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PubMed2000

Hierarchical models in generalized synthesis of evidence: an example based on studies of breast cancer screening.

Evidence regarding the potential benefits of a particular health care intervention is often available from a variety of disparate sources. However, formal synthesis of such evidence has traditionally concentrated almost exclusively on that derived from randomized studies, although for a range of conditions the randomized evidence will be less than adequate due to economic, organizational or ethical considerations. In such situations a formal synthesis of the evidence that is available from observational studies can be valuable whilst awaiting higher quality evidence from randomized trials. Consideration of randomized studies alone may be appropriate when assessing the efficacy of an intervention, but assessment of the effectiveness of such an intervention within a more general target population may be improved by consideration of evidence from non-randomized studies as well. Standard meta-analysis methods may allow for both within- and between-study heterogeneity; however when multiple sources of evidence are considered an extra level of complexity is introduced, namely study type. One possible solution to the problem of making inferences, particularly regarding an overall population effect, in such situations is to model the heterogeneity, both quantitative and qualitative, using a Bayesian hierarchical model. The hierarchical nature of such models specifically allows for the quantitative within and between sources of heterogeneity, whilst the Bayesian approach can accommodate a priori beliefs regarding qualitative differences between the various sources of evidence. The use of such methods in practice is illustrated in the context of screening for breast cancer; in this example evidence is available from both randomized clinical trials and observational studies. A particular appeal of a Bayesian approach for this type of problem lies in the prediction of future benefits likely to be observed in a target population. This approach to health service monitoring in general is discussed.

Aged↗

PubMed1998

Approximate hierarchical modelling of discrete data in epidemiology.

Hierarchical models are used in epidemiology to estimate and analyse multiple, related relative risks. Examples include meta-analyses of series of 2 x 2 tables and mapping of spatially correlated disease rates. Empirical transform and penalized quasilikelihood procedures, both of which may be implemented using standard programs for mixed model analysis, provide satisfactory approximate inferences for these problems when cell frequencies are large. Simulation studies show that, in certain situations involving small cell frequencies, penalized quasilikelihood provides satisfactory estimates of variance components and regression coefficients whereas the empirical transform approach does not.

BCG Vaccine↗

PubMed2002

Profiling nursing homes using Bayesian hierarchical modeling.

OBJECTIVES: New methods developed to improve the statistical basis of provider profiling may be particularly applicable to nursing homes. We examine the use of Bayesian hierarchical modeling in profiling nursing homes on their rate of pressure ulcer development. DESIGN: Observational study using Minimum Data Set data from 1997 and 1998. SETTING: A for-profit nursing home chain. PARTICIPANTS: Residents of 108 nursing homes who were without a pressure ulcer on an index assessment. MEASUREMENTS: Nursing homes were compared on their performance on risk-adjusted rates of pressure ulcer development calculated using standard statistical techniques and Bayesian hierarchical modeling. RESULTS: Bayesian estimates of nursing home performance differed considerably from rates calculated using standard statistical techniques. The range of risk-adjusted rates among nursing homes was 0% to 14.3% using standard methods and 1.0% to 4.8% using Bayesian analysis. Fifteen nursing homes were designated as outliers based on their z scores, and two were outliers using Bayesian modeling. Only one nursing home had greater than a 50% probability of having a true rate of ulcer development exceeding 4%. CONCLUSIONS: Bayesian hierarchical modeling can be successfully applied to the problem of profiling nursing homes. Results obtained from Bayesian modeling are different from those obtained using standard statistical techniques. The continued evaluation and application of this new methodology in nursing homes may ensure that consumers and providers have the most accurate information regarding performance.

Bayes Theorem↗

PubMed2005

An introduction to Bayesian hierarchical models with an application in the theory of signal detection.

