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1.
Sci Rep ; 12(1): 7379, 2022 May 05.
Artículo en Inglés | MEDLINE | ID: mdl-35513435

RESUMEN

Systemic immune-inflammation index (SII) is a novel inflammatory marker based on the composition ratio of blood cell counts. In this study, we evaluated the association between the SII and cerebral small vessel disease (cSVD) in health check-up participants. We evaluated participants from our health check-up registry between 2006 and 2013. The SII was calculated using the following formula: SII = (platelet count × neutrophil count)/lymphocyte count. cSVD was assessed by considering white matter hyperintensity (WMH) volume, lacunes, and cerebral microbleeds (CMBs). A total of 3187 participants were assessed. In multivariable linear regression analysis, the SII was significantly related to WMH volume [ß = 0.120, 95% confidence interval (CI) 0.050-0.189]. However, lacunes and CMBs showed no statistical significance with the SII. In the subgroup analysis by age, the SII was significantly associated with WMH volume only in participants aged ≥ 60 years (ß = 0.225, 95% CI 0.068-0.381). In conclusion, a high SII was associated with cSVD. Since this association was more pronounced in WMH than in lacunes or CMBs, WMH might be closer to the inflammation-related pathological mechanisms.


Asunto(s)
Enfermedades de los Pequeños Vasos Cerebrales , Sustancia Blanca , Enfermedades de los Pequeños Vasos Cerebrales/patología , Humanos , Inflamación/patología , Modelos Lineales , Imagen por Resonancia Magnética , Persona de Mediana Edad , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patología
2.
Sci Rep ; 12(1): 7119, 2022 May 03.
Artículo en Inglés | MEDLINE | ID: mdl-35504934

RESUMEN

There was very limited evidence linking high-sensitivity C-reactive protein (HS-CRP) and total bone mineral density (BMD) in adolescents. The aim of this population-based study was to investigate the relationship between HS-CRP and total BMD in adolescents aged 10-20 years. A cross-sectional study was performed in the normal U.S. population from the data of the National Health and Nutrition Examination Survey (NHANES). The correlation between HS-CRP and total BMD was evaluated by using weighted multivariate linear regression models. And further subgroup analysis was conducted. There were 1747 participants in this study, 47.1% were female, 29.4% were white, 19.5% were black, and 22.3% were Mexican-American. In the multi-regression model that after the potential confounders had been adjusted, HS-CRP was negatively associated with total BMD. The negative association was also observed in the subgroup analyses stratified by gender and age. Our results demonstrated that higher HS-CRP was negatively correlated with total BMD in 10-20 years old adolescents.


Asunto(s)
Densidad Ósea , Proteína C-Reactiva , Adolescente , Adulto , Proteína C-Reactiva/metabolismo , Niño , Estudios Transversales , Femenino , Humanos , Modelos Lineales , Masculino , Encuestas Nutricionales , Adulto Joven
3.
BMJ Open ; 12(5): e055336, 2022 May 09.
Artículo en Inglés | MEDLINE | ID: mdl-35534072

RESUMEN

OBJECTIVES: Several methods are commonly used for meta-analyses of diagnostic studies, such as the bivariate linear mixed model (LMM). It estimates the overall sensitivity, specificity, their correlation, diagnostic OR (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (ROC) estimates. Nevertheless, the bivariate LMM makes potentially unrealistic assumptions (ie, normality of within-study estimates), which could be avoided by the bivariate generalised linear mixed model (GLMM). This article aims at investigating the real-world performance of the bivariate LMM and GLMM using meta-analyses of diagnostic studies from the Cochrane Library. METHODS: We compared the bivariate LMM and GLMM using the relative differences in the overall sensitivity and specificity, their 95% CI widths, between-study variances, and the correlation between the (logit) sensitivity and specificity. We also explored their relationships with the number of studies, number of subjects, overall sensitivity and overall specificity. RESULTS: Among the extracted 1379 meta-analyses, point estimates of overall sensitivities and specificities by the bivariate LMM and GLMM were generally similar, but their CI widths could be noticeably different. The bivariate GLMM generally produced narrower CIs than the bivariate LMM when meta-analyses contained 2-5 studies. For meta-analyses with <100 subjects or the overall sensitivities or specificities close to 0% or 100%, the bivariate LMM could produce substantially different AUCs, DORs and DOR CI widths from the bivariate GLMM. CONCLUSIONS: The variation of estimates calls into question the appropriateness of the normality assumption within individual studies required by the bivariate LMM. In cases of notable differences presented in these methods' results, the bivariate GLMM may be preferred.


