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B P Carlin

Publications and source records attributed to B P Carlin.

14 recordsLinked to original sources
PubMed2000

Air quality and pediatric emergency room visits for asthma in Atlanta, Georgia, USA.

Pediatric emergency room visits for asthma were studied in relation to air quality indices in a spatio-temporal investigation of approximately 130,000 visits (approximately 6,000 for asthma) to the major emergency care centers in Atlanta, Georgia, during the summers of 1993-1995. Generalized estimating equations, logistic regression, and Bayesian models were fitted to the data. In logistic regression models comparing estimated exposures of asthma cases with those of the nonasthma patients, controlling for temporal and demographic covariates and using residential zip code to link patients to spatially resolved ozone levels, the estimated relative risk per 20 parts per billion (ppb) increase in the maximum 8-hour ozone level was 1.04 (p < 0.05). The estimated relative risk for particulate matter less than or equal to 10 microm in aerodynamic diameter (PM10) was 1.04 per 15 microg/m3 (p < 0.05). Exposure-response trends (p < 0.01) were observed for ozone (>100 ppb vs. <50 ppb: odds ratio = 1.23, p = 0.003) and PM10 (>60 microg/m3 vs. <20 microg/m3: odds ratio = 1.26, p = 0.004). In models with ozone and PM10, both terms became nonsignificant because of collinearity of the variables (r= 0.75). The other analytical approaches yielded consistent findings. This study supports accumulating evidence regarding the relation of air pollution to childhood asthma exacerbation.

Adolescent↗
PubMed1999

Adaptive design improvements in the continual reassessment method for phase I studies.

The continual reassessment method (CRM) enables full and efficient use of all data and prior information available in a phase I study. However, despite a number of recent enhancements to the method, its acceptance in actual clinical practice has been hampered by several practical difficulties. In this paper, we consider several further refinements in the context of phase I oncology trials. In particular, we allow the trial to stop when the width of the posterior 95 per cent probability interval for the maximum tolerated dose (MTD) becomes sufficiently narrow (that is, when the information accumulating from the trial data reaches a prespecified level). We employ a simulation study to evaluate five such stopping rules under three alternative states of prior knowledge regarding the MTD (accurate, too low and too high). Our results suggest our adaptive designs preserve the CRM's estimation ability while offering the possibility of earlier stopping of the trial.

Antineoplastic Agents↗
PubMed1999

Cross-study hierarchical modeling of stratified clinical trial data.

Hierarchical random-effects models can be used to estimate treatment or other covariate effects in single-study analyses coordinated over multiple clinical units and can also be extended to a wide variety of cross-study applications. After reviewing the single-study case, we use data from five trial protocols to look for units that tend to have treatment effects consistently above or below the study-specific grand mean across several studies. As a first step, we summarize the patient-level data as study-specific and unit-specific estimated treatment effects and standard errors using independent Cox regression models. We then compare the results of a hierarchical model using these data summaries as input to those produced by a more fully Bayesian method that uses the actual patient-level survival data. We also compare various different models using a deviance information criterion, a recent extension of the Akaike information criterion designed for hierarchical models. Our procedure appears to be effective at answering the question whether certain clinical units of the Terry Beirn Community Programs for Clinical Research on AIDS are better than others at identifying treatment effects where they exist.

AIDS-Related Opportunistic Infections↗
PubMed1999

Hierarchical proportional hazards regression models for highly stratified data.

In clinical trials conducted over several data collection centers, the most common statistically defensible analytic method, a stratified Cox model analysis, suffers from two important defects. First, identification of units that are outlying with respect to the baseline hazard is awkward since this hazard is implicit (rather than explicit) in the Cox partial likelihood. Second (and more seriously), identification of modest treatment effects is often difficult since the model fails to acknowledge any similarity across the strata. We consider a number of hierarchical modeling approaches that preserve the integrity of the stratified design while offering a middle ground between traditional stratified and unstratified analyses. We investigate both fully parametric (Weibull) and semiparametric models, the latter based not on the Cox model but on an extension of an idea by Gelfand and Mallick (1995, Biometrics 51, 843-852), which models the integrated baseline hazard as a mixture of monotone functions. We illustrate the methods using data from a recent multicenter AIDS clinical trial, comparing their ease of use, interpretation, and degree of robustness with respect to estimates of both the unit-specific baseline hazards and the treatment effect.

AIDS-Related Opportunistic Infections↗
PubMed1998

Spatio-temporal models with errors in covariates: mapping Ohio lung cancer mortality.