Although many nonlinear models of cognition have been proposed in the past 50 years, there has been little consideration of corresponding statistical techniques for their analysis. In analyses with nonlinear models, unmodeled variability from the selection of items or participants may lead to asymptotically biased estimation. This asymptotic bias, in turn, renders inference problematic. We show, for example, that a signal detection analysis of recognition memory data leads to asymptotic underestimation of sensitivity. To eliminate asymptotic bias, we advocate hierarchical models in which participant variability, item variability, and measurement error are modeled simultaneously. By accounting for multiple sources of variability, hierarchical models yield consistent and accurate estimates of participant and item effects in recognition memory. This article is written in tutorial format; we provide an introduction to Bayesian statistics, hierarchical modeling, and Markov chain Monte Carlo computational techniques.

Bayes Theorem↗

PubMed2003

ECoS, a framework for modelling hierarchical spatial systems.

A general framework for modelling hierarchical spatial systems has been developed and implemented as the ECoS3 software package. The structure of this framework is described, and illustrated with representative examples. It allows the set-up and integration of sets of advection-diffusion equations representing multiple constituents interacting in a spatial context. Multiple spaces can be defined, with zero, one or two-dimensions and can be nested, and linked through constituent transfers. Model structure is generally object-oriented and hierarchical, reflecting the natural relations within its real-world analogue. Velocities, dispersions and inter-constituent transfers, together with additional functions, are defined as properties of constituents to which they apply. The resulting modular structure of ECoS models facilitates cut and paste model development, and template model components have been developed for the assembly of a range of estuarine water quality models. Published examples of applications to the geochemical dynamics of estuaries are listed.

Journal Article↗

PubMed2002

Bayesian hierarchical models for multi-level repeated ordinal data using WinBUGS.

Multi-level repeated ordinal data arise if ordinal outcomes are measured repeatedly in subclusters of a cluster or on subunits of an experimental unit. If both the regression coefficients and the correlation parameters are of interest, the Bayesian hierarchical models have proved to be a powerful tool for analysis with computation being performed by Markov Chain Monte Carlo (MCMC) methods. The hierarchical models extend the random effects models by including a (usually flat) prior on the regression coefficients and parameters in the distribution of the random effects. Because the MCMC can be implemented by the widely available BUGS or WinBUGS software packages, the computation burden of MCMC has been alleviated. However, thoughtfulness is essential in order to use this software effectively to analyze such data with complex structures. For example, we may have to reparameterize the model and standardize the covariates to accelerate the convergence of the MCMC, and then carefully monitor the convergence of the Markov chain. This article aims at resolving these issues in the application of the WinBUGS through the analysis of a real multi-level ordinal data. In addition, we extend the hierarchical model to include a wider class of distributions for the random effects. We propose to use the deviance information criterion (DIC) for model selection. We show that the WinBUGS software can readily implement such extensions and the DIC criterion.

Bayes Theorem↗

PubMedDate not supplied

A hierarchical model for PACS.

In this paper, a hierarchical model for Picture Archiving and Communication Systems (HPACS) is presented and implemented at Taichung Veterans General Hospital (TCVGH) in Taiwan. Despite the fact that the HPACS is built on the architecture of the second generation PACS, it offers many improved features and has advantages over the second generation PACS, such as the user security control, fast resource dispatch and efficient resource management. This HPACS can be used as a reference model for a hospital with any scale-size. The real implementation of HPACS is currently undertaken in the Taichung Veterans General Hospital (TCVGH), Taiwan, Republic of China and consists of four phases with the first two phases already completed. It is the first pilot system ever to be implemented successfully in a large-scale hospital in Taiwan. The experiences have illustrated the great promise of the HPACS in the future.

Computer Communication Networks↗

PubMed2003

Code-switching across brainstorming sessions: implications for the revised hierarchical model of bilingual language processing.

