SEARCH · Search PubMed

Results for “linear mixed model”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11Linked to original sources

PubMed1998

Incidences and effects of diseases on the performance of Swedish dairy herds stratified by production.

Incidences of diseases and their effects on reproductive performance and risk of culling in herds stratified by production and estrus detection efficiency were studied. Data were from the Swedish milk and disease recording systems and consisted of records for 33,748 first parity Swedish Friesian cows. A standardized mixed threshold model was used for statistical analyses of categorical outcome variables, and an ordinary linear mixed model was used for continuous outcome variables. An increase in production was associated with increased frequencies of treatments of most diseases, shorter intervals from calving to first artificial insemination, fewer days open, and lower culling rates. Cows treated for metritis, silent estrus, and cystic ovaries had an increased number of days to first artificial insemination and more days open. However, the negative consequences of these diseases on reproductive performance decreased as herd production increased. The risk of culling was higher for cows treated for dystocia, cystic ovaries, and mastitis, but the increase in the risk of culling was lower for higher producing herds. Similar trends were observed when herds were stratified by estrus detection efficiency. The results support the hypothesis that herd management, as characterized by milk production or estrus detection efficiency, is important in the incidences and consequences of diseases. Herd management, measured directly or indirectly, should be considered when the health status or cost of disease for a given herd is evaluated.

Animals↗

PubMedDate not supplied

Prediction error variance and expected response to selection, when selection is based on the best predictor - for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits.

In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed or random effects). In the different models, expressions are given (when these can be found - otherwise unbiased estimates are given) for prediction error variance, accuracy of selection and expected response to selection on the additive genetic scale and on the observed scale. The expressions given for non Gaussian traits are generalisations of the well-known formulas for Gaussian traits - and reflect, for Poisson mixed models and frailty models for survival data, the hierarchal structure of the models. In general the ratio of the additive genetic variance to the total variance in the Gaussian part of the model (heritability on the normally distributed level of the model) or a generalised version of heritability plays a central role in these formulas.

Analysis of Variance↗

PubMed2003

Analysis of a Bayesian repeated measures model for detecting differences in GP prescribing habits.

A linear mixed model is used to detect a change, if any, in the prescribing habits in the UK at the general practice (family medicine) level due to an educational intervention given repeated measures data before and after the intervention and a control group. Inferences are corrected for general practice size and fundholding status. The estimates of the model parameters are obtained using Bayesian inference by applying Gibbs sampling. We develop three different priors for the parameters of the model. These three priors correspond to 'sceptical,' 'reference' and 'enthusiastic' priors in terms of the opinion about the treatment effects that they represent. We compare the results obtained by using these three priors for the parameters in the random effects model.

Anti-Inflammatory Agents, Non-Steroidal↗

PubMed2006

Preliminary evidence of riluzole efficacy in antidepressant-treated patients with residual depressive symptoms.

BACKGROUND: Excessive glutamatergic neurotransmission may contribute to the pathophysiology of major depressive disorder (MDD). Recent evidence suggests that riluzole and other agents that target glutamate neurotransmission may show antidepressant activity. METHODS: Ten patients with treatment-resistant depression had riluzole added to their ongoing medication regimen for 6 weeks, followed by an optional 6-week continuation phase. Depression and anxiety severity were assessed using the Hamilton Depression Rating Scale (HDRS) and the Hamilton Anxiety Rating Scale (HARS). Linear mixed models were used to test for a linear trend in HDRS and HARS scores across time with treatment. RESULTS: Subjects' HDRS and HARS scores declined significantly following the initiation of riluzole augmentation therapy. The effect of riluzole was significant at the end of the first week of treatment and persisted for the 12-week duration of the study. CONCLUSIONS: These data suggest that riluzole augmentation produces antidepressant and anxiolytic effects in patients with treatment-resistant depression.

Adult↗

PubMed2006

Statistical analysis of test-day milk yields using random regression models for the comparison of feeding groups during the lactation period.

Random regression models are widely used in the field of animal breeding for the genetic evaluation of daily milk yields from different test days. These models are capable of handling different environmental effects on the respective test day, and they describe the characteristics of the course of the lactation period by using suitable covariates with fixed and random regression coefficients. As the numerically expensive estimation of parameters is already part of advanced computer software, modifications of random regression models will considerably grow in importance for statistical evaluations of nutrition and behaviour experiments with animals. Random regression models belong to the large class of linear mixed models. Thus, when choosing a model, or more precisely, when selecting a suitable covariance structure of the random effects, the information criteria of Akaike and Schwarz can be used. In this study, the fitting of random regression models for a statistical analysis of a feeding experiment with dairy cows is illustrated under application of the program package SAS. For each of the feeding groups, lactation curves modelled by covariates with fixed regression coefficients are estimated simultaneously. With the help of the fixed regression coefficients, differences between the groups are estimated and then tested for significance. The covariance structure of the random and subject-specific effects and the serial correlation matrix are selected by using information criteria and by estimating correlations between repeated measurements. For the verification of the selected model and the alternative models, mean values and standard deviations estimated with ordinary least square residuals are used.