Asunto(s)
Curva ROC , Estudios Epidemiológicos , Humanos , Modelos Lineales , Sensibilidad y Especificidad
4.
Dis Model Mech ; 15(5)2022 05 01.
Artículo en Inglés | MEDLINE | ID: mdl-35521689

RESUMEN

Longitudinal studies are commonly used to examine possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe the linear mixed effects (LME) model and how to use it for longitudinal studies. We re-analyze a dataset published by Blanton et al. in 2016 that modeled growth trajectories in mice after microbiome implantation from nourished or malnourished children. We compare the fit and stability of different parameterizations of ANOVA and LME models; most models found that the nourished versus malnourished growth trajectories differed significantly. We show through simulation that the results from the two-way ANOVA and LME models are not always consistent. Incorrectly modeling correlated data can result in increased rates of false positives or false negatives, supporting the need to model correlated data correctly. We provide an interactive Shiny App to enable accessible and appropriate analysis of longitudinal data using LME models.


Asunto(s)
Modelos Estadísticos , Animales , Simulación por Computador , Modelos Lineales , Estudios Longitudinales , Ratones
5.
Environ Monit Assess ; 194(6): 422, 2022 May 11.
Artículo en Inglés | MEDLINE | ID: mdl-35543768

RESUMEN

With the complex landform and climate in the Sichuan region, the need for practical and scientific research production by only utilising the rainfall data derived from ground stations or satellites has not been satisfied. To overcome this difficulty, rainfall data from 161 meteorological stations in 2016 are used in this study. According to the distribution of stations, 146 rainfall data from 161 meteorological stations in 2016 are used for inverse distance weighted interpolation, and then, linear regression, weighted regression, and Kalman filter fusion and optimal interpolation method data fusion are performed with TRMM 3B42 satellite rainfall data, respectively. Then, 15 meteorological stations evenly distributed in the study area are selected for the accuracy test. The results show that compared with the measurement at ground stations, linear regression shows the best merging effect on rainfall data derived from ground stations and satellite rainfall estimates across the daily scale: the correlation coefficient is the most significantly improved (0.2-0.7) and the reduction in root-mean-square error (RMSE) is the largest. The method is applicable for use in Sichuan Province when merging rainfall data. At the monthly scale, the rainfall data processed by using the Kalman filter present the highest accuracy (0.72-0.84). At this scale, the Kalman filter is more suitable.


Asunto(s)
Monitoreo del Ambiente , Lluvia , Clima , Modelos Lineales , Meteorología
6.
Transl Vis Sci Technol ; 11(5): 5, 2022 May 02.
Artículo en Inglés | MEDLINE | ID: mdl-35522306

RESUMEN

Purpose: Data postprocessing with statistical techniques that are less sensitive to noise can be used to reduce variability in visual field (VF) series. We evaluated the detection of glaucoma progression with postprocessed VF data generated with the dynamic structure-function (DSF) model and MM-estimation robust regression (MRR). Method: The study included 118 glaucoma eyes with at least 15 visits selected from the Rotterdam dataset. The DSF and MRR models were each applied to observed mean deviation (MD) values from the first three visits (V1-3) to predict the MD at V4. MD at V5 was predicted with data from V1-4 and so on until the MD at V9 was predicted, creating two additional datasets: DSF-predicted and MRR-predicted. Simple linear regression was performed to assess progression at the ninth visit. Sensitivity was evaluated by adjusting for false-positive rates estimated from patients with stable glaucoma and by using longer follow-up series (12th and 15th visits) as a surrogate for progression. Results: For specificities of 80% to 100%, the DSF-predicted dataset had greater sensitivity than the observed and MRR-predicted dataset when positive rates were normalized with corresponding false-positive estimates. The DSF-predicted and observed datasets had similar sensitivity when the surrogate reference standard was applied. Conclusions: Without compromising specificity, the use of DSF-predicted measurements to identify progression resulted in a better or similar sensitivity compared to using existing VF data. Translational Relevance: The DSF model could be applied to postprocess existing visual field data, which could then be evaluated to identify patients at risk of progression.