The revised hierarchical model (RHM) of bilingual language processing posits independent word form representations for the dominant language (L1) and the nondominant language (L2), facilitated translation from L2 words to L1 words, access to common concepts for L1 and L2, and stronger activation of concepts for L1 than for L2. Spanish-English and English-Spanish bilinguals brainstormed for two sessions; half switched languages (L1-L2 or L2-L1) and half stayed in the same language (L1-L1 or L2-L2) across sessions. In both sessions, L1 brainstorming resulted in more efficient idea productivity than L2 brainstorming, supporting stronger concept activation for L1, consistent with the RHM. Switching languages from L2 to L1 resulted in the most efficient idea productivity in Session 2, suggesting that switching to L1 not only permits strong concept activation, but also the activation of concepts that are relatively different than those activated by L2, inconsistent with the RHM. Switching languages increased the proportion of Session 1 ideas repeated during Session 2, despite instructions not to repeat. This finding suggests that there is activation of concepts as well as word forms in same language brainstorming and that this dual activation aids in following instructions not to repeat, consistent with the RHM. It is suggested that the RHM be re-specified to accommodate the notion that L1 and L2 access relatively different concepts.

Cognition↗

PubMed1999

Use of hierarchical models for meta-analysis: experience in the metabolic ward studies of diet and blood cholesterol.

Overviews that combine single effect estimates from published studies generally use a summary statistic approach where the effect of interest is first estimated within each study and then averaged across studies in an appropriately weighted manner. Combining multiple regression coefficients from publications is more problematic, particularly when there are differences in study design and inconsistent reporting of effect sizes and standard errors. This paper describes the use of a hierarchical model in such circumstances. Its use is illustrated in a meta-analysis of the metabolic ward studies that have investigated the effect of changes in intake of various dietary lipids on blood cholesterol. These studies all reported average blood cholesterol for groups of individuals who were studied on one or more diets. Thirty-one studies had randomized cross-over designs, 12 had matched parallel group designs, 12 had non-randomized Latin square designs and 16 had other uncontrolled designs. The hierarchical model allowed the different types of comparison (within-group between-diet, between matched group) that were made in the various studies to each contribute to the overall estimates in an appropriately weighted manner by distinguishing between-study variation, within-study between-matched-group variation and within-group between-diet variation. The hierarchical models do not require consistent specification of effect sizes and standard errors and hence have particular utility in combining results from published studies where the relationships between a dependent variable and two or more predictors have been investigated using heterogeneous methods of analysis.

Cholesterol↗

PubMed2000

A hierarchical model of domains of disablement in the elderly: a longitudinal approach.

PURPOSE: the aims of this paper are to verify that a hierarchical relationship exists between the concepts of Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL) and mobility and to use this hierarchical model to describe the evolution of disability. METHODS: 3751 elderly community dwellers were followed-up 3 and 5 years after baseline interview. A hierarchic disability scale was computed by summing up the number of domains (ADL, IADL, mobility) in which a subject was dependent. Coefficients of scalability and reproducibility of the scale were computed. The hierarchic scale was used to describe transitions between states at each follow-up. RESULTS: the hierarchical model fitted 99.3% of the subjects at baseline. At each follow-up most transitions were towards contiguous grades of disability in survivors, whatever their age. There was a significant trend towards increasing disability. Death rates were higher in subjects aged 75 and over, whatever their disability level. The patterns of evolution differed according to gender. CONCLUSIONS: the cumulative disability scale can be used to describe the evolution of disability with time in elderly community dwellers.

Activities of Daily Living↗

PubMed2006

Neuroticism as a common feature of the depressive and anxiety disorders: a test of the revised integrative hierarchical model in a national sample.

The revised integrative hierarchical model of depression and anxiety (S. Mineka, D. Watson, & L. A. Clark, 1998) proposes that high levels of neuroticism are shared between the depressive and anxiety disorders. This perspective was evaluated with data from the National Comorbidity Survey (N = 5,847), a population-based community sample. Analyses were based on both a broadband (i.e., diagnostic class) and a narrowband (i.e., specific disorder) approach. Results supported the model insofar as high neuroticism was shared across the depressive and anxiety disorders and was particularly elevated in people with comorbid depression and anxiety. Results are discussed in terms of their implications for understanding the association between personality and the depressive and anxiety disorders in a community sample and for the revised integrative hierarchical perspective.