Animal Feed↗

PubMed2001

Bayesian analysis of a time series of counts with covariates: an application to the control of an infectious disease.

This paper presents a Bayesian analysis of a time series of counts to assess its dependence on an explanatory variable. The time series represented is the incidence of the infectious disease ESBL-producing Klebsiella pneumoniae in an Australian hospital and the explanatory variable is the number of grams of antibiotic (third generation) cephalosporin used during that time. We demonstrate that there is a statistically significant relationship between disease occurrence and use of the antibiotic, lagged by three months. The model used is a parameter-driven model in the form of a generalized linear mixed model. Comparison of models is made in terms of mean square error.

Journal Article↗

PubMed2004

Contributions of Portuguese cattle breeds to genetic diversity using marker-estimated kinships.

The quantitative assessment of genetic diversity within and between populations is important for decision-making in genetic conservation plans. In our study, we applied the livestock core set method to define the contribution of 15 cattle breeds, 11 of which are Portuguese indigenous cattle breeds, to genetic diversity. In livestock core set theory genetic diversity is defined as the maximum genetic variance that can be obtained in a random-mating population that is bred from the populations present in that core set. Two methods to estimate marker-estimated kinships to obtain the contributions to the core set were used in this study: the weighted log-linear model (WLM) and the weighted log-linear mixed model (WLMM). The breeds that contributed most to diversity in the core set were Holstein-Friesian followed by the Portuguese Mertolenga and Cachena for both WLM and WLMM methods. The ranking of relative contributions of cattle breeds was maintained when we considered only the Portuguese cattle breeds. Furthermore, we were able to identify the marginal contributions and respective losses of diversity for each of the 11 Portuguese cattle breeds when we considered a subset of populations that are not threatened of being lost (the Safe set composed of the four exotic breeds present in this study). When WLM was used losses in genetic diversity ranged from 2.68 to 0.65% while the loss in founder genome equivalents ranged from 37.37 to 8.43% for Mertolenga and Brava de Lide breeds respectively. When WLMM was used losses in genetic diversity and founder genome equivalents were less extreme than for the WLM method, ranging from 1.27 to 0.69 and 26.8 to 12.99 respectively.

Animals↗

PubMed2005

Natural selection and genetic variation for reproductive reaction norms in a wild bird population.

Many morphological and life-history traits show phenotypic plasticity that can be described by reaction norms, but few studies have attempted individual-level analyses of reaction norms in the wild. We analyzed variation in individual reaction norms between laying date and three climatic variables (local temperature, local rainfall, and North Atlantic Oscillation) of 1126 female collared flycatchers (Ficedula albicollis) with a restricted maximum likehood linear mixed model approach using random-effect best linear unbiased predictor estimates for the elevation (i.e., expected laying date in the average environment) and slope (i.e., adjustment in laying date as a function of environment) of females' reaction norms. Variation in laying date was best explained by local temperature, and individual females differed in both the elevation and the slope of their laying date-temperature reaction norms. As revealed by animal model analyses, there was weak evidence for additive genetic variance of elevation (h2 +/- SE = 0.09 +/- 0.09), whereas there was no evidence for heritability of slope (h2 +/- SE = 0.00 +/- 0.01). Selection analysis, using a female's lifetime production of fledglings or recruits as an estimate of her fitness, revealed significant selection for a lower phenotypic value and breeding value for elevation (i.e., earlier laying date at the average temperature). There was selection for steeper phenotypic values of slope (i.e., greater plasticity in the adjustment of laying date to temperature), but no significant selection on the breeding values of slope. Although these results suggest that phenotypic laying date is influenced by additive genetic factors, as well as by an interaction with the environment, selection on plasticity would not produce an evolutionary response.

Animals↗

PubMed2006

The analysis of longitudinal data using mixed model L-splines.

L-splines are a large family of smoothing splines defined in terms of a linear differential operator. This article develops L-splines within the context of linear mixed models and uses the resulting mixed model L-spline to analyze longitudinal data from a grassland experiment. In the spirit of time-series analysis, a periodic mixed model L-spline is developed, which partitions data into a smooth periodic component plus smooth long-term trend.

Biomass↗

PubMed2003

Spatial prediction of counts and rates.

In this paper we provide both theoretical and empirical comparisons of marginal and conditional methods for analysing spatial count data. We focus on methods for spatial prediction developed from a generalized linear mixed model framework and compare them with the traditional linear (kriging) predictor. Prediction methods are illustrated and compared through a case study based on real data and through a detailed simulation study. The paper emphasizes a better understanding of the strengths and weaknesses of each approach.