Asunto(s)
Glaucoma , Campos Visuales , Progresión de la Enfermedad , Glaucoma/diagnóstico , Humanos , Modelos Lineales , Pruebas del Campo Visual
7.
ScientificWorldJournal ; 2022: 9779829, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35530532

RESUMEN

Generating an accurate rainfall prediction is a challenging work due to the complexity of the climate system. Numerous efforts have been conducted to generate reliable prediction such as through ensemble forecasts, the North Multi-Model Ensemble (NMME). The performance of NMME globally has been investigated in many studies. However, its performance in a specific location has not been much validated. This paper investigates the performance of NMME to forecast rainfall in Surabaya, Indonesia. Our study showed that the rainfall prediction from NMME tends to be underdispersive, which thus requires a bias correction. We proposed a new bias correction method based on gamma regression to model the asymmetric pattern of rainfall distribution and further compared the results with the average ratio method and linear regression. This study showed that the NMME performance can be improved significantly after bias correction using the gamma regression method. This can be seen from the smaller RMSE and MAE values, as well as higher R 2 values compared with the results from linear regression and average ratio methods. Gamma regression improved the R 2 value by about 30% higher than raw data, and it is about 20% higher than the linear regression approach. This research showed that NMME can be used to improve the accuracy of rainfall forecast in Surabaya.


Asunto(s)
Clima , Ciudades , Indonesia , Modelos Lineales , Análisis de Regresión
8.
J Neural Eng ; 19(2)2022 Apr 21.
Artículo en Inglés | MEDLINE | ID: mdl-35395645

RESUMEN

Objective.Brain-computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates.Approach.Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network).Main results.The current study showed that: (a) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization) were sustained during prolonged force holding periods; (b) continuously changing grasp force can be decoded from the SEEG signals; (c) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates.Significance.This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.


Asunto(s)
Interfaces Cerebro-Computador , Electroencefalografía , Electroencefalografía/métodos , Fuerza de la Mano , Humanos , Modelos Lineales , Redes Neurales de la Computación
9.
Genome Biol ; 23(1): 95, 2022 Apr 14.
Artículo en Inglés | MEDLINE | ID: mdl-35421994

RESUMEN

Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control challenging. Here, we show that the compositional effects can be addressed by a simple, yet highly flexible and scalable, approach. The proposed method, LinDA, only requires fitting linear regression models on the centered log-ratio transformed data, and correcting the bias due to compositional effects. We show that LinDA enjoys asymptotic FDR control and can be extended to mixed-effect models for correlated microbiome data. Using simulations and real examples, we demonstrate the effectiveness of LinDA.


Asunto(s)
Escarabajos , Microbiota , Animales , Modelos Lineales , Proyectos de Investigación
10.
Math Biosci Eng ; 19(5): 4737-4748, 2022 Mar 10.
Artículo en Inglés | MEDLINE | ID: mdl-35430838

RESUMEN

This paper proposes the D-optimal design for the additive mixture model with two-response, which is linear model with no interaction terms. The optimality was validated by using the general equivalence theorem, and the corresponding weights are found under which additive model satisfies D-optimality. In addition, relevant statistics and graphics are given to illustrate our results.


Asunto(s)
Modelos Lineales
11.
Methods Mol Biol ; 2467: 157-187, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35451776

RESUMEN

Genomic prediction models are showing their power to increase the rate of genetic gain by boosting all the elements of the breeder's equation. Insight into the factors associated with the successful implementation of this prediction model is increasing with time but the technology has reached a stage of acceptance. Most genomic prediction models require specialized computer packages based mainly on linear models and related methods. The number of computer packages has exploded in recent years given the interest in this technology. In this chapter, we explore the main computer packages available to fit these models; we also review the special features, strengths, and weaknesses of the methods behind the most popular computer packages.


Asunto(s)
Genómica , Herencia Multifactorial , Computadores , Genoma , Genotipo , Modelos Lineales , Modelos Genéticos , Fenotipo
12.
Arch Microbiol ; 204(5): 282, 2022 Apr 26.
Artículo en Inglés | MEDLINE | ID: mdl-35471713

RESUMEN

Magnetotactic bacteria (MTB) use iron from their habitat to create magnetosomes, a unique organelle required for magnetotaxis. Due to a lack of cost-effective assay methods for estimating iron in magnetosomes, research on MTB and iron-rich magnetosomes is limited. A systemized assay was established in this study to quantify iron in MTB using ferric citrate colorimetric estimation. With a statistically significant R2 value of 0.9935, the iron concentration range and wavelength for iron estimation were optimized using linear regression. This colorimetric approach and the inductively coupled plasma optical emission spectrometry (ICP-OES) exhibited an excellent correlation R2 value of 0.961 in the validatory correlative study of the iron concentration in the isolated magnetotactic bacterial strains. In large-scale screening studies, this less-expensive strategy could be advantageous.