In estimating spatial disease patterns, as well as in related assessments of environmental equity, regional morbidity and mortality rate maps are widely used. Hierarchical Bayes methods are increasingly popular tools for creating such maps, since they permit smoothing of the fitted rates toward spatially local mean values, with more unreliable estimates (those arising in low-population regions) receiving more smoothing. In this paper we blend methods for spatial-temporal mapping with those for handling errors in covariates in a single hierarchical model framework. Estimated posterior distributions for the resulting highly-parameterized models are obtained via Markov chain Monte Carlo (MCMC) methods, which also play a key role in our approach to model evaluation and selection. We apply our approach to a data set of county-specific lung cancer rates in the state of Ohio during the period 1968-1988. Our model uses age-adjusted death rates, and incorporates recent information regarding smoking prevalence, population density, and the socio-economic status of the counties. This information is critical to understanding the role played by a certain depleted uranium fuel processing facility on the elevated lung cancer rates in the counties that neighbour it.

Adolescent↗
PubMed1998

Approaches for optimal sequential decision analysis in clinical trials.

Unlike traditional approaches, Bayesian methods enable formal combination of expert opinion and objective information into interim and final analyses of clinical trial data. However, most previous Bayesian approaches have based the stopping decision on the posterior probability content of one or more regions of the parameter space, thus implicitly determining a loss and decision structure. In this paper, we offer a fully Bayesian approach to this problem, specifying not only the likelihood and prior distributions but appropriate loss functions as well. At each data monitoring point, we enumerate the available decisions and investigate the use of backward induction, implemented via Monte Carlo methods, to choose the optimal course of action. We then present a forward sampling algorithm that substantially eases the analytic and computational burdens associated with backward induction, offering the possibility of fully Bayesian optimal sequential monitoring for previously untenable numbers of interim looks. We show that forward sampling can always identify the optimal sequential strategy in the case of a one-parameter exponential family with a conjugate prior and monotone loss functions as well as the best member of a certain class of strategies when backward induction is infeasible. Finally, we illustrate and compare the forward and backward approaches using data from a recent AIDS clinical trial.

AIDS-Related Opportunistic Infections↗
PubMed1996

Robust Bayesian approaches for clinical trial monitoring.

The interim monitoring and final analysis of data arising from a clinical trial require an inferential method capable of convincing a broad group of potential consumers: doctors; patients; politicians; members of the media, and so on. While Bayesian methods offer a powerful and flexible analytic framework in this setting, this need to convince a diverse community necessitates a practical approach for studying and communicating the robustness of conclusions to the prior specification. In this paper we attempt to characterize the class of priors leading to a given decision (such as stopping the trial and rejecting the null hypothesis) conditional on the observed data. We evaluate the practicality and effectiveness of this procedure over a range of smoothness conditions on the prior class. First, we consider a non-parametric class of priors restricted only in that its elements must have certain prespecified quantiles. We then obtain more precise results by further restricting the prior class, first to a non-parametric class whose members are quasi-unimodal, then to a semi-parametric normal mixture class, and finally to the fully parametric normal family. We illustrate all of our comparisons with a dataset from an AIDS clinical trial that compared the effectiveness of the drug pyrimethamine and a placebo in preventing toxoplasmic encephalitis.

AIDS-Related Opportunistic Infections↗
PubMed1996

Response of CD4 lymphocytes and clinical consequences of treatment using ddI or ddC in patients with advanced HIV infection.

The value of CD4 lymphocyte counts as a surrogate marker in persons with advanced human immunodeficiency virus infection during antiretroviral treatment was assessed using longitudinal models and data from the Terry Beirn Community Programs for Clinical Research on AIDS didanosine/zalcitabine trial of 467 HIV-infected patients. Patients with AIDS or two CD4 counts of < or = 300 who fulfilled specific criteria for zidovudine intolerance or failure were randomized to receive either 500 mg didanosine (ddl) daily or 2.25 mg zalcitabine (ddC) per day. Absolute CD4 counts were recorded at study entry and at as many as four visits. Patients were followed for clinical disease progression and survival. At 2 months, the difference in mean CD4 count from baseline was +15.4 cells/mm3 in the ddI group but -1.3 cells/mm3 in the ddC group. Patients assigned to ddI had a greater chance of a CD4 response at 2 months than those on ddC, yet only those in the ddC group with a response showed significant improvement in progression of disease or survival compared with ddC nonresponders, ddI responders, and ddI nonresponders (p = 0.03). We conclude that a CD4 response does not necessarily correlate with improved outcome and is therefore not a useful surrogate marker in these patients.

Antiviral Agents↗
PubMed1993

Adaptation six months after multiple trauma: a pilot study.