Computer Simulation↗

PubMed2005

Optimal haplotype structure for linkage disequilibrium-based fine mapping of quantitative trait loci using identity by descent.

A linkage disequilibrium-based method for fine mapping quantitative trait loci (QTL) has been described that uses similarity between individuals' marker haplotypes to determine if QTL alleles are identical by descent (IBD) to model covariances among individuals' QTL alleles for a mixed linear model. Mapping accuracy with this method was found to be sensitive to the number of linked markers that was included in the haplotype when fitting the model at a putative position of the QTL. The objective of this study was to determine the optimal haplotype structure for this IBD-based method for fine mapping a QTL in a previously identified QTL region. Haplotypes consisting of 1, 2, 4, 6, or all 10 available markers were fit as a "sliding window" across the QTL region under ideal and nonideal simulated population conditions. It was found that using haplotypes of 4 or 6 markers as a sliding "window" resulted in the greatest mapping accuracy under nearly all conditions, although the true IBD state at a putative QTL position was most accurately predicted by IBD probabilities obtained using all markers. Using 4 or 6 markers resulted in greater discrimination of IBD probabilities between positions while maintaining sufficient accuracy of IBD probabilities to detect the QTL. Fitting IBD probabilities on the basis of a single marker resulted in the worst mapping accuracy under all conditions because it resulted in poor accuracy of IBD probabilities. In conclusion, for fine mapping using IBD methods, marker information must be used in a manner that results in sensitivity of IBD probabilities to the putative position of the QTL while maintaining sufficient accuracy of IBD probabilities to detect the QTL. Contrary to expectation, use of haplotypes of 4-6 markers to derive IBD probabilities, rather than all available markers, best fits these criteria. Thus for populations similar to those simulated here, optimal mapping accuracy for this IBD-based fine-mapping method is obtained with a haplotype structure including a subset of all available markers.

Crosses, Genetic↗

PubMed2005

Evaluation by simulation of tests based on non-linear mixed-effects models in pharmacokinetic interaction and bioequivalence cross-over trials.

We propose tests based on non-linear mixed effects models (NLMEM) in pharmacokinetic interaction and bioequivalence cross-over trials comparing two treatments or two formulations. To compare the logarithm of the area under the curve (AUC) using these models, two approaches are studied: in the first one, concentration data are analysed globally, with and without the estimation of a treatment effect; and in the second one, they are analysed separately in each treatment group with the estimation of the individual parameters. Four tests for comparison of the logarithm AUC between two treatment arms are studied: a likelihood-ratio test (LRT), a Wald test and two tests, parametric and non-parametric, comparing the individual Empirical Bayes (EB) estimates. These tests are adapted to the case of equivalence, except the LRT which does not have any simple extension. We evaluate by simulation of the type I error and the power for both comparison and equivalence tests. They are compared to the standard tests recommended by the FDA and the EMEA, based on non-compartmental (NC) AUC. Trials for a usual PK model are simulated under H(0) and several H(1) using S-plus software and analysed with the nlme function. Different configurations of the number of subjects (n=12, 24 and 40) and of the number of samples per subject (J=10, 5 and 3) are studied. The type I error alpha of LRT and Wald comparison test in the 5000 replications of interaction cross-over trials is found to be 20.9 per cent and 21.7 per cent, respectively, in the original design (n=12, J=10), which is far superior to 5 per cent, and decreases when n increases. When n is fixed, alpha is found to increase with J. Power is satisfactory for both tests, after correction of the significance threshold. Results of EB and NC tests are similar with satisfactory powers and a type I error close to 5 per cent, except when J=3 for EB tests. Similar results are obtained for equivalence tests, except for EB and NC Student tests, which are not of a great interest. NC tests keep their place when the number of samples per subject J is large, but NLMEM seem useful for cross-over studies performed in special populations where J limited; the evaluation by Monte-Carlo simulations of empirical threshold seems however necessary because of the inflation of the type I error.

Area Under Curve↗

PubMed2001

Optimal design for linear interpolation of curves.

Non-parametric procedures are often used for the analysis of pharmacokinetic trials. Fewer design procedures are available for non-parametric estimation than for parametric estimation. Linear interpolation is widely used for curve estimation in pharmacokinetic trials, where often only sparse sampling is feasible. Current design procedures for smoothing or local fit are not suitable as they are based on asymptotic properties and the bias of the estimate is ignored. This paper proposes optimal designs that minimize the mean squared error of linear interpolation. Optimal designs for three situations are considered. The first situation is single curve estimation based on an ordinary non-linear model. The second is estimating several curves in a non-linear mixed model setting using an average mean squared error as the design criterion. The third situation is destructive sampling where estimating the average curve is the main purpose. In the first situation, the design results in the best linear interpolation when the variance is constant. For the destructive sampling design, an algorithm based on approximations is proposed. This algorithm can be programmed in a common statistical package. Numerical examples are used to illustrate the design procedure.