Asunto(s)
Magnetosomas , Colorimetría , Óxido Ferrosoférrico/análisis , Bacterias Gramnegativas , Hierro , Modelos Lineales , Magnetosomas/química
13.
J Plant Physiol ; 272: 153686, 2022 May.
Artículo en Inglés | MEDLINE | ID: mdl-35381493

RESUMEN

The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves. A total of 1,872 fresh and intact leaves from 13 crops were analyzed using a SPAD-502 meter and scanned using the Nix™ Pro color sensor. The color was assessed via RGB and CIELab systems. The full dataset was divided into calibration (70% of data) and validation (30% of data). For each crop and color pattern, multiple linear regression (MLR) analysis and multivariate modeling [least absolute shrinkage and selection operator (LASSO), and elastic net (ENET) regression] were employed and compared. The obtained MLR equations and multivariate models were then tested using the validation dataset based on r, R2, root mean squared error (RMSE), and mean absolute error (MAE). In both RGB and CIELab color systems, the Nix™ Pro color sensor was able to differentiate crops, and the SPAD indices were successfully predicted, mainly for mango, quinoa, peach, pear, and rice crops. Validation results indicated that ENET performed best in most crops (e.g., coffee, corn, mango, pear, rice, and soy) and very close to MLR in bean, grape, peach, and quinoa. The correlation between SPAD and greenness is crop-dependent. Overall, the Nix™ Pro color sensor was a fast, sensible and an easy way to obtain leaf color directly in the field, constituting a reliable alternative to digital camera imagery and associated image processing.


Asunto(s)
Clorofila , Oryza , Color , Modelos Lineales , Proyectos Piloto , Hojas de la Planta
14.
Dev Cogn Neurosci ; 54: 101070, 2022 04.
Artículo en Inglés | MEDLINE | ID: mdl-35395594

RESUMEN

Event-related potentials (ERPs) are advantageous for investigating cognitive development. However, their application in infants/children is challenging given children's difficulty in sitting through the multiple trials required in an ERP task. Thus, a large problem in developmental ERP research is high subject exclusion due to too few analyzable trials. Common analytic approaches (that involve averaging trials within subjects and excluding subjects with too few trials, as in ANOVA and linear regression) work around this problem, but do not mitigate it. Moreover, these practices can lead to inaccuracies in measuring neural signals. The greater the subject exclusion, the more problematic inaccuracies can be. We review recent developmental ERP studies to illustrate the prevalence of these issues. Critically, we demonstrate an alternative approach to ERP analysis-linear mixed effects (LME) modeling-which offers unique utility in developmental ERP research. We demonstrate with simulated and real ERP data from preschool children that commonly employed ANOVAs yield biased results that become more biased as subject exclusion increases. In contrast, LME models yield accurate, unbiased results even when subjects have low trial-counts, and are better able to detect real condition differences. We include tutorials and example code to facilitate LME analyses in future ERP research.


Asunto(s)
Electroencefalografía , Potenciales Evocados , Preescolar , Electroencefalografía/métodos , Humanos , Modelos Lineales
15.
Sci Rep ; 12(1): 6090, 2022 Apr 12.
Artículo en Inglés | MEDLINE | ID: mdl-35414065

RESUMEN

Phosphorylation of PI3Kγ as a member of lipid kinases-enzymes, plays a crucial role in regulating immune cells through the generation of intracellular signals. Deregulation of this pathway is involved in several tumors. In this research, diverse sets of potent and selective isoform-specific PI3Kγ inhibitors whose drug-likeness was confirmed based on Lipinski's rule of five were used in the modeling process. Genetic algorithm (GA)-based multivariate analysis was employed on the half-maximal inhibitory concentration (IC50) of them. In this way, multiple linear regression (MLR) and artificial neural network (ANN) algorithm, were used to QSAR models construction on 245 compounds with a wide range of pIC50 (5.23-9.32). The stability and robustness of the models have been evaluated by external and internal validation methods (R2 0.623-0.642, RMSE 0.464-0.473, F 40.114, Q2LOO 0.600, and R2y-random 0.011). External verification using a wide variety of structures out of the training and test sets show that ANN is superior to MLR. The descriptors entered into the model are in good agreement with the X-ray structures of target-ligand complexes; so the model is interpretable. Finally, Williams plot-based analysis was applied to simultaneously compare the inhibitory activity and structural similarity of training, test and validation sets.


