摘要:

The alignment of multiple homologous biopolymer sequences is crucial in research on protein modeling and engineering, molecular evolution, and prediction in terms of both gene function and gene product structure. In this article we provide a coherent view of the two recent models used for multiple sequence alignment—the hidden Markov model (HMM) and the block-based motif model—to develop a set of new algorithms that have both the sensitivity of the block-based model and the flexibility of the HMM. In particular, we decompose the standard HMM into two components: the insertion component, which is captured by the so-called “propagation model,” and the deletion component, which is described by a deletion vector. Such a decomposition serves as a basis for rational compromise between biological specificity and model flexibility. Furthermore, we introduce a Bayesian model selection criterion that—in combination with the propagation model, genetic algorithm, and other computational aspects—forms the core of PROBE, a multiple alignment and database search methodology. The application of our method to a GTPase family of protein sequences yields an alignment that is confirmed by comparison with known tertiary structures.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473814

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Migraine headache is a common condition in the United States for which a wide range of drug and nondrug treatments are available. There is wide disagreement about which treatments are most effective; meta-analysis of existing clinical trials can help to bring existing evidence to bear on this question. Conducting a meta-analysis is a challenging statistical problem because of the absence of a uniform accepted definition of headache syndromes, the diversity of treatments, and the heterogeneous and incomplete nature of published information. The results of studies are summarized in various ways; most studies report continuous treatment effects for each treatment, but some report only differences in effectiveness for pairs of treatments, and others report only 2 × 2 contingency tables for dichotomized responses. In this article we present a hierarchical Bayesian grouped random-effects model for synthesizing evidence from several clinical trials comparing the effectiveness of commonly recommended prophylactic treatments for migraine headaches. We incorporate explicitly the relationships among the different classes of treatments and introduce latent auxiliary variables to create a common scale for combining information from studies that report results in very different forms. This model permits us to synthesize this heterogeneous information and to make inferences about treatment effects and the relative ranks of treatment without understating uncertainty. Estimation, ranking, model validation, and sensitivity analysis are all implemented through simulation-based methods.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473815

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

In recent years many studies have reported large differences in the use of medical treatments and procedures across geographic regions, hospitals, and individual health care providers. Beyond reporting on the extent of observed variations, these studies examine the role of contributing factors including patient, regional, and provider characteristics. In addition, they may assess the relation between health care processes and outcomes, such as patient mortality, morbidity, and functioning. Studies of variations in health care utilization and outcomes involve the analysis of multilevel clustered data; for example, data on patients clustered by hospital and/or geographic region. The goals of the analysis include the estimation of cluster-specific adjusted responses, covariate effects, and components of variance. The analytic strategy needs to account for correlations induced by clustering and to handle the presence of large variations in cluster size. In this article we formulate a broad class of hierarchical generalized linear models (HGLMs) and discuss their applications to the analysis of health care utilization data. The models can incorporate covariates at each level of the hierarchical data structure, can account for greater variation than what is allowed by the variance in a one-parameter exponential family, and permit the use of heavy-tailed distributions for the random effects. We develop a Bayesian approach to fitting HGLMs using Markov chain Monte Carlo methods and discuss several methods for model checking. The HGLM analysis is presented in the context of two examples of applications to the study of variations in the utilization of medical procedures for elderly Medicare beneficiaries who sustained a heart attack. The first example involves the analysis of clustered longitudinal data with binomial responses and examines geographic and temporal trends in the utilization of coronary angiography across the United States during the 4-year period 1987–1990. The second example involves the analysis of multilevel, clustered data with Poisson responses and examines hospital variations in the utilization of coronary artery bypass graft surgery in 1990. The HGLM analysis incorporates state-level and hospital-level covariates and makes it possible to estimate covariate effects and cluster-specific rates of utilization for both hospitals and states.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473816

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

We propose an item response theory model for ordinal customer satisfaction data where the probability of each response is a function of latent person and question parameters and of cutoffs for the ordinal response categories. This structure was incorporated into a Bayesian hierarchical model by Albert and Chib. We extend this formulation by modeling item nonresponse, coded as “no answer” (NA), as due to either lack of a strong opinion or indifference to the entire question. Because the probability of an NA is related to the latent opinion, the missing-data model is nonignorable. In our hierarchical Bayesian framework, prior means for the person and item effects are related to observed covariates. This structure supports model inferences about satisfaction of individual customers and about associations between customer characteristics and satisfaction levels or propensity to respond. We contrast this with exploratory and standard regression analyses that do not fully support these inferences. Our motivating example, an analysis of a DuPont Corporation 1992 customer satisfaction survey, is described in detail. The nonconjugate likelihood and prior prevent closed-form posterior inference. We present a Markov chain Monte Carlo solution using data augmentation. We diagnose case influence and identify outliers by importance reweighting, and apply posterior predictive model checks. The methods illustrated have application in other situations in which categorical observations can be determined by several latent variables.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473817

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Following United Nations recommendations, many countries collect or publish internal migration data in last-move form, despite continuing uncertainty among researchers about how to estimate transition rates and probabilities from such information. “Last-move” data are a form of retrospective event history in which the only available information for each observational unit are the state at the time of a survey (ω), the last previous state (ψ), and the time at which the ψ → ω transition occurred. The statistical literature has addressed special cases, but there is still no general method for estimating transition hazards from last-move data. In this article I propose such a method, analyze its performance in a Monte Carlo simulation study, and apply it to migration data from Brazil's 1980 census.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473818

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

This article uses a unique set of pooled cross-sectional and time series data to examine the annual rate of U.S. immigration during 1972–1991 from 60 source countries. One distinguishing feature of the article is that it breaks out and cross-classifies various classes of immigrants—numerically limited versus numerically exempt and new immigrant versus adjustment of status. A second distinguishing feature is that it utilizes a unique vector of variables relating to the presence and characteristics of various social programs in source countries. The models developed here emphasize the importance of both differential economic advantage and the ease with which a prospective migrant can transfer skills to the U.S. labor market. Hausman–Taylor instrumental variable estimates of the coefficients indicate that in addition to other factors, social programs in source countries are significant determinants of immigration to the U.S.A.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473819

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Household surveys often elicit respondents' intentions or predictions of future outcomes. The survey questions may ask respondents to choose among a selection of (ordered) response categories. If panel data or repeated cross-sections are available, then predictions may be compared with realized outcomes. The categorical nature of the predictions data complicates this comparison, however. Generalizing previous findings on binary intentions data, we derive bounds on features of the empirical distribution of realized outcomes under the “best-case” hypothesis that respondents form rational expectations and that reported expectations are best predictions of future outcomes. These bounds are shown to depend on the assumed model of how respondents form their “best prediction” when forced to choose among (ordered) categories. An application to data on income change expectations and realizations illustrates how alternative response models may be used to test the best-case hypothesis.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473820

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

The spatial cumulative distribution function (SCDF) is a random function that provides a statistical summary of a random field over a spatial domain of interest. In this article we develop a spatial subsampling method for predicting an SCDF based on observations made on a hexagonal grid, similar to the one used in the Environmental Monitoring and Assessment Program of the U.S. Environmental Protection Agency. We show that under quite general conditions, the proposed subsampling method provides accurate data-based approximations to the sampling distributions of various functionals of the SCDF predictor. In particular, it produces estimators of different population characteristics, such as the quantiles and weighted mean integrated squared errors of the empirical predictor. As an illustration, we apply the subsampling method to construct large-sample prediction bands for the SCDF of an ecological index for foliage condition of red maple trees in the state of Maine.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473821

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473822

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10473823

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor