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At least 811 records · Page 45Linked to original sources
PubMed2006

Urinary biomarker profiling in transitional cell carcinoma.

Urinary biomarkers or profiles that allow noninvasive detection of recurrent transitional cell carcinoma (TCC) of the bladder are urgently needed. We obtained duplicate proteomic (SELDI) profiles from 227 subjects (118 TCC, 77 healthy controls and 32 controls with benign urological conditions) and used linear mixed effects models to identify peaks that are differentially expressed between TCC and controls and within TCC subgroups. A Random Forest classifier was trained on 130 profiles to develop an algorithm to predict the presence of TCC in a randomly selected initial test set (n = 54) and an independent validation set (n = 43) several months later. Twenty two peaks were differentially expressed between all TCC and controls (p < 10(-7)). However potential confounding effects of age, sex and analytical run were identified. In an age-matched sub-set, 23 peaks were differentially expressed between TCC and combined benign and healthy controls at the 0.005 significance level. Using the Random Forest classifier, TCC was predicted with 71.7% sensitivity and 62.5% specificity in the initial set and with 78.3% sensitivity and 65.0% specificity in the validation set after 6 months, compared with controls. Several peaks of importance were also identified in the linear mixed effects model. We conclude that SELDI profiling of urine samples can identify patients with TCC with comparable sensitivities and specificities to current tumor marker tests. This is the first time that reproducibility has been demonstrated on an independent test set analyzed several months later. Identification of the relevant peaks may facilitate multiplex marker assay development for detection of recurrent disease.

Adult↗
PubMed2005

Population pharmacokinetic investigation of disopyramide by mixed effect modelling using routine clinical pharmacokinetic data in Japanese patients.

OBJECTIVE: To estimate the population pharmacokinetic parameters of disopyramide using non-linear mixed effects modelling. METHOD: A total of 148 serum levels from 109 patients (61 males and 48 females) receiving disopyramide were collected. RESULTS: The final pharmacokinetic model was Cl (L/h)=3.75.TBW0.567.AGE-0.374.Conc(-0.719).1.48(DOSE>or=5), Vd (L/kg)=4.13 and k(a) (h-1)=0.363, where Cl is total body clearance, Vd is apparent volume of distribution, k(a) is absorption rate constant, TBW is total bodyweight (kg), AGE is age (years), Conc is the concentration of disopyramide (microg/mL), and DOSE>or=5=1 for patient received 5 mg/kg/day of disopyramide dosage or over and 0 otherwise. CONCLUSION: Application of the findings in this study to patient care may permit selection of an appropriate initial maintenance dosage to achieve target disopyramide concentrations and the desired therapeutic effect.

Absorption↗
PubMed1997

Level A in vivo-in vitro correlation: nonlinear models and statistical methodology.

Some new nonlinear models for the relationship between the fraction of drug dose dissolved (absorbed) in vivo and that dissolved in vitro are described. The models are empirical in nature and are generalizations of the linear model that, at present, is the most commonly used model. The modeling approach is based on considering the time at which a drug molecule goes into solution (in vitro or in vivo) to be a random variable and relating the distribution functions using proportional odds, proportional hazards, and proportional reversed hazards models. The models are further extended by allowing the parameter that relates in vivo and in vitro to be a function of time. A statistical model for the data is developed and used as the basis for a statistical methodology for fitting these models. The methods are shown to be generalized linear mixed effects model (GLMM) methods. The models are fitted to some data sets, and the results demonstrate that these models have potential.

Models, Statistical↗
PubMed2001

An alternative parameterization of the general linear mixture model for longitudinal data with non-ignorable drop-outs.

This paper considers the mixture model methodology for handling non-ignorable drop-outs in longitudinal studies with continuous outcomes. Recently, Hogan and Laird have developed a mixture model for non-ignorable drop-outs which is a standard linear mixed effects model except that the parameters which characterize change over time depend also upon time of drop-out. That is, the mean response is linear in time, other covariates and drop-out time, and their interactions. One of the key attractions of the mixture modelling approach to drop-outs is that it is relatively easy to explore the sensitivity of results to model specification. However, the main drawback of mixture models is that the parameters that are ordinarily of interest are not immediately available, but require marginalization of the distribution of outcome over drop-out times. Furthermore, although a linear model is assumed for the conditional mean of the outcome vector given time of drop out, after marginalization, the unconditional mean of the outcome vector is not, in general, linear in the regression parameters. As a result, it is not possible to parsimoniously describe the effects of covariates on the marginal distribution of the outcome in terms of regression coefficients. The need to explicitly average over the distribution of the drop-out times and the absence of regression coefficients that describe the effects of covariates on the outcome are two unappealing features of the mixture modelling approach. In this paper we describe a particular parameterization of the general linear mixture model that circumvents both of these problems.