Asunto(s)
Fosfatidilinositol 3-Quinasas , Relación Estructura-Actividad Cuantitativa , Modelos Lineales , Análisis Multivariante , Redes Neurales de la Computación , Fosfatidilinositol 3-Quinasa , Inhibidores de las Quinasa Fosfoinosítidos-3/farmacología
16.
Sci Rep ; 12(1): 5440, 2022 03 31.
Artículo en Inglés | MEDLINE | ID: mdl-35361850

RESUMEN

Regularized regression analysis is a mature analytic approach to identify weighted sums of variables predicting outcomes. We present a novel Coarse Approximation Linear Function (CALF) to frugally select important predictors and build simple but powerful predictive models. CALF is a linear regression strategy applied to normalized data that uses nonzero weights + 1 or - 1. Qualitative (linearly invariant) metrics to be optimized can be (for binary response) Welch (Student) t-test p-value or area under curve (AUC) of receiver operating characteristic, or (for real response) Pearson correlation. Predictor weighting is critically important when developing risk prediction models. While counterintuitive, it is a fact that qualitative metrics can favor CALF with ± 1 weights over algorithms producing real number weights. Moreover, while regression methods may be expected to change most or all weight values upon even small changes in input data (e.g., discarding a single subject of hundreds) CALF weights generally do not so change. Similarly, some regression methods applied to collinear or nearly collinear variables yield unpredictable magnitude or the direction (in p-space) of the weights as a vector. In contrast, with CALF if some predictors are linearly dependent or nearly so, CALF simply chooses at most one (the most informative, if any) and ignores the others, thus avoiding the inclusion of two or more collinear variables in the model.


Asunto(s)
Algoritmos , Área Bajo la Curva , Humanos , Modelos Lineales , Curva ROC
17.
BMC Med Res Methodol ; 22(1): 111, 2022 04 13.
Artículo en Inglés | MEDLINE | ID: mdl-35413793

RESUMEN

BACKGROUND: Cluster randomised trials often randomise a small number of units, putting them at risk of poor balance of covariates across treatment arms. Covariate constrained randomisation aims to reduce this risk by removing the worst balanced allocations from consideration. This is known to provide only a small gain in power over that averaged under simple randomisation and is likely influenced by the number and prognostic effect of the covariates. We investigated the performance of covariate constrained randomisation in comparison to the worst balanced allocations, and considered the impact on the power of the prognostic effect and number of covariates adjusted for in the analysis. METHODS: Using simulation, we examined the Monte Carlo type I error rate and power of cross-sectional, two-arm parallel cluster-randomised trials with a continuous outcome and four binary cluster-level covariates, using either simple or covariate constrained randomisation. Data were analysed using a small sample corrected linear mixed-effects model, adjusted for some or all of the binary covariates. We varied the number of clusters, intra-cluster correlation, number and prognostic effect of covariates balanced in the randomisation and adjusted in the analysis, and the size of the candidate set from which the allocation was selected. For each scenario, 20,000 simulations were conducted. RESULTS: When compared to the worst balanced allocations, covariate constrained randomisation with an adjusted analysis provided gains in power of up to 20 percentage points. Even with analysis-based adjustment for those covariates balanced in the randomisation, the type I error rate was not maintained when the intracluster correlation is very small (0.001). Generally, greater power was achieved when more prognostic covariates are restricted in the randomisation and as the size of the candidate set decreases. However, adjustment for weakly prognostic covariates lead to a loss in power of up to 20 percentage points. CONCLUSIONS: When compared to the worst balanced allocations, covariate constrained randomisation provides moderate to substantial improvements in power. However, the prognostic effect of the covariates should be carefully considered when selecting them for inclusion in the randomisation.


Asunto(s)
Ensayos Clínicos Controlados Aleatorios como Asunto , Proyectos de Investigación , Análisis por Conglomerados , Simulación por Computador , Estudios Transversales , Humanos , Modelos Lineales , Distribución Aleatoria
18.
BMC Med Res Methodol ; 22(1): 110, 2022 04 13.
Artículo en Inglés | MEDLINE | ID: mdl-35413840

