RADT-35. CHANGE IN HIPPOCAMPAL VOLUME AS A FUNCTION OF RADIATION DOSE: RESULTS FROM A PROSPECTIVE TRIAL WITH STANDARDIZED IMAGING AND MORPHOMETRIC EVALUATION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi48-vi48
Author(s):  
Matthew Hall ◽  
Yazmin Odia ◽  
Toba Niazi ◽  
Reshma Naidoo ◽  
Golnar Alamdari ◽  
...  
Keyword(s):  
Radiation Dose ◽  
Volume Change ◽  
Prospective Trial ◽  
Hippocampal Volume ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Adult Brain ◽  
Intensity Modulated Proton Therapy ◽  
Automated Software ◽  
Over Time

Abstract PURPOSE/OBJECTIVES MRIs in pediatric and adult brain tumor patients (Age< 35) were prospectively collected at baseline and during follow-up to measure volumetric changes in multiple brain substructures with neurocognitive, laboratory, and quality-of-life assessments. In this planned interim analysis, we model early outcomes for change in hippocampal volume at 6 months following radiotherapy. MATERIALS AND METHODS As of 5/15/2021, 50 patients enrolled on this prospective study and 41 completed 6-month post-treatment assessments after fractionated intensity-modulated proton therapy. Left and right hippocampus volumes were independently measured on T1 sagittal precontrast MRI at baseline and 6-months after radiotherapy using both automated software and physician-delineated contours. The relationship between mean hippocampus dose and change in volume was assessed by Pearson’s correlation coefficient. A linear mixed-effects model was applied to evaluate other predictors associated with change in hippocampal volume, assuming random effects of subjects. RESULTS Mean hippocampus dose was strongly correlated with change in hippocampal volume at 6 months following radiotherapy (r=−0.727, 95% CI [-0.820,-0.596], p< 0.001). Changes in hippocampal volumes over time were similar between software and physician contours. Hippocampal volume was significantly reduced for mean doses ≥10 Gy (mean Δ -10.8% ± 5.5%, p< 0.001), while no significant volume change was observed with mean doses < 10 Gy (mean Δ +0.7% ± 3.9%). In the mixed-effects model, only mean hippocampus dose was significantly associated with hippocampal volume change (p< 0.001). The final model predicted a -3.4% change in hippocampal volume for every 10 Gy increase in mean dose. CONCLUSIONS Change in hippocampal volume was correlated with hippocampus mean dose at 6 months following radiotherapy. Future analyses will assess volume change in the hippocampus and other brain substructures over time as a function of radiation dose and correlate with measured neurocognitive and other effects.

scholarly journals A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease

2020 ◽  
Vol 16 (12) ◽  
pp. e1008473
Author(s):  
Pamela N. Luna ◽  
Jonathan M. Mansbach ◽  
Chad A. Shaw
Keyword(s):  
Joint Modeling ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Time Dependent ◽  
Time To Event ◽  
Modeling Framework ◽  
Microbial Composition ◽  
Disease Outcomes ◽  
Microbiome Data ◽  
Over Time

Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.

scholarly journals Clinical Importance of Free Thyroxine Concentration Decline After Radiotherapy for Pediatric and Adolescent Brain Tumors

2019 ◽  
Vol 104 (11) ◽  
pp. 4998-5007
Author(s):  
Laura van Iersel ◽  
Jiahui Xu ◽  
Brian S Potter ◽  
Heather M Conklin ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Brain Tumors ◽  
Health Outcomes ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Height Loss ◽  
Low Grade ◽  
Lower Baseline ◽  
Increased Risk ◽  
Over Time

Abstract Context Clinical significance of a decline in free T4 (FT4) concentrations across the reference range in children with brain tumors treated with radiation therapy (RT) is uncertain. Objectives To study trends in FT4 in children after RT and risk factors and health outcomes associated with plasma FT4 concentrations. Design and Setting Longitudinal, single-center retrospective cohort study. Patients Low-grade glioma or ependymoma patients (n = 267; age ≤25 years) who received RT (50.4 to 59.4 Gy) at a single institution (1996 to 2016) and followed with serial FT4 measurements. Main Outcome Measure A linear mixed-effects model with a random intercept was used to investigate risk factors for longitudinal changes in FT4 concentrations. A two-stage mixed-effects model examined associations between clinical outcomes and plasma FT4 concentrations. Results FT4 concentrations declined over time after RT (P < 0.001). Females (P < 0.001) and younger patients (P < 0.001) demonstrated greater declines in FT4 concentrations over time. The rate of weight gain, but not of height loss, increased with a higher FT4 decline rate (P < 0.001). At last follow-up, patients with lower baseline FT4 concentrations had increased risk of glucose disorder (OR, 19.73; P = 0.002) or dyslipidemia (OR, 19.40; P = 0.003) but not high fat mass (P = 0.18). Lower baseline FT4 concentrations were not associated with impaired scores for intelligence, attention, memory, or psychosocial functioning. Conclusions FT4 concentrations significantly decline in children with brain tumor after RT. Variation and trends in FT4 concentration are associated with physical health outcomes. Future studies should assess whether continuous FT4 concentrations and trends, rather than population-based cut-off values, can distinguish between euthyroid and hypothyroid states.

