Search results for "random effect"
showing 10 items of 57 documents
Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study
2014
We present a simple and effective iterative procedure to estimate segmented mixed models in a likelihood based framework. Random effects and covariates are allowed for each model parameter, including the changepoint. The method is practical and avoids the computational burdens related to estimation of nonlinear mixed effects models. A conventional linear mixed model with proper covariates that account for the changepoints is the key to our estimating algorithm. We illustrate the method via simulations and using data from a randomized clinical trial focused on change in depressive symptoms over time which characteristically show two separate phases of change.
Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data
2008
Remote sensing is a helpful tool for crop monitoring or vegetation-growth estimation at a country or regional scale. However, satellite images generally have to cope with a compromise between the time frequency of observations and their resolution (i.e. pixel size). When concerned with high temporal resolution, we have to work with information on the basis of kilometric pixels, named mixed pixels, that represent aggregated responses of multiple land cover. Disaggreggation or unmixing is then necessary to downscale from the square kilometer to the local dynamic of each theme (crop, wood, meadows, etc.). Assuming the land use is known, that is to say the proportion of each theme within each m…
Long-term experiments and strip plot designs
2015
In a long-term experiment usually the experimenter needs to know whether the effect of a treatment varies over time. But time usually has both a fixed and a random effects over the output and the difficulty in the analysis depends on the particular design considered and the availability of covariates. Actually, as shown in the paper, the presence of covariates can be very useful to model the random effect of time. In this paper a model to analyze data from a long-term strip plot design with covariates is proposed. Its effectiveness will be tested using both simulated and real data from a crop rotation experiment.
Bayesian longitudinal models for paediatric kidney transplant recipients
2015
Chronic kidney disease is a progressive loss of renal function which results in the inability of the kidneys to properly filter waste from the blood. Renal function is usually estimated by the glomerular filtration rate (eGFR), which decreases with the worsening of the disease. Bayesian longitudinal models with covariates, random effects, serial correlation and measurement error are discussed to analyse the progression of eGFR in first transplanted children taken from a study in Valencia, Spain.
Time-to-event analysis of mastitis at first-lactation in Valle del Belice ewes
2007
A time-to-event study for mastitis at first-lactation in Valle del Belice ewes was conducted, using survival analysis with an animal model. The goals were to evaluate the effect of lambing season and level of milk production on the time from lambing to the day when a ewe experienced a test-day with a recorded SCC greater than or equal to 750,000 cells/ml, and to estimate, for this trait, its heritability and the percentage of variation explained by the flock-year of lambing effect. A dataset with 2468 first-lactation records, collected from 1998 to 2003 in Valle del Belice ewes allocated in 17 flocks, was used. The Cox model used included lambing season and total milk yield adjusted for lac…
On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations
2019
Entities such as individuals, teams, or organizations can vary systematically from one another. Researchers typically model such data using multilevel models, assuming that the random effects are uncorrelated with the regressors. Violating this testable assumption, which is often ignored, creates an endogeneity problem thus preventing causal interpretations. Focusing on two-level models, we explain how researchers can avoid this problem by including cluster means of the Level 1 explanatory variables as controls; we explain this point conceptually and with a large-scale simulation. We further show why the common practice of centering the predictor variables is mostly unnecessary. Moreover, …
Bayesian reanalysis of a quantitative trait locus accounting for multiple environments by scaling in broilers1
2006
A Bayesian method was developed to handle QTL analyses of multiple experimental data of outbred populations with heterogeneity of variance between sexes for all random effects. The method employed a scaled reduced animal model with random polygenic and QTL allelic effects. A parsimonious model specification was applied by choosing assumptions regarding the covariance structure to limit the number of parameters to estimate. Markov chain Monte Carlo algorithms were applied to obtain marginal posterior densities. Simulation demonstrated that joint analysis of multiple environments is more powerful than separate single trait analyses of each environment. Measurements on broiler BW obtained from…
How common is bipolar disorder in general primary care attendees? A systematic review and meta-analysis investigating prevalence determined according…
2016
Objective: There are mounting calls for bipolar disorder to be managed in primary care, yet the exact prevalence remains unclear. We conducted a meta-analysis to investigate the prevalence of bipolar disorder in general primary care attendees without other comorbid psychiatric diagnosis. Method: We systematically searched major electronic databases from inception till 03/2015. Articles were included that reported the prevalence of bipolar disorder determined in line with structured clinical assessment in primary care settings. Two independent authors conducted searches, completed methodological appraisal and extracted data. A random effects meta-analysis and meta-regression were performed. …
Mortality associated with cardiovascular disease in patients with COVID-19
2020
Introducción y objetivos: Las enfermedades cardiovasculares (ECV) se han identificado como un factor de riesgo de mal pronóstico en pacientes con infección por COVID-19. Métodos: Se realizó un metanálisis de estudios actualmente disponibles con la prevalencia de ECV en supervivientes frente a no supervivientes en pacientes con infección por COVID-19 hasta el 16 de julio de 2020. Los análisis se realizaron mediante un modelo de efectos aleatorios y sensibilidad. Se realizaron análisis para identificar posibles fuentes de heterogeneidad o evaluar los efectos de los estudios pequeños. Resultados: Se incluyó a 307.596 pacientes de 16 estudios, de los que 46.321 (15,1%) tenían ECV. La tasa de mo…
Effects of motor imagery on strength, range of motion, physical function, and pain intensity in patients with total knee arthroplasty: A systematic r…
2021
Abstract Background In the early stages of total knee arthroplasty (TKA) rehabilitation, in which physical function in general can be affected, motor imagery (MI) might play a relevant role. Objective To assess the impact of MI on strength, active range of motion (ROM), pain intensity, and physical function in patients with TKA. Methods We conducted a systematic review and meta-analysis of randomised controlled trials. Pooled effects were calculated as standardised mean differences (SMDs) and 95% confidence intervals (CIs) for the relevant outcomes using random effects model. The certainty of evidence was assessed with GRADE approach. Results This review included 7 articles. The addition of…