scholarly journals 576Is the method of longitudinal data analysis equally relevant for common versus uncommon endpoints?

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Moleen Dzikiti ◽  
Barbara Laughton ◽  
Steve Innes ◽  
Mark Cotton
Keyword(s):  
Antiretroviral Treatment ◽  
Viral Suppression ◽  
Mixed Model ◽  
Infant Health ◽  
Linear Mixed Model ◽  
Clinical Trial Data ◽  
Correlation Structure ◽  
Random Intercept ◽  
Autoregressive Correlation ◽  
Early Antiretroviral Treatment

Abstract Background We explored the effects of predominant breastfeeding on infection-related hospitalization (uncommon outcome), over the first year of life, using the Mother Infant Health cohort study (MIHS), and the effect of early antiretroviral treatment (ART) on viral suppression (< 400 copies/mL) (common outcome), in children aged 7 to 12 weeks, using a subset of the Children with HIV Early AntiRetroviral Treatment (CHER) clinical trial data. We assessed the sensitivity of findings to different models to account for dependency of uncommon and common binary outcome. Methods We fitted generalized linear mixed model with (1) random intercept and (2) random slope, generalized estimating equations (GEE) with 3) an exchangeable correlation structure; 4) autoregressive correlation structure of order 1 (AR1) and 5) unstructured correlation structure and 6) logistic regression model. Results Eighty four and 119 children from MIHS were non-predominantly and predominantly breastfed, respectively. There were 34 infection-related hospitalizations overall. Most infants were hospitalized once, except for four with two hospitalizations. We analysed 88 HIV-infected children from the CHER trial. On average, a child achieved viral suppression twice, range of one to four. The effect of predominant breastfeeding on infection-related hospitalization was similar across all models, except for the GEE with AR1 that had a high estimate (wider confidence intervals). The effect of early ART exposure on viral suppression varied across models. Conclusions The sensitivity of estimates to the method of analysis was driven by frequency of the outcome.

Bias in common catch-curve methods applied to age frequency data from fish surveys

2019 ◽  
Vol 76 (7) ◽  
pp. 2090-2101
Author(s):  
Gary A Nelson
Keyword(s):  
Linear Regression ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Standard Errors ◽  
Cluster Sampling ◽  
Probability Models ◽  
Generalized Poisson ◽  
Random Intercept ◽  
Multi Stage ◽  
Wide Range

Abstract Catch curve analysis is often used in data-limited fisheries stock assessments to estimate total instantaneous mortality (Z). There are now six catch-curve methods available in the literature: the Chapman–Robson, linear regression, weighted linear regression, Heincke, generalized Poisson linear, and random-intercept Poisson linear mixed model. An assumption shared among the underyling probability models of these estimators is that fish collected for ageing are sampled from the population by simple random sampling. This type of sampling is nearly impossible in fisheries research because populations are sampled in surveys that use gears that capture individuals in clusters and often fish for ageing are selected from multi-stage sampling. In this study, I explored the effects of multi-stage cluster sampling on the bias of the estimates of Z and their associated standard errors. I found that the generalized Poisson linear model and the Chapman–Robson estimators were the least biased, whereas the random-intercept Poisson linear mixed model was the most biased under a wide range of simulation scenarios that included different levels of recruitment variation, intra-cluster correlation, sample sizes, and methods used to generate age frequencies. Standard errors of all estimators were under-estimated in almost all cases and should not be used in statistical comparisons.

scholarly journals Association of county perinatal resources and gestational weight gain in West Virginia, United States

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Wilson A. Koech ◽  
Christa L. Lilly
Keyword(s):  
Weight Gain ◽  
West Virginia ◽  
Gestational Weight Gain ◽  
Mixed Model ◽  
Infant Health ◽  
Linear Mixed Model ◽  
Smoking During Pregnancy ◽  
Gestational Weight ◽  
Excessive Gestational Weight Gain ◽  
Significant Difference

Abstract Background Inappropriate (inadequate or excessive) gestational weight gain (GWG) is of great concern to maternal, fetal and infant health. Different maternal and fetal risk factors are associated with GWG, but little is known about a more distal risk factor: inadequate county-level perinatal resources. Therefore, the study aim was to investigate GWG in women living in counties with below average perinatal resources in comparison with their counterparts living in counties with above average perinatal resources. Methods Retrospective study of 406,792,010–2011 West Virginia births in 55 counties. The outcome was GWG and the main predictor was county perinatal resources. Hierarchical linear mixed model was used to investigate the association of county perinatal resources and GWG. Results County perinatal resources was associated with GWG (p = 0.009), controlling for important covariates. Below average county perinatal resources was not significantly associated with a decrease in mean GWG (M: − 5.29 lbs., 95% CI: − 13.94, 3.35, p = 0.2086), in comparison with counties with above average county perinatal resources. There was significant difference between average, and above average county perinatal resources (M: − 17.20 lbs., 95% CI: − 22.94, − 11.47, p < 0.0001), controlling for smoking during pregnancy and other covariates. Conclusions Average county perinatal resources was associated with reduced mean GWG relative to above average county perinatal resources, but not below average county perinatal resources. However, this could be due to the small number of counties with above average resources as the effect was in the hypothesized direction. This highlights one of the challenges in county perinatal resource studies.