This pilot study investigated the functional and psychosocial adaptation of 18 survivors of multiple trauma who were in the home setting 6 months after discharge from a tertiary trauma center. Seventeen subjects reported complete functional independence and one reported the need for assistance with self-care activities as measured by the Modified Barthel Index. All subjects reported problems with psychosocial adaptation as measured by the Psychosocial Adjustment to Illness Scale (PAIS). Subjects with high PAIS scores (worst adaptation) reported problems in all domains of the PAIS, whereas those with low PAIS scores (best adaptation) reported most problems in health care orientation. Mann-Whitney U tests were significant for gender, household composition and employment status when compared with low PAIS scores (p < 0.05). An analysis of variance confirmed employment status was the best predictive factor for low PAIS scores.

Abbreviated Injury Scale↗
PubMedDate not supplied

Comparing hierarchical models for spatio-temporally misaligned data using the deviance information criterion.

Bayes and empirical Bayes methods have proven effective in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining overall geographic trends and patterns. Recent work extends these methods to the analysis of areal data which are spatially misaligned, that is, involving variables (typically counts or rates) which are aggregated over differing sets of regional boundaries. The addition of a temporal aspect complicates matters further, since now the misalignment can arise either within a given time point, or across time points (as when the regional boundaries themselves evolve over time). Hierarchical Bayesian methods (implemented via modern Markov chain Monte Carlo computing methods) enable the fitting of such models, but a formal comparison of their fit is hampered by their large size and often improper prior specifications. In this paper, we accomplish this comparison using the deviance information criterion (DIC), a recently proposed generalization of the Akaike information criterion (AIC) designed for complex hierarchical model settings like ours. We investigate the use of the delta method for obtaining an approximate variance estimate for DIC, in order to attach significance to apparent differences between models. We illustrate our approach using a spatially misaligned data set relating a measure of traffic density to paediatric asthma hospitalizations in San Diego County, California.

Asthma↗
PubMedDate not supplied

Identifiability and convergence issues for Markov chain Monte Carlo fitting of spatial models.

The marked increase in popularity of Bayesian methods in statistical practice over the last decade owes much to the simultaneous development of Markov chain Monte Carlo (MCMC) methods for the evaluation of requisite posterior distributions. However, along with this increase in computing power has come the temptation to fit models larger than the data can readily support, meaning that often the propriety of the posterior distributions for certain parameters depends on the propriety of the associated prior distributions. An important example arises in spatial modelling, wherein separate random effects for capturing unstructured heterogeneity and spatial clustering are of substantive interest, even though only their sum is well identified by the data. Increasing the informative content of the associated prior distributions offers an obvious remedy, but one that hampers parameter interpretability and may also significantly slow the convergence of the MCMC algorithm. In this paper we investigate the relationship among identifiability, Bayesian learning and MCMC convergence rates for a common class of spatial models, in order to provide guidance for prior selection and algorithm tuning. We are able to elucidate the key issues with relatively simple examples, and also illustrate the varying impacts of covariates, outliers and algorithm starting values on the resulting algorithms and posterior distributions.

Algorithms↗
PubMedDate not supplied

Environmental justice and statistical summaries of differences in exposure distributions.

Recent regulatory action requires the assessment of environmental justice (equitable protection from the burdens of environmental hazards across sociodemographic subpopulations) in the siting of hazardous waste sites, and prioritization of environmental remediation efforts. Assessments of environmental justice require linking exposure, demographic, and health data. The geographic nature of the data makes the use of geographic information systems attractive for environmental justice assessments. Typical geographic assessments compare the composition of 'exposed' populations, while typical statistical assessments focus on differences in health outcomes between population subgroups, possibly adjusted for exposure. We outline an alternate approach based on summarized differences between exposure distributions within each population subgroup. We illustrate how such summaries provide a tool for site evaluation (e.g., defining exposure inequities resulting from locating a new potential hazard at any of a number of possible sites). In addition, we describe summaries, based on dose-response relationships, to describe risk differences imposed by the observed exposure differences. Reported toxic emissions from Allegheny County, Pennsylvania illustrate the approach.

Bayes Theorem↗
PubMedDate not supplied

Assessing environmental justice using Bayesian hierarchical models: two case studies.

Sound statistical methodology for assessing environmental justice is clearly needed, but has been slow to develop. In this paper, we investigate the use of hierarchical Bayesian methods for combining disparate sources of environmental data featuring complex correlations over both space and time. After a brief review of the Bayesian approach and its specific application to disease mapping problems, we illustrate two case studies. The first to these investigates the effect of a certain nuclear fuel reprocessing facility in Ohio on the lung cancer rates in the counties that surround it, while the second concerns the relation between air quality (especially in terms of ambient ozone levels) and pediatric emergency room visits due to asthma in the Atlanta metro area. We close by summarizing the method's implications for environmental justice, as well as future methodological and applied work.

Adult↗