Adolescent↗

PubMed2005

Hierarchical model of vulnerabilities for anxiety: replication and extension with a clinical sample.

This study served to replicate and extend our previously obtained hierarchical model of the relationships among general anxiety vulnerabilities, specific anxiety vulnerabilities and specific anxiety manifestations including panic symptoms, health anxiety, obsessive-compulsive symptoms and worry. Questionnaires assessing these variables, as well as positive affectivity and depressiveness, were administered to 125 outpatients seeking treatment for panic disorder, social anxiety disorder, obsessive-compulsive disorder, generalized anxiety disorder or major depressive disorder. The results, using a clinical sample, were highly consistent with the hierarchical model obtained in the previous study using a student sample. A more elaborate model, based on published theoretical and empirical evidence, was identified and tested, and similar results were obtained. Negative affectivity had expected direct positive effects on all of the specific anxiety and depression manifestations, with the exception of health anxiety, which showed a negative relationship, and OCD symptoms, which showed no relationship. Positive affectivity was found to be a specific risk factor for depression, while intolerance of uncertainty was found to be a specific risk factor for worry and depression. Finally, anxiety sensitivity appears to be a significant risk factor for panic and health anxiety.

Adult↗

PubMed2002

A hierarchical model of HIV-1 protease drug resistance.

A hierarchical model of HIV-1 drug therapy may be used to evaluate new inhibitors and test new treatment strategies that address the problem of drug resistance. The model includes an atomic representation of drug-protease interaction, evaluation of viral fitness based on cleavage of polyprotein substrates during viral maturation, evolutionary modelling of drug resistance mutations in the face of selection pressures by drug and a mathematical description of viral population dynamics in infected individuals. These techniques have been used for the design of resistance-evading inhibitors by computational coevolution techniques and for the optimisation of existing protease inhibitors for improving their robustness in the face of resistance mutation.

Catalytic Domain↗

PubMed1999

Safer sex strategies for women: the hierarchical model in methadone treatment clinics.

Women clients of a methadone maintenance treatment clinic were targeted for an intervention aimed to reduce unsafe sex. The hierarchical model was the basis of the single intervention session, tested among 63 volunteers. This model requires the educator to discuss and demonstrate a full range of barriers that women might use for protection, ranking these in the order of their known efficacy. The model stresses that no one should go without protection. Two objections, both untested, have been voiced against the model. One is that, because of its complexity, women will have difficulty comprehending the message. The second is that, by demonstrating alternative strategies to the male condom, the educator is offering women a way out from persisting with the male condom, so that instead they will use an easier, but less effective, method of protection. The present research aimed at testing both objections in a high-risk and disadvantaged group of women. By comparing before and after performance on a knowledge test, it was established that, at least among these women, the complex message was well understood. By comparing baseline and follow-up reports of barriers used by sexually active women before and after intervention, a reduction in reports of unsafe sexual encounters was demonstrated. The reduction could be attributed directly to adoption of the female condom. Although some women who had used male condoms previously adopted the female condom, most of those who did so had not used the male condom previously. Since neither theoretical objection to the hierarchical model is sustained in this population, fresh weight is given to emphasizing choice of barriers, especially to women who are at high risk and relatively disempowered. As experience with the female condom grows and its unfamiliarity decreases, it would seem appropriate to encourage women who do not succeed with the male condom to try to use the female condom, over which they have more control.

Acquired Immunodeficiency Syndrome↗

PubMed2003

A Bayesian semiparametric joint hierarchical model for longitudinal and survival data.

