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At least 217 records · Page 12Linked to original sources
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↗
PubMed2001

Linkage and association with structural relationships.

The use of structural equations (path analysis) provides an alternative, equivalent formulation to variance components models. Instead of partitioning the variance, we focus on modeling the underlying random variables themselves through a system of linear, mixed model, regression equations. A few specific examples of genetic path models for linkage and association (linkage disequilibrium) are discussed. This formulation provides a simple yet elegant framework that can continue to be extended to meet the challenges of modeling and dissecting the genetic nature of complex traits in the new century.

Chromosome Mapping↗
PubMed2001

Use and cost of hospitalization of patients with AD by stage and living arrangement: CERAD XXI.

OBJECTIVE: To determine the probability, frequency, length of stay, and Medicare costs of hospitalization of institutionalized and noninstitutionalized patients with AD at various stages of dementia. METHODS: The authors analyzed the 1991 to 1995 Medicare records of 420 CERAD patients with AD, a group which, at entry, had no serious comorbidities. They were geographically distributed across the United States and observed for a median of 2.5 years. Repeated measures logistic regression and generalized estimating equations were used to model the probability of hospitalization. Among those hospitalized, the general linear mixed model was used to determine number of admissions, length of stay, and Medicare cost. Demographic characteristics and calendar date were controlled in all analyses. RESULTS: As dementia worsened, the probability of hospitalization increased among patients living at home, but decreased among those who were institutionalized. Number of admissions, length of stay, and cost also decreased significantly as stage worsened among the institutionalized patients, but the stage of dementia had no effect in non-institutionalized patients. CONCLUSION: The hospitalization experience of patients with AD living at home differs from that of patients with AD living in institutions. Residential setting appears to be an important determinant of hospitalization in patients with AD.

Aged↗
PubMed2000

Analysis of left-censored longitudinal data with application to viral load in HIV infection.

The classical model for the analysis of progression of markers in HIV-infected patients is the mixed effects linear model. However, longitudinal studies of viral load are complicated by left censoring of the measures due to a lower quantification limit. We propose a full likelihood approach to estimate parameters from the linear mixed effects model for left-censored Gaussian data. For each subject, the contribution to the likelihood is the product of the density for the vector of the completely observed outcome and of the conditional distribution function of the vector of the censored outcome, given the observed outcomes. Values of the distribution function were computed by numerical integration. The maximization is performed by a combination of the Simplex algorithm and the Marquardt algorithm. Subject-specific deviations and random effects are estimated by modified empirical Bayes replacing censored measures by their conditional expectations given the data. A simulation study showed that the proposed estimators are less biased than those obtained by imputing the quantification limit to censored data. Moreover, for models with complex covariance structures, they are less biased than Monte Carlo expectation maximization (MCEM) estimators developed by Hughes (1999) Mixed effects models with censored data with application to HIV RNA Levels. Biometrics 55, 625-629. The method was then applied to the data of the ALBI-ANRS 070 clinical trial for which HIV-1 RNA levels were measured with an ultrasensitive assay (quantification limit 50 copies/ml). Using the proposed method, estimates obtained with data artificially censored at 500 copies/ml were close to those obtained with the real data set.

Journal Article↗
PubMed1992

Correlated responses in reproduction accompanying selection for milk yield in Jerseys.

Reproductive traits of heifers and primiparous cows from a long-term selection project were analyzed to determine correlated response to single-trait selection for milk yield. Data were from 1056 daughters (765 selection, 291 control) of 37 bulls (17 selection, 20 control). Traits in heifers were ages at first observed estrus and at first breeding, services to conception, interval from first service to conception, and length of first gestation. Traits in primiparous cows were ages at first calving and at first breeding, after calving; services to conception; length of second gestation; and intervals from calving to first observed estrus, to first breeding, and to conception, from first service to conception, and from first to second calving. Analyses for services to conception in heifers and primiparous cows were categorical using models containing genetic group and generation. Analyses of other traits were by linear mixed models using fixed effects of genetic group, generation within group, and year-season of birth. Sires were assumed random and nested within genetic group. The mean square for sires within group was used to test for group differences. No significant differences were found between genetic groups in traits measured in heifers; however, the interval from first service to conception approached significance (control superior). In primiparous cows, differences between genetic groups were significant for the intervals of calving to first breeding and calving to conception and for length of second gestation (control superior). For other traits, reproductive performance of the control was better but not significantly different from that of the selected group. Reproductive performance should be monitored during selection for high milk yield.