Anti-Asthmatic Agents↗
PubMed2006

Seemingly unrelated measurement error models, with application to nutritional epidemiology.

Motivated by an important biomarker study in nutritional epidemiology, we consider the combination of the linear mixed measurement error model and the linear seemingly unrelated regression model, hence Seemingly Unrelated Measurement Error Models. In our context, we have data on protein intake and energy (caloric) intake from both a food frequency questionnaire (FFQ) and a biomarker, and wish to understand the measurement error properties of the FFQ for each nutrient. Our idea is to develop separate marginal mixed measurement error models for each nutrient, and then combine them into a larger multivariate measurement error model: the two measurement error models are seemingly unrelated because they concern different nutrients, but aspects of each model are highly correlated. As in any seemingly unrelated regression context, the hope is to achieve gains in statistical efficiency compared to fitting each model separately. We show that if we employ a "full" model (fully parameterized), the combination of the two measurement error models leads to no gain over considering each model separately. However, there is also a scientifically motivated "reduced" model that sets certain parameters in the "full" model equal to zero, and for which the combination of the two measurement error models leads to considerable gain over considering each model separately, e.g., 40% decrease in standard errors. We use the Akaike information criterion to distinguish between the two possibilities, and show that the resulting estimates achieve major gains in efficiency. We also describe theoretical and serious practical problems with the Bayes information criterion in this context.

Bias↗
PubMed2002

An estimator for the proportional hazards model with multiple longitudinal covariates measured with error.

In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and several time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependent covariate follows a linear mixed effects model and the hazard of failure depends on random effects and time-independent covariates via a proportional hazards relationship. Regression calibration and likelihood or Bayesian methods have been advocated for implementation; however, generalization to more than one time-dependent covariate may become prohibitive. For a single time-dependent covariate, Tsiatis and Davidian (2001) have proposed an approach that is easily implemented and does not require an assumption on the distribution of the random effects. This technique may be generalized to multiple, possibly correlated, time-dependent covariates, as we demonstrate. We illustrate the approach via simulation and by application to data from an HIV clinical trial.

Journal Article↗
PubMed2001

Population pharmacodynamics in patients receiving tinzaparin for the prevention and treatment of deep vein thrombosis.

OBJECTIVES: We conducted population anticoagulant pharmacodynamic analysis for patients administered the low-molecular weight heparin tinzaparin. METHODS: Data from 425 patients (2,631 observations) who participated in 2 Phase III clinical studies were utilized in an analysis based on a pharmacodynamic structural model of anti-Xa activity using non-linear mixed effects modeling techniques. Anti-Xa activity from patients participating in a multicenter, randomized, double-blind clinical trial comparing intravenous once daily subcutaneous tinzaparin (175 IU/kg) with heparin for the treatment of deep vein thrombosis (DVT) was first examined using a 2-compartment model with first-order absorption and endogenous anti-Xa activity. Covariates included renal function, body weight, age, gender, race, obesity, concomitant administration of warfarin, and diabetes. RESULTS: The population estimates and 90% confidence intervals (CI) for oral clearance (CL) and apparent volume of distribution of the plasma compartment (Vc) were 0.0176 l/h/kg (CI = 0.012-0.023) and 0.098 l/kg (CI = 0.088 - 0.109), respectively. The elimination half-life was 3.9 h (CI = 2.5-5.2). These estimates are similar to findings in healthy volunteers. The inter-patient variability in clearance was related to plasma creatinine and percent above ideal body weight. Clearance decreased by 22% for patients with severe renal function impairment (creatinine clearance < 30 ml/min), and by about 25% in obese patients (BMI > 30 kg/m2). CONCLUSIONS: Changes of these magnitudes were not clinically important in pooled clinical trial safety and efficacy analyses. Body weight was not a significant covariate in the model supporting the observations in earlier well-defined trials, where tinzaparin was dosed on a weight basis (lU/kg). Clearance was not influenced by age, race or gender. The same model was applied to data obtained from a prospective, randomized, double-blind clinical trial comparing tinzaparin (4,500 IU) to enoxaparin (40 mg) once daily in patients undergoing total hip replacement. Model parameters were similar to those previously obtained supporting the extension of these results across dose and indication. Population analysis in patients with disease and heterogeneity indicated similar pharmacodynamics as in volunteers, supporting weight-based dosing and identified the dependence of clearance on obesity and severe renal function, although the magnitude of these effects are probably not clinically significant.