Algorithms↗

PubMed2000

Proportional hazards model with random effects.

We propose a general proportional hazards model with random effects for handling clustered survival data. This generalizes the usual frailty model by allowing a multivariate random effect with arbitrary design matrix in the log relative risk, in a way similar to the modelling of random effects in linear, generalized linear and non-linear mixed models. The distribution of the random effects is generally assumed to be multivariate normal, but other (preferably symmetrical) distributions are also possible. Maximum likelihood estimates of the regression parameters, the variance components and the baseline hazard function are obtained via the EM algorithm. The E-step of the algorithm involves computation of the conditional expectations of functions of the random effects, for which we use Markov chain Monte Carlo (MCMC) methods. Approximate variances of the estimates are computed by Louis' formula, and posterior expectations and variances of the individual random effects can be obtained as a by-product of the estimation. The inference procedure is exemplified on two data sets.

Algorithms↗

PubMedDate not supplied

Statistical power analysis for hemodynamic cardiovascular safety pharmacology studies in beagle dogs.

INTRODUCTION: We studied the statistical power of a replicated Latin square design where eight animals each receive a vehicle control and three dose levels of a drug on four separate dosing days. Cardiovascular parameters evaluated in the study were systolic arterial pressure, diastolic arterial pressure, left ventricular heart rate, and dP/dt(max). METHODS: Observations were simulated based on historical data and drug response profiles from cardiovascular safety pharmacology studies conducted at Lilly Research Laboratories. Statistical analysis for treatment effects was performed using a linear mixed model. Monotonicity of dose response was examined using sequential linear trend tests based on ordinal spacing of dose levels. RESULTS: The replicated Latin square design for cardiovascular safety pharmacology studies is shown to have at least an 80% power of detecting changes from control of at least a 10% increment in systolic and diastolic pressure and a 15% increment in heart rate and dP/dt(max). The power is not sensitive to the shape of dose response profile over time. DISCUSSION: Several unique features of our statistical power evaluation include the comparison of different covariance structures and drug response profiles. The procedure can also be applied to future power evaluations of other cardiovascular parameters, such as the QT interval, and the loss of statistical power due to missing observations.

Animals↗

PubMed2000

Semiparametric regression for periodic longitudinal hormone data from multiple menstrual cycles.

We consider semiparametric regression for periodic longitudinal data. Parametric fixed effects are used to model the covariate effects and a periodic nonparametric smooth function is used to model the time effect. The within-subject correlation is modeled using subject-specific random effects and a random stochastic process with a periodic variance function. We use maximum penalized likelihood to estimate the regression coefficients and the periodic nonparametric time function, whose estimator is shown to be a periodic cubic smoothing spline. We use restricted maximum likelihood to simultaneously estimate the smoothing parameter and the variance components. We show that all model parameters can be easily obtained by fitting a linear mixed model. A common problem in the analysis of longitudinal data is to compare the time profiles of two groups, e.g., between treatment and placebo. We develop a scaled chi-squared test for the equality of two nonparametric time functions. The proposed model and the test are illustrated by analyzing hormone data collected during two consecutive menstrual cycles and their performance is evaluated through simulations.

Biometry↗

PubMed2001

Multilevel models for survival analysis with random effects.

A method for modeling survival data with multilevel clustering is described. The Cox partial likelihood is incorporated into the generalized linear mixed model (GLMM) methodology. Parameter estimation is achieved by maximizing a log likelihood analogous to the likelihood associated with the best linear unbiased prediction (BLUP) at the initial step of estimation and is extended to obtain residual maximum likelihood (REML) estimators of the variance component. Estimating equations for a three-level hierarchical survival model are developed in detail, and such a model is applied to analyze a set of chronic granulomatous disease (CGD) data on recurrent infections as an illustration with both hospital and patient effects being considered as random. Only the latter gives a significant contribution. A simulation study is carried out to evaluate the performance of the REML estimators. Further extension of the estimation procedure to models with an arbitrary number of levels is also discussed.

Biometry↗

PubMedDate not supplied

The PX-EM algorithm for fast stable fitting of Henderson's mixed model.

This paper presents procedures for implementing the PX-EM algorithm of Liu, Rubin and Wu to compute REML estimates of variance covariance components in Henderson's linear mixed models. The class of models considered encompasses several correlated random factors having the same vector length e.g., as in random regression models for longitudinal data analysis and in sire-maternal grandsire models for genetic evaluation. Numerical examples are presented to illustrate the procedures. Much better results in terms of convergence characteristics (number of iterations and time required for convergence) are obtained for PX-EM relative to the basic EM algorithm in the random regression.

Journal Article↗