Age Factors↗
PubMed2006

Low-rank scale-invariant tensor product smooths for generalized additive mixed models.

A general method for constructing low-rank tensor product smooths for use as components of generalized additive models or generalized additive mixed models is presented. A penalized regression approach is adopted in which tensor product smooths of several variables are constructed from smooths of each variable separately, these "marginal" smooths being represented using a low-rank basis with an associated quadratic wiggliness penalty. The smooths offer several advantages: (i) they have one wiggliness penalty per covariate and are hence invariant to linear rescaling of covariates, making them useful when there is no "natural" way to scale covariates relative to each other; (ii) they have a useful tuneable range of smoothness, unlike single-penalty tensor product smooths that are scale invariant; (iii) the relatively low rank of the smooths means that they are computationally efficient; (iv) the penalties on the smooths are easily interpretable in terms of function shape; (v) the smooths can be generated completely automatically from any marginal smoothing bases and associated quadratic penalties, giving the modeler considerable flexibility to choose the basis penalty combination most appropriate to each modeling task; and (vi) the smooths can easily be written as components of a standard linear or generalized linear mixed model, allowing them to be used as components of the rich family of such models implemented in standard software, and to take advantage of the efficient and stable computational methods that have been developed for such models. A small simulation study shows that the methods can compare favorably with recently developed smoothing spline ANOVA methods.

Analysis of Variance↗
PubMed1999

Evaluation of carcass, live, and real-time ultrasound measures in feedlot cattle: I. Assessment of sex and breed effects.

Carcass and live-animal measures from 1,029 cattle were collected at the Iowa State University Rhodes and McNay research farms over a 6-yr period. Data were from bull, heifer, and steer progeny of composite, Angus, and Simmental sires mated to three composite lines of dams. The objectives of this study were to estimate genetic parameters for carcass traits, to evaluate effects of sex and breed of sire on growth models (curves), and to suggest a strategy to adjust serially measured data to a constant age end point. Estimation of genetic parameters using a three-trait mixed model showed differences between bulls and steers in estimates of h2 and genetic correlations. Heritability for carcass weight, percentage of retail product, retail product weight, fat thickness, and longissimus muscle area from bull data were .43, .04, .46, .05, and .21, respectively. The corresponding values for steer data were in order of .32, .24, .40, .42, and .07, respectively. Analysis of serially measured fat thickness, longissimus muscle area, body weight, hip height, and ultrasound percentage of intramuscular fat using a repeated measures model showed a limitation in the use of growth models based on pooled data. In further evaluation of regression parameters using a linear mixed model analysis, sex and breed of sire showed an important (P < .05) effect on intercept and slope values. Regression of serially measured traits on age within animal showed a relatively larger R2 (62 to 98%) and a smaller root mean square error (RMSE, .09 to 8.85) as compared with R2 (0 to 58%) and RMSE (.31 to 67.9) values when the same model was used on pooled data. We concluded that regression parameters from a within-animal regression of a serially measured trait on age, averaged by sex and breed, are the best choice in describing growth and adjusting data to a constant age end point.

Animal Feed↗
PubMed2005

Survival analysis applied to genetic evaluation for female fertility in dairy cattle.

The objective of this research was to study whether survival analysis results in a more accurate genetic evaluation for female fertility traits compared with the usual methodology based on linear models. The fertility trait studied was interval between calving and last insemination. A stochastic simulation describing the reproductive cycle of first-parity cows was done, in which true breeding values for conception rate were created. A model containing effects of sire and herd was used both with survival analysis and with mixed linear model analysis to predict sire breeding values. Correlations between true breeding values for conception rate and breeding values for calving to last insemination predicted by the best survival analysis model or the best linear model were 0.77 and 0.68, respectively. The results showed that when pregnancy status is known, survival analysis is a better method than linear models for genetic evaluation of conception rate when using observations on the interval between calving and last insemination.

Animals↗
PubMed2003

Modeling the training-performance relationship using a mixed model in elite swimmers.