Adult↗
PubMed2006

A multidimensional integrative medicine intervention to improve cardiovascular risk.

BACKGROUND: Integrative medicine is an individualized, patient-centered approach to health, combining a whole-person model with evidence-based medicine. Interventions based in integrative medicine theory have not been tested as cardiovascular risk-reduction strategies. Our objective was to determine whether personalized health planning (PHP), an intervention based on the theories and principles underlying integrative medicine, reduces 10-year risk of coronary heart disease (CHD). METHODS: We conducted a randomized, controlled trial among 154 outpatients age 45 or over, with 1 or more known cardiovascular risk factors. Subjects were enrolled from primary care practices near an academic medical center, and the intervention was delivered at a university Center for Integrative Medicine. Following a health risk assessment, each subject in the intervention arm worked with a health coach and a medical provider to construct a personalized health plan. The plan identified specific health behaviors important for each subject to modify; the choice of behaviors was driven both by cardiovascular risk reduction and the interests of each individual subject. The coach then assisted each subject in implementing her/his health plan. Techniques used in implementation included mindfulness meditation, relaxation training, stress management, motivational techniques, and health education and coaching. Subjects randomized to the comparison group received usual care (UC) without access to the intervention. Our primary outcome measure was 10-year risk of CHD, as measured by a standard Framingham risk score, and assessed at baseline, 5, and 10 months. Differences between arms were assessed by linear mixed effects modeling, with time and study arm as independent variables. RESULTS: Baseline 10-year risk of CHD was 11.1% for subjects randomized to UC (n=77), and 9.3% for subjects randomized to PHP (n=77). Over 10 months of the intervention, CHD risk decreased to 9.8% for UC subjects and 7.8% for intervention subjects. Based on a linear mixed-effects model, there was a statistically significant difference in the rate of risk improvement between the 2 arms (P=.04). In secondary analyses, subjects in the PHP arm were found to have increased days of exercise per week compared with UC (3.7 vs 2.4, P=.002), and subjects who were overweight on entry into the study had greater weight loss in the PHP arm compared with UC (P=.06). CONCLUSIONS: A multidimensional intervention based on integrative medicine principles reduced risk of CHD, possibly by increasing exercise and improving weight loss.

Aged↗
PubMed2006

Shared parameter models for the joint analysis of longitudinal data and event times.

Longitudinal studies often gather joint information on time to some event (survival analysis, time to dropout) and serial outcome measures (repeated measures, growth curves). Depending on the purpose of the study, one may wish to estimate and compare serial trends over time while accounting for possibly non-ignorable dropout or one may wish to investigate any associations that may exist between the event time of interest and various longitudinal trends. In this paper, we consider a class of random-effects models known as shared parameter models that are particularly useful for jointly analysing such data; namely repeated measurements and event time data. Specific attention will be given to the longitudinal setting where the primary goal is to estimate and compare serial trends over time while adjusting for possible informative censoring due to patient dropout. Parametric and semi-parametric survival models for event times together with generalized linear or non-linear mixed-effects models for repeated measurements are proposed for jointly modelling serial outcome measures and event times. Methods of estimation are based on a generalized non-linear mixed-effects model that may be easily implemented using existing software. This approach allows for flexible modelling of both the distribution of event times and of the relationship of the longitudinal response variable to the event time of interest. The model and methods are illustrated using data from a multi-centre study of the effects of diet and blood pressure control on progression of renal disease, the modification of diet in renal disease study.

Blood Pressure↗
PubMed2026

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs.

MOTIVATION: Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. RESULTS: We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635&#xa0;969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Genome-Wide Association Study↗
PubMed2004

Mixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data.

In this article, I review the use of nonparametric methods in the analysis of longitudinal and growth curve data, particularly the multivariate adaptive splines models for the analysis of longitudinal data (MASAL). These methods combine nonparametric techniques (B-splines, kernel smoothing, piecewise polynomials) and models with random effects, and provide fruitful alternatives to mixed effects linear models. Similarities, differences, strengths and limitations among these methods are presented. The analysis of a real example is also presented to illustrate the application and interpretation of MASAL. Open questions are posed for further investigation.

Biomedical Research↗
PubMed1999

Problematic formulations of SAS PROC.MIXED models for repeated measurements.