scholarly journals Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Guri Feten ◽  
Trygve Almøy ◽  
Are H. Aastveit
Keyword(s):  
Gene Expression ◽  
Missing Values ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Correlation Structure ◽  
Data Sets ◽  
Data Set ◽  
Nearest Neighbours ◽  
Complete Matrix ◽  
The Ideal

Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six imputation methods. A comparative study of the methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. The study showed that different methods' capability to predict is dependent on the data, hence the ideal choice of method and number of components are different for each data set. For data with correlation structure methods based on K-nearest neighbours seemed to be best, while for data without correlation structure using the average of the gene was to be preferred.

scholarly journals Retention in Continuous Care and Sustained Viral Suppression

2016 ◽  
Vol 16 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Timothy N. Crawford ◽  
Alice Thornton
Keyword(s):  
Viral Suppression ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Sustained Viral Suppression ◽  
Continuous Care ◽  
Disease Clinic ◽  
Study Population ◽  
The Relationship ◽  
Over Time

Objectives: To examine the relationship between retention in continuous care and sustained viral suppression. Methods: The authors retrospectively followed 653 persons who were virally suppressed and seeking care at an infectious disease clinic in Kentucky for an average of 6 years to determine the rates of retention in medical care (≥2 visits separated by ≥3 months within a 12-month period) and sustained viral suppression (<400 copies/mL). A generalized linear mixed model was used to determine an association between retention and suppression over time. Results: Approximately 61% of the study population were retained in continuous care and 75% had sustained viral suppression for all patient-years. Persons retained in care were 3 times the odds of sustaining viral suppression over time ( P < .001). Conclusion: Retention is essential to achieving and maintaining viral suppression. Strategies should be set in place that emphasize increasing the rates of retention, which in turn may increase the rates of suppression.

scholarly journals Linear mixed model for weight analysis in mice infected by Trypanosoma cruzi

2020 ◽  
Vol 42 ◽  
pp. e49916
Author(s):  
Roney Peterson Pereira ◽  
Terezinha Aparecida Guedes ◽  
Érika Cristina Ferreira ◽  
Silvana Marques de Araújo ◽  
Larissa Aparecida Ricardini ◽  
...  
Keyword(s):  
Trypanosoma Cruzi ◽  
Hierarchical Linear Modeling ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Ratio Test ◽  
Linear Modeling ◽  
Components Of Variance ◽  
Treatment Groups ◽  
Random Intercept

The use of linear mixed models for nested structure longitudinal data is called hierarchical linear modeling. This modeling takes into account the dependence of existing data within each level and between hierarchical levels. The process of modeling, estimating and analyzing diagnoses was illustrated through data on the weights of mice experimentally infected by Trypanosoma cruzi, divided into different treatment groups, with the purpose of verifying the evolution of their body weight as a result of using different types of biotherapeutics produced from Gallus gallus domesticus (chicken) serum to treat Trypanosoma cruzi. Through the model selection criteria AIC and BIC and the likelihood ratio test, a model was chosen to describe the data correctly. Model diagnoses were then performed by means of residual analysis for both levels and an analysis of influential observations to verify if any observations were signaled as influencing the fixed effects, the components of variance and the adjusted values. After the analysis, it was possible to notice that the observations that were signaled as influential had little impact on the Model chosen initially, so it was maintained, with no differences being evidenced between the treatments with the biotherapeutics tested; only the Time variable and the Random intercept were necessary to describe the weight of the mice.

Using mobile wearables to establish sleep bioprofiles in newly diagnosed multiple myeloma (MM) patients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8040-8040
Author(s):  
Gil Hevroni ◽  
Donna Mastey ◽  
Elizabet Tavitian ◽  
Andriy Derkach ◽  
Meghan Salcedo ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Well Being ◽  
Cycle Period ◽  
Newly Diagnosed ◽  
Sleep Patterns ◽  
Sleep Monitoring ◽  
Random Intercept ◽  
Sleep Measurement ◽  
Insight Into