PURPOSE: The aim of this study was to model the relationship between training and performance in 13 competitive swimmers, over three seasons, and to identify individual and group responses to training. METHODS: A linear mixed model was used as an alternative to the Banister model. Training effect on performance was studied over three training periods: short-term, the average of training load accomplished during the 2 wk preceding each performance of the studied period; mid-term, the average of training load accomplished during weeks 3, 4, and 5 before each performance; and long-term, weeks 6, 7, and 8. RESULTS: Cluster analysis identified four groups of subjects according to their reactions to training. The first group corresponded to the subjects who responded well to the long-term training period, the second group to the long- and mid-term periods, the third to the short- and mid-term periods, and the fourth to the combined periods. In the model, the intersubject differences and the evolution over the three seasons were statistically significant for the identified groups of swimmers. Influence of short-term training was negative on performance in the four groups, whereas mid- and long-term training had, on the average, a positive effect in three groups out of four. Between seasons 1 and 3, the effect of mid-term training declined, whereas the effect of long-term training increased. The fit between real and modeled performances was significant for all swimmers (0.15 </= r2 </= 0.65; P </= 0.01). CONCLUSION: The mixed model described a significant relationship between training and performance both for individuals and for groups of swimmers. This relationship was different over the 3 yr. Personalized training schedules could be prescribed on the basis of the model results.

Adult↗
PubMed2004

Limitations of ordinary least squares models in analyzing repeated measures data.

PURPOSE: To a) introduce and present the advantages of linear mixed models using generalized least squares (GLS) when analyzing repeated measures data; and b) show how model misspecification and an inappropriate analysis using repeated measures ANOVA with ordinary least squares (OLS) methodology can negatively impact the probability of occurrence of Type I error. METHODS: The effects of three strength-training groups were simulated. Strength gains had two slope conditions: null (no gain), and moderate (moderate gain). Ten subjects were hypothetically measured at five time points, and the correlation between measurements within a subject was modeled as compound symmetric (CS), autoregressive lag 1 (AR(1)), and random coefficients (RC). A thousand data sets were generated for each correlation structure. Then, each was analyzed four times--once using OLS, and three times using GLS, assuming the following variance/covariance structures: CS, AR(1), and RC. RESULTS: OLS produced substantially inflated probabilities of Type I errors when the variance/covariance structure of the data set was not CS. The RC model was less affected by the actual variance/covariance structure of the data set, and gave good estimates across all conditions. CONCLUSIONS: Using OLS to analyze repeated measures data is inappropriate when the covariance structure is not known to be CS. Random coefficients growth curve models may be useful when the variance/covariance structure of the data set is unknown.

Endpoint Determination↗
PubMed2000

A latent class mixed model for analysing biomarker trajectories with irregularly scheduled observations.

This paper considers a latent class model to uncover subpopulation structure for both biomarker trajectories and the probability of disease outcome in highly unbalanced longitudinal data. A specific pattern of trajectories can be viewed as a latent class in a finite mixture where membership in latent classes is modelled with a polychotomous logistic regression. The biomarker trajectories within a latent class are described by a linear mixed model with possibly time-dependent covariates and the probabilities of disease outcome are estimated via a class specific model. Thus the method characterizes biomarker trajectory patterns to unveil the relationship between trajectories and outcomes of disease. The coefficients for the model are estimated via a generalized EM (GEM) algorithm, a natural tool to use when latent classes and random coefficients are present. Standard errors of the coefficients are calculated using a parametric bootstrap. The model fitting procedure is illustrated with data from the Nutritional Prevention of Cancer trials; we use prostate specific antigen (PSA) as the biomarker for prostate cancer and the goal is to examine trajectories of PSA serial readings in individual subjects in connection with incidence of prostate cancer.

Adolescent↗
PubMed2002

A hierarchical Poisson mixture regression model to analyse maternity length of hospital stay.

Inpatient length of stay (LOS) is often considered as a proxy of hospital resource consumption. Using statewide obstetrical delivery data, a two-component Poisson mixture model provides a reasonable fit to the heterogeneous LOS distribution. Adopting the generalized linear mixed model (GLMM) approach, random effects are introduced to the two-component Poisson mixture regression model to account for the inherent correlation of patients clustered within hospitals. An EM algorithm is developed for the joint estimation of regression coefficients and variance component parameters. Related diagnostic measures for assessing model adequacy are derived. When applying the method to analyse maternity LOS, appropriate risk factors for the short-stay and long-stay subgroups can be identified from the respective Poisson components. In addition, predicted random hospital effects enable the comparison of relative efficiencies among hospitals after adjustment for patient case-mix and health provision characteristics.

Adult↗
PubMed2006

Effect of Neospora caninum-serostatus on culling, reproductive performance and milk production in Dutch dairy herds with and without a history of Neospora caninum-associated abortion epidemics.