The work reported in this article was undertaken to evaluate the utility of SAS PROC.MIXED for testing hypotheses concerning GROUP and TIME x GROUP effects in repeated measurements designs with drop-outs. If dropouts are not completely at random, covariate control over informative individual differences on which dropout data patterns depend is widely recognized to be important. However, the inclusion of baseline scores and time-in-study as between-subject covariates in an otherwise well formulated SAS PROC.MIXED model resulted in inadequate control over type I error in simulated data with or without drop-outs present. The inadequate model formulations and resulting deviant test sizes are presented here as a warning for others who might be guided by the same information sources to employ similar model specifications when analyzing data from actual clinical trials. It is important that the complete model specification be provided in detail when reporting applications of the general linear mixed-model procedure. A single random-coefficients model produced appropriate test sizes, but it provided inferior power when informative covariates were added in the attempt to adjust for dropouts. As an alternative, the incorporation of covariate controls in simpler two-stage endpoint or random regression analyses is documented to be effective in dealing with dropouts under specifiable conditions.

Biopharmaceutics↗
PubMed2001

Mixed pharmacokinetic population study and diffusion model to describe ciprofloxacin lung concentrations.

The pharmacokinetics of ciprofloxacin in plasma and lung tissue at steady-state (500 mg b.i.d.) were studied in 38 patients subjected to lung surgery for bronchial epithelioma. The mean characteristics of the patient population were: age = 60 years (range: 48-70), weight=70kg (47-95), height=165cm (range 160-170) and serum creatinine=85microM (range 62-168). Plasma samples, two for each patient and lung samples, one for each patient, were obtained and analyzed. Seven groups were made according to the time of sampling after ingestion of the 5th dose. A three-compartment model was used to describe ciprofloxacin kinetics in plasma and lung. The non-linear mixed effect model approach was used to estimate the mean and variance of the pharmacokinetic parameters. The mean +/- SD of the estimates (coefficient of variation of interindividual variability as a percentage) were central volume of distribution, 39.45+/-52.47l(133%); steady-state volume of distribution, 145.86+/-97.51l(60%), clearance of influx into lung tissue, 35.83+/-22.57l/h(63%), extrapolated elimination rate constant, 0.173+/-0.25/h and extrapolated elimination half-life, 4.02+/-0.89h. The mean +/- SD ciprofloxacin concentration versus time curve in plasma and lung at steady state was simulated using pharmacokinetic parameters and lung physiological parameters, another approach was studied to model the transport of ciprofloxacin into the lung tissue by diffusion. Ciprofloxacin concentration-time history was obtained both by experiments or simulations. The ciprofloxacin level in the lung tissue followed the ciprofloxacin plasma level with a lag time resulting from the time necessary for ciprofloxacin to diffuse through the lung.

Aged↗
PubMed2005

[Statistical analysis of community-based studies -- presentation and comparison of possible solutions with reference to statistical meta-analytic methods].

PURPOSE: The statistical analysis of community-based trials and of other cluster-randomised trials, requires specific statistical methods. We show the consequences of the application of these models for study results, using data of the German Cardiovascular Prevention Study (GCP) as an example. METHODS: Data of 30,285 subjects were analysed, which were collected at the beginning and at the end of the study period. These data had been collected in 7 intervention regions and by national surveys. We grouped data of the national surveys in 7 control clusters to mimick a design typical for cluster-randomised trials. We applied the following statistical models to estimate the effect of the intervention on total cholesterol as well as on systolic blood pressure and the respective confidence intervals: a linear model, a mixed model, and fixed and random effects meta-analyses. RESULTS: While the estimates and confidence intervals for the intervention effect were similar in mixed model analysis and random effects meta-analysis, results from models incorporating fixed effects only were anti-conservative. The underestimation of variance in models incorporating fixed effects only was especially large in the analysis of systolic blood pressure data, where great heterogeneity between intervention communities was observed. Despite seemingly low intraclass correlation coefficients of 0.0019 for total cholesterol and 0.0166 for systolic blood pressure, respectively, the variance of the intervention effect was increased in the mixed model 2.8fold or 17.1fold, respectively, in comparison to the variance estimated in the linear model. Due to this variance inflation the intervention effect on systolic blood pressure lost statistical significance. CONCLUSION: Our results emphasise the importance to account for correlations in community-based trials. Besides the mixed model random effects meta-analysis can be applied as an alternative method.

Adult↗
PubMed1998

Modeling development of sleep-wake behaviors. II. Results of two cohorts of preterms.