RESUMEN

BACKGROUND: Systematic reviews of in-vitro studies, like any other study, can be of heterogeneous quality. The present study aimed to evaluate the methodological quality of systematic reviews of in-vitro dental studies. METHODS: We searched for systematic reviews of in-vitro dental studies in PubMed, Web of Science, and Scopus databases published up to January 2022. We assessed the methodological quality of the systematic reviews using a modified "A MeaSurement Tool to Assess systematic Reviews" (AMSTAR-2) instrument. The 16 items, in the form of questions, were answered with yes, no, or py (partial yes). Univariable and multivariable linear regression models were used to examine the association between systematic review characteristics and AMSTAR-2 percent score. Overall confidence in the results of the systematic reviews was rated, based on weaknesses identified in critical and non-critical AMSTAR-2 items. RESULTS: The search retrieved 908 potential documents, and after following the eligibility criteria, 185 systematic reviews were included. The most researched topics were ceramics and dental bonding. The overall rating for the confidence in the results was critically low in 126 (68%) systematic reviews. There was high variability in the response among the AMSTAR-2 items (0% to 75% positively answered). The univariable analyses indicated dental specialty (p = 0.03), number of authors (coef: 1.87, 95% CI: 0.26, 3.47, p = 0.02), and year of publication (coef: 2.64, 95% CI: 1.90, 3.38, p < 0.01) were significantly associated with the AMSTAR-2 percent score. Whereas, in the multivariable analysis only specialty (p = 0.01) and year of publication (coef: 2.60, 95% CI: 1.84, 3.35, p < 0.001) remained significant. Among specialties, endodontics achieved the highest AMSTAR-2 percent score. CONCLUSIONS: The methods of systematic reviews of in vitro dental studies were suboptimal. Year of publication and dental specialty were associated with AMSTAR-2 scores. The overall rating of the confidence in the results was low and critically low for most systematic reviews.


Asunto(s)
Proyectos de Investigación , Informe de Investigación , Bibliometría , Humanos , Modelos Lineales , Revisiones Sistemáticas como Asunto
19.
PLoS One ; 17(4): e0266830, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35417486

RESUMEN

Studies of training and competition load in sport are usually based on data that represents a sample of a league and or annual training program. These studies sometimes explore important factors that are affected by load, such as training adaptations and injury risk. The generalisability of the conclusions of these studies, can depend on how much load varies between seasons, training phases and teams. The interpretation of previous load studies and the design of future load studies should be influenced by an understanding of how load can vary across seasons, training phases and between teams. The current study compared training loads (session rating of perceived exertion x session duration) between all (8) teams in an elite Netball competition for multiple (2) season phases and (2) seasons. A total of 29,545 records of athlete session training loads were included in the analysis. Linear mixed models identified differences between seasons and training phases (p < .05). There were also differences between teams and a complex set of interactions between these three factors (season, phase, and team) (p < .05). While the absolute value of the training loads reported here are only relevant to elite netball, these results illustrate that when data is sampled from a broader context, the range and variation in load may increase. This highlights the importance of cautiously interpreting and generalisation of findings from load studies that use limited data sets.


Asunto(s)
Baloncesto , Adaptación Fisiológica , Humanos , Modelos Lineales , Esfuerzo Físico , Estaciones del Año
20.
Epidemiology ; 33(3): 362-371, 2022 May 01.
Artículo en Inglés | MEDLINE | ID: mdl-35383644

RESUMEN

BACKGROUND: Identifying determinants of cognitive decline is crucial for developing strategies to prevent Alzheimer's disease and related dementias. However, determinants of cognitive decline remain elusive, with inconsistent results across studies. One reason could be differential survival. Cognitive decline and many exposures of interest are associated with mortality making survival a collider. Not accounting for informative attrition can result in survival bias. Generalized estimating equations (GEE) and linear mixed-effects model (LME) are commonly used to estimate effects of exposures on cognitive decline, but both assume mortality is not informative. Joint models combine LME with Cox proportional hazards models to simultaneously estimate cognitive decline and the hazard of mortality. METHODS: Using simulations, we compared estimates of the effect of a binary exposure on rate of cognitive decline from GEE, weighted GEE using inverse-probability-of-attrition weights, and LME to joint models under several causal structures of survival bias. RESULTS: We found that joint models with correctly specified relationship between survival and cognition performed best, producing unbiased estimates and appropriate coverage. Even those with misspecified relationship between survival and cognition showed advantage under causal structures consistent with survival bias. We also compared these models in estimating the effect of education on cognitive decline after dementia diagnosis using Framingham Heart Study data. Estimates of the effect of education on cognitive decline from joint models were slightly attenuated with similar precision compared with LME. CONCLUSIONS: In our study, joint models were more robust than LME, GEE, and weighted GEE models when evaluating determinants of cognitive decline.


Asunto(s)
Disfunción Cognitiva , Sesgo , Disfunción Cognitiva/epidemiología , Simulación por Computador , Humanos , Modelos Lineales , Estudios Longitudinales
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