We quantified the effect of Neospora caninum (NC)-serostatus on culling and (re)production in 83 herds randomly selected from the Dutch dairy herd population (random group) and in 17 herds that had experienced an abortion epidemic associated with NC infection (epidemic-abortion group). In the random group, a single whole-herd blood sampling was done during the spring of 2003, while in the epidemic-abortion group whole-herd blood sampling was done repeatedly at least once a year starting after the abortion epidemic during the period 1997-2000 until the summer of 2004. Serological test-results for NC were given as 'negative' (N), 'low-positive' (LP) and 'high-positive' (HP). For analysing the time to culling, calving interval and age of first calving, survival analysis was used. For categorical reproduction parameters either a logistic-regression model (abortion, non-return after 1st insemination) or a Poisson-regression model (number of inseminations per pregnancy) was used. For milk production a linear-mixed model was used. All models were controlled, if applicable, for confounding variables like parity, production, season, year and abortion and adjusted for within-herd clustering. In random herds, HP serostatus increased the hazard for culling 1.73-fold (95% CI: 1.37-2.19) compared to N and LP serostatus. Compared to N serostatus, LP and HP serostatus in epidemic-abortion herds increased the odds for abortion 1.88-fold (95% CI: 1.41-2.52) and 1.72-fold (95% CI: 1.38-2.14), respectively. No other reproduction parameters were associated with NC-serostatus in the random or epidemic-abortion herds. We found no effect of serostatus on milk production in the random group. In contrast, milk production of LP and HP serostatus in the epidemic-abortion group was respectively, 0.72kgmilk/day (95% CI: 0.15-1.03) and 0.59kgmilk/day (95% CI: 0.13-1.30), less during the first 100 days of lactation in the first year after the abortion epidemic compared with N serostatus.

Abortion, Veterinary↗
PubMed2005

A longitudinal measurement error model with a semicontinuous covariate.

Covariate measurement error in regression is typically assumed to act in an additive or multiplicative manner on the true covariate value. However, such an assumption does not hold for the measurement error of sleep-disordered breathing (SDB) in the Wisconsin Sleep Cohort Study (WSCS). The true covariate is the severity of SDB, and the observed surrogate is the number of breathing pauses per unit time of sleep, which has a nonnegative semicontinuous distribution with a point mass at zero. We propose a latent variable measurement error model for the error structure in this situation and implement it in a linear mixed model. The estimation procedure is similar to regression calibration but involves a distributional assumption for the latent variable. Modeling and model-fitting strategies are explored and illustrated through an example from the WSCS.

Blood Pressure↗
PubMed2003

Testing correlation of cognitive decline at adjacent stages of dementia.

This paper studies the correlation of cognitive progression for subjects whose dementia has made a transition from a milder stage of severity to the next stage of impairment. We model the progression of cognitive decline at adjacent stages of dementia by using a general linear mixed model. We also propose a three-step procedure to detect the best configuration of the covariance matrices for the random components in the model. After the best configuration of covariance matrices for the random components in the model is determined, we then recommend another two-step process to test whether there exists a significant correlation between the rate of cognitive decline before the transition, the cognitive status at the transition time, and the rate of cognitive decline after the transition. In addition, we present asymptotic confidence interval estimates for the correlations associated with a transition. This method is applied to several composite psychometric factor scores in the longitudinal database from the Alzheimer's Disease Research Center (ADRC) at Washington University in St. Louis.

Aged↗
PubMed2001

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma.

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environmental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed.

Carcinoma, Squamous Cell↗
PubMed2001

Tutorial in Biostatistics: Evaluating the impact of 'critical periods' in longitudinal studies of growth using piecewise mixed effects models.

Recent developments in modern multivariate methods provide applied researchers with the means to address many important research questions that arise in studies with repeated measures data collected on individuals over time. One such area of applied research is focused on studying change associated with some event or critical period in human development. This tutorial deals with the use of the general linear mixed model for regression analysis of correlated data with a two-piece linear function of time corresponding to the pre- and post-event trends. The model assumes a continuous outcome is linearly related to a set of explanatory variables, but allows for the trend after the event to be different from the trend before it. This task can be accomplished using a piecewise linear random effects model for longitudinal data where the response depends upon time of the event. A detailed example that examines the influence of menarche on changes in body fat accretion will be presented using data from a prospective study of 162 girls measured annually from approximately age 10 until 4 years post menarche.

Adipose Tissue↗