This article proposes a new semiparametric Bayesian hierarchical model for the joint modeling of longitudinal and survival data. We relax the distributional assumptions for the longitudinal model using Dirichlet process priors on the parameters defining the longitudinal model. The resulting posterior distribution of the longitudinal parameters is free of parametric constraints, resulting in more robust estimates. This type of approach is becoming increasingly essential in many applications, such as HIV and cancer vaccine trials, where patients' responses are highly diverse and may not be easily modeled with known distributions. An example will be presented from a clinical trial of a cancer vaccine where the survival outcome is time to recurrence of a tumor. Immunologic measures believed to be predictive of tumor recurrence were taken repeatedly during follow-up. We will present an analysis of this data using our new semiparametric Bayesian hierarchical joint modeling methodology to determine the association of these longitudinal immunologic measures with time to tumor recurrence.

Bayes Theorem↗

PubMed2000

Testing relationships from the hierarchical model of intrinsic and extrinsic motivation using flow as a motivational consequence.

The purpose of this study was to test a motivational model based on Vallerand's (1997) Hierarchical Model of Intrinsic and Extrinsic Motivation. This model incorporates situational and contextual motivational variables, and was tested using a time-lagged design. Master's level swimmers (N = 104) completed a questionnaire on two separate occasions. At Time 1, situational social factors (perceptions of success and perceptions of the motivational climate), situational motivational mediators (perceptions of autonomy, competence, and relatedness), situational motivation, and flow were assessed immediately following a swim practice. Contextual measures of these same variables were assessed at Time 2, 1 week later, with the exception of flow. Results of a path analysis supported numerous links in the hypothesized model. Findings are discussed in light of research and theory on motivation and flow.

Adolescent↗

PubMed2005

Residual spatial correlation between geographically referenced observations: a Bayesian hierarchical modeling approach.

BACKGROUND: Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, this omission can bias parameter estimates and yield incorrect standard error estimates. We present a Bayesian hierarchical model (BHM) approach that accounts for spatial correlation, and illustrate its strengths and weaknesses by applying this modeling approach to data on Wuchereria bancrofti infection in Haiti. METHODS: A program to eliminate lymphatic filariasis in Haiti assessed prevalence of W. bancrofti infection in 57 schools across Leogane Commune. We analyzed the spatial pattern in the prevalence data using semi-variograms and correlograms. We then modeled the data using (1) standard logistic regression (GLM); (2) non-Bayesian logistic generalized linear mixed models (GLMMs) with school-specific nonspatial random effects; (3) BHMs with school-specific nonspatial random effects; and (4) BHMs with spatial random effects. RESULTS: An exponential semi-variogram with an effective range of 2.15 km best fit the data. GLMM and nonspatial BHM point estimates were comparable and also were generally similar with the marginal GLM point estimates. In contrast, compared with the nonspatial mixed model results, spatial BHM point estimates were markedly attenuated. DISCUSSION: The clear spatial pattern evident in the Haitian W. bancrofti prevalence data and the observation that point estimates and standard errors differed depending on the modeling approach indicate that it is important to account for residual spatial correlation in analyses of W. bancrofti infection data. Bayesian hierarchical models provide a flexible, readily implementable approach to modeling spatially correlated data. However, our results also illustrate that spatial smoothing must be applied with care.

Animals↗

PubMed2002

Hierarchical modeling of sequential behavioral data: an empirical Bayesian approach.

The authors review the common methods for measuring strength of contingency between 2 behaviors in a behavioral sequence, the binomial z score and the adjusted cell residual, and point out a number of limitations of these approaches. They present a new approach using log odds ratios and empirical Bayes estimation in the context of hierarchical modeling, an approach not constrained by these limitations. A series of hierarchical models is presented to test the stationarity of behavioral sequences, the homogeneity of sequences across a sample of episodes, and whether covariates can account for variation in sequences across the sample. These models are applied to observational data taken from a study of the behavioral interactions of 254 couples to illustrate their use.

Bayes Theorem↗