A mixed general linear model analysis of the development of sleep-wake states was conducted on 37 high-risk preterm infants and replicated with a second cohort of 34 infants. Most dependent variables showed significant development over the preterm period: active sleep decreased, and active waking, quiet waking, and the organization of active sleep and quiet sleep increased over the preterm period in both cohorts. The amount of quiet sleep also increased over age, but this change was significant only for Cohort 1. Seven infant characteristics used as covariates had only minor effects. There were no significant differences in the developmental trajectories (slopes) of the two cohorts. The amounts of four variables differed between cohorts: Cohort 2 infants had less sleep-wake transition, more active sleep, less active sleep without REM, and more regular quiet sleep. These findings suggest that developmental patterns of sleep wake states are stable enough in the preterm period that deviant individual patterns might be used to identify infants with neurological problems.

Caregivers↗
PubMed2006

Population pharmacokinetics of ibandronate in Caucasian and Japanese healthy males and postmenopausal females.

INTRODUCTION: Ibandronate is a potent, nitrogen-containing bisphosphonate that is licensed as a once-monthly oral preparation and is currently in clinical development as a novel intermittent intravenous (i.v.) injection in osteoporosis. Ibandronate pharmacokinetic (PK) data were used to develop a PK model that could ultimately be incorporated into a PK pharmacodynamic (PD) model to assist the ibandronate development program through computer-assisted trial design. This manuscript reports the use of non-linear mixed-effects modeling to characterize the PK of ibandronate, to examine the possible influence of ethnicity on the disposition of ibandronate and to develop an appropriate population PK model for ibandronate. METHODS: A retrospective, cross-study population PK analysis was performed using PK data from five phase I studies with i.v. ibandronate (0.125 - 2.0 mg) conducted in Caucasian and Japanese healthy male volunteers, postmenopausal Caucasian women without osteopenia and postmenopausal Japanese women with osteopenia. The following covariates were investigated to establish their influence on the central volume of distribution (V1) and drug clearance (CL): age, body weight, gender, disease status (healthy versus osteopenic), creatinine clearance (CLCR), and ethnicity (Japanese versus Caucasian). Serum concentrations of ibandronate were quantified by GC-MS or ELISA, and data were modeled using non-linear mixed-effects modeling implemented by the software program NONMEM. RESULTS: The PK of ibandronate was adequately described by a linear 3-compartment model. Disease status, body weight, gender and CLCR significantly influenced ibandronate CL (10 34%) and the latter 3 also influenced V1 (20 29%). Ethnicity was not a determinant for ibandronate PK in the final model. Although gender was the most influential covariate, differences in V1 and CL between the sexes were modest (29 and 34%, respectively) and the overall effects on ibandronate exposure (Cmax and AUC) were not clinically relevant. The final model described the observed PK of ibandronate well, and all PK parameters were estimated with an acceptable degree of precision (SE < 13%). CONCLUSION: The PK of i.v. ibandronate was well described by a linear 3-compartment population PK model that included disease status, body weight, gender and CLCR as covariates, but without greatly affecting ibandronate exposure (Cmax and AUC). Ethnicity did not influence ibandronate PK and was not included in the final model.

Adult↗
PubMed2002

Relative therapeutic index between inhaled formoterol and salbutamol in asthma patients.

A double-blind, randomized crossover study in 28 asthmatic patients assessed the relative therapeutic index for inhaled formoterol and salbutamol. Pre-drug administration FEV1 (mean 2.08 l) was 49-93% of predicted and reversibility 16-82% after inhalation of salbutamol. Patients inhaled single doses of formoterol (Oxis) (4.5,18 and 54 microg, delivered doses) via Turbuhaler, salbutamol (Ventolin) (200 and 1800 microg) via pressurized metered dose inhaler (pMDI) and placebo at intervals of 48 h or more. Individual maximum FEV1 and minimum S-K+ were calculated. Relative local (maximum FEV1) and systemic (minimum S-K+) dose potencies, and their ratio, the relative therapeutic index, were estimated using a non-linear mixed effect model. The drug effects were well tolerated and dose dependent. A log-linear approximation was used to describe the bronchodilatory effect, whereas a sigmoid approximation was more apt to describe the decrease in serum potassium concentration. A bivariate dose-response model based on these principles was fitted simultaneously to all data. The mean relative therapeutic index between formoterol 4.5-54 microg given via Turbuhaler and salbutamol 200-1800 microg given via pMDI was estimated to be 2.5 in favour of formoterol; this trend was not statistically significant.

Adult↗