A Nonlinear Mixed Effects Model for Latent Variables

2009 ◽  
Vol 34 (3) ◽  
pp. 293-318 ◽  
Author(s):  
Jeffrey R. Harring
Keyword(s):  
Latent Variables ◽  
Repeated Measures ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Longitudinal Change ◽  
Nonlinear Mixed Effects ◽  
Marginal Maximum Likelihood ◽  
Nonlinear Mixed Effects Model ◽  
Latent Construct ◽  
Over Time

The nonlinear mixed effects model for continuous repeated measures data has become an increasingly popular and versatile tool for investigating nonlinear longitudinal change in observed variables. In practice, for each individual subject, multiple measurements are obtained on a single response variable over time or condition. This structure can be adapted to examine the change in latent variables rather than modeling change in manifest variables. This article considers a nonlinear mixed effects model for describing nonlinear change of a latent construct over time, where the latent construct of interest is measured by multiple indicators gathered at each measurement occasion. To accomplish this, the nonlinear mixed effects model is modified to include a measurement model that explicitly expresses the relationship of the observed variables to the latent constructs. A method for marginal maximum likelihood estimation of this model is presented and discussed. An example using education data is provided to illustrate the utility of the model.

scholarly journals Moral Identity Predicts the Development of Presence of Meaning During Emerging Adulthood

2018 ◽  
Vol 7 (3) ◽  
pp. 230-237 ◽  
Author(s):  
Hyemin Han ◽  
Indrawati Liauw ◽  
Ashley Floyd Kuntz
Keyword(s):  
High School ◽  
Emerging Adulthood ◽  
Moral Identity ◽  
Moral Values ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Senior Year ◽  
Presence Of Meaning ◽  
The Relationship ◽  
Over Time

We examined change over time in the relationship between moral identity and presence of meaning during emerging adulthood. Moral identity refers to a sense of morality and moral values that are central to one’s identity. Presence of meaning refers to the belief that one’s existence has meaning, purpose, and value. Participants responded to questions on moral identity and presence of meaning in their senior year of high school and 2 years after. Mixed effects model analyses were used to examine how moral identity and presence of meaning interacted during this 2-year period. The findings demonstrated that moral identity positively predicted presence of meaning over time.

scholarly journals A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption

2020 ◽  
Vol 39 (15) ◽  
pp. 2051-2066 ◽  
Author(s):  
Rui Wang ◽  
Ante Bing ◽  
Cathy Wang ◽  
Yuchen Hu ◽  
Ronald J. Bosch ◽  
...  
Keyword(s):  
Viral Load ◽  
Treatment Interruption ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Nonlinear Mixed Effects ◽  
Hiv Viral Load ◽  
Nonlinear Mixed Effects Model ◽  
Viral Load Rebound

scholarly journals The state of Spanish /s/ variation in Concepción, Chile: Linguistic and social trends

2020 ◽  
Vol 6 (1) ◽  
pp. 132-153
Author(s):  
Brandon M. A. Rogers
Keyword(s):  
Social Factors ◽  
Central City ◽  
Mixed Effects ◽  
The State ◽  
Mixed Effects Model ◽  
Proportional Odds ◽  
Random Factor ◽  
Social Trends ◽  
Southern Central ◽  
Social Demographics

AbstractThe current study examines /s/ variation in the southern-central city of Concepción, Chile and its relation to a variety of linguistic and social factors. A proportional-odds mixed effects model, with the random factor of “speaker”, was used to treat the categorically coded data on a continuum of acoustical variation ([s] > [h] > ∅). The results presented show that contrary to the previous assertions, heavy sibilant reduction, especially elision, in Concepción, Chile is the rule, rather than the exception, to the extent that it is no longer a marker of certain social demographics as has been reported previously. Furthermore, based on the trends reported, it is likely that this has been the case for several decades. Finally, the overall observed trends are indicative that the rates of /s/ elision will continue to increase across social demographics and different phonetic and phonological contexts in Concepción, Chile.

Estimating local arterial stiffness using mixed-effects model-based residuals: a novel approach

2021 ◽  
Author(s):  
Avinash Chandran ◽  
Derek W. Brown ◽  
Gabriel H. Zieff ◽  
Zachary Y. Kerr ◽  
Daniel Credeur ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Model Based ◽  
Novel Approach
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