8040 Background: Passive monitoring using wearables can objectively measure sleep over extended time periods. MM patients (PTs) are susceptible to fluctuating sleep patterns due to pain and dexamethasone (dex) treatment. In this prospective study, we remotely monitored sleep patterns on 40 newly diagnosed MM (NDMM) PTs while administering electronic PT reported outcome (ePRO) surveys. The study aim was to establish sleep bioprofiles during therapy and correlate with ePROs. Methods: Eligible PTs for the study had untreated NDMM and assigned to either Cohort A – PTs < 65 years or Cohort B – PTs ≥ 65 years. PTs were remotely monitored for sleep 1-7 days at baseline [BL] and continuously up to 6 therapy cycles. PTs completed ePRO surveys (EORTC - QLQC30 and MY20) at BL and after each cycle. Sleep data and completed ePRO surveys were synced to Medidata Rave through Sensorlink technology. Associations between sleep measurement trends and QLQC30 scores were estimated using a linear mixed model with a random intercept. Results: Between Feb 2017 - Sep 2019, 40 PTs (21 M and 19 F) were enrolled with 20 in cohort A (mean 54 yrs, 41-64) and 20 in cohort B (mean 71 yrs, 65-82). Regimens included KRd 14(35%), RVd 12(30%), Dara-KRd 8(20%), VCd 5(12.5%), and Rd 1(2.5%). Sleep data was compiled among 23/40 (57.5%) PTs. BL mean sleep was 578.9 min/24 hr for Cohort A vs. 544.9 min/24 hr for Cohort B (p = 0.41, 95% CI -51.5, 119.5). Overall median sleep trends changed for cohort A by -6.3 min/24 hr per cycle (p = 0.09) and for cohort B by +0.8 min/24 hr per cycle (p = 0.88). EPRO data trends include global health +1.5 score/cycle (p = 0.01, 95% CI 0.31, 3.1), physical +2.16 score/cycle (p < 0.001, 95% CI 1.26, 3.07), insomnia -1.6 score/cycle (p = 0.09, 95% CI [-3.47, 0.26]), role functioning +2.8 score/cycle (p = 0.001, 95% CI 1.15, 4.46), emotional +0.3 score/cycle (p = 0.6, 95% CI -0.73, 1.32), cognitive -0.36 score/cycle (p = 0.44, 95% CI -1.29,0.56), and fatigue -0.36 score/cycle (p = 0.4, 95% CI -1.65, 0.93). No association between sleep measurements and ePRO were detected. Difference in sleep on dex days compared to all other days during the sample cycle period for cohort A was 81.4 min/24 hr (p = 0.004, 95% CI 26, 135) and for cohort B was 37.4 min/24 hr (p = 0.35, 95% CI -41, 115). Conclusions: Our study provides insight into wearable sleep monitoring in NDMM. Overall sleep trends in both cohorts do not demonstrate significant gains or losses, and these trends fit with HRQOL ePRO insomnia responses. Upon further examination, we demonstrate objective differences (younger PTs) in intra-cyclic sleep measurements on dex days compared to other cycle days (less sleep by > 1 hr). For older patients, less variation in sleep profiles was detected during dex days, possibly due to higher levels of fatigue or longer sleep duration. Sleep is an integral part of well-being in the cancer patient. Future studies should continue to characterize sleep patterns as it relates to HRQOL.

scholarly journals Fatalism, Social Support and Self-Management Perceptions among Rural African Americans Living with Diabetes and Pre-Diabetes

2021 ◽  
Vol 11 (2) ◽  
pp. 242-252
Author(s):  
Laurie Abbott ◽  
Elizabeth Slate ◽  
Lucinda Graven ◽  
Jennifer Lemacks ◽  
Joan Grant
Keyword(s):  
Social Support ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Diabetes Management ◽  
Disease Risk ◽  
Public Health Problem ◽  
Clinical Trial Data ◽  
Population Data ◽  
The United States ◽  
Self Management

Diabetes is a public health problem and a major risk factor for cardiovascular disease, the leading cause of death in the United States. Diabetes is prevalent among underserved rural populations. The purposes of this study were to perform secondary analyses of existing clinical trial data to determine whether a diabetes health promotion and disease risk reduction intervention had an effect on diabetes fatalism, social support, and perceived diabetes self-management and to provide precise estimates of the mean levels of these variables in an understudied population. Data were collected during a cluster randomized trial implemented among African American participants (n = 146) in a rural, southern area and analyzed using a linear mixed model. The results indicated that the intervention had no significant effect on perceived diabetes management (p = 0.8), diabetes fatalism (p = 0.3), or social support (p = 0.4). However, the estimates showed that, in the population, diabetes fatalism levels were moderate (95% CI = (27.6, 31.3)), and levels of social support (CI = (4.0, 4.4)) and perceived diabetes self-management (CI = (27.7, 29.3)) were high. These findings suggest that diabetes fatalism, social support, and self-management perceptions influence diabetes self-care and rural health outcomes and should be addressed in diabetes interventions.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

2020 ◽  
Vol 641 ◽  
pp. 159-175
Author(s):  
J Runnebaum ◽  
KR Tanaka ◽  
L Guan ◽  
J Cao ◽  
L O’Brien ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
Management Tool ◽  
American Lobster ◽  
Suitable Habitat ◽  
Climate Conditions ◽  
Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

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