Elinor K. Karlsson, Marc Kissel, Jennifer Kobylecky, Jason Krell, Danielle N. Lee, Kate M. Lesciotto, Kristi L. Lewton, Jessica E. Light, Jessica Martin, Asia Murphy, William Nickley, Alejandra Nunez-de la Mora, Olivia Pellicer, Valeria Pellicer, Anali Maughan Perry, Stephanie G. Schuttler, Anne C. Stone, Brian Tanis, Jesse Weber, Melissa Wilson, Emma Willcocks, Christopher N. Anderson
Summary: March Mammal Madness is a science outreach project that engages hundreds of thousands of people in the United States each year through a simulated tournament featuring 64 animals. By combining gamification, social media, community events, and creative products, it translates academic literature into gripping narratives for high school educators and students, reaching an estimated 1% of high school students in the US in 2019. The intentional design and use of human psychological and cognitive adaptations contribute to the widespread use and success of the project.
Article
Anthropology
Carlos Eduardo G. Amorim, Mauna Dasari, Lara Durgavich, Katie Hinde, Marc Kissel, Kristi L. Lewton, Tisa Loewen
Summary: Public engagement is seen as crucial in scientific scholarship, but scholars face challenges in navigating public education and science communication. Using the science communication program "March Mammal Madness (MMM)" as an example, it is important to address implicit biases while refining and improving public science education programs to align with values and goals.
AMERICAN JOURNAL OF HUMAN BIOLOGY
(2022)
Article
Behavioral Sciences
Gregory E. Blomquist, Katie Hinde, John P. Capitanio
Summary: Early life interindividual variation in HPA reactivity to stress plays a significant role in predicting later life psychological and physical well-being, with genetic factors, especially dominance effects, contributing to the observed differences. Sex-specific genetic effects were also noted, particularly in the heritability of cortisol levels in response to maternal separation and relocation during infancy.
BEHAVIORAL NEUROSCIENCE
(2022)
Review
Food Science & Technology
Carolina de Weerth, Anna-Katariina Aatsinki, Meghan B. Azad, Frank F. Bartol, Lars Bode, Maria Carmen Collado, Amanda M. Dettmer, Catherine J. Field, Meagan Guilfoyle, Katie Hinde, Aniko Korosi, Hellen Lustermans, Nurul Husna Mohd Shukri, Sophie E. Moore, Shikha Pundir, Juan Miguel Rodriguez, Carolyn M. Slupsky, Sarah Turner, Johannes B. van Goudoever, Anna Ziomkiewicz, Roseriet Beijers
Summary: Human milk is a complex liquid food that is customized to meet the needs of infants. The non-nutrient bioactives in milk have an impact on child cognitive and behavioral development, a process known as 'Lactocrine Programming'. This review discusses the links between human milk composition and child cognitive and behavioral development, and provides recommendations for future studies.
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION
(2023)
Article
Anthropology
Brooke A. Scelza, Katie Hinde
HUMAN NATURE-AN INTERDISCIPLINARY BIOSOCIAL PERSPECTIVE
(2019)
Article
Evolutionary Biology
Laura D. Klein, Jincui Huang, Elizabeth A. Quinn, Melanie A. Martin, Alicia A. Breakey, Michael Gurven, Hillard Kaplan, Claudia Valeggia, Grazyna Jasienska, Brooke Scelza, Carlito B. Lebrilla, Katie Hinde
EVOLUTION MEDICINE AND PUBLIC HEALTH
(2018)
Article
Psychology, Educational
Amanda M. Dettmer, Ashley M. Murphy, Denisse Guitarra, Emily Slonecker, Stephen J. Suomi, Kendra L. Rosenberg, Melinda A. Novak, Jerrold S. Meyer, Katie Hinde
Article
Anthropology
Florent Pittet, Crystal Johnson, Katie Hinde
AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY
(2017)