Biomedical subjects

Bradley P Carlin

Publications and source records attributed to Bradley P Carlin.

10 recordsLinked to original sources

PubMed2005

Insurance status and access to urgent ambulatory care follow-up appointments.

CONTEXT: There is growing pressure to avoid hospitalizing emergency department patients who can be treated safely as outpatients, but this strategy depends on timely access to follow-up care. OBJECTIVE: To determine the association between reported insurance status and access to follow-up appointments for serious conditions that are commonly identified during an emergency department visit. DESIGN, SETTING, AND PARTICIPANTS: Eight research assistants called 499 randomly selected ambulatory clinics in 9 US cities (May 2002-February 2003) and identified themselves as new patients who had been seen in an emergency department and needed an urgent follow-up appointment (within 1 week) for 1 of 3 clinical vignettes (pneumonia, hypertension, or possible ectopic pregnancy). The same person called each clinic twice using the same clinical vignette but different insurance status. MAIN OUTCOME MEASURE: Proportion of callers who were offered an appointment within a week. RESULTS: Of 499 clinics contacted in the final sample, 430 completed the study protocol. Four hundred six (47.2%) of 860 total callers and 277 (64.4%) of 430 privately insured callers were offered appointments within a week. Callers who claimed to have private insurance were more likely to receive appointments than those who claimed to have Medicaid coverage (63.6% [147/231] vs 34.2% [79/231]; difference, 29.4 percentage points; 95% confidence interval, 21.2-37.6; P<.001). Callers reporting private insurance coverage had higher appointment rates than callers who reported that they were uninsured but offered to pay 20 dollars and arrange payment of the balance (65.3% [130/199] vs 25.1% [50/199]; difference, 40.2; 95% confidence interval, 31.4-49.1; P<.001). There were no differences in appointment rates between callers who claimed to have private insurance coverage and those who reportedly were uninsured but willing to pay cash for the entire visit fee (66.3% [132/199] vs 62.8% [125/199]; difference, 3.5; 95% confidence interval -3.7 to 10.8; P = .31). The median charge was 100 dollars (range, 25 dollars-600 dollars). Seventy-two percent of clinics did not attempt to determine the severity of the caller's condition. CONCLUSIONS: Reported insurance status is associated with access to timely follow-up ambulatory care for potentially serious conditions. Having private insurance and being willing to pay cash may not eliminate the difficulty in obtaining urgent follow-up appointments.

Ambulatory Care Facilities↗

PubMed2005

Does it always help to adjust for misclassification of a binary outcome in logistic regression?

It is well known that in logistic regression, where the outcome is measured with error, a biased estimate of the association between the outcome and a risk factor may result if no proper adjustment is made. Hence, it seems tempting to always adjust for possible misclassification of the outcome. Here we show that it is not always beneficial to do so because, though the adjustment reduces the bias, it also inflates the variance, leading to a possibly larger mean squared error of the estimate. In the context of a data set on agricultural injuries, numerical evidence is provided through simulation studies.

Accidents, Occupational↗

PubMed2005

Multivariate parametric spatiotemporal models for county level breast cancer survival data.

In clustered survival settings where the clusters correspond to geographic regions, biostatisticians are increasingly turning to models with spatially distributed random effects. These models begin with spatially oriented frailty terms, but may also include further region-level terms in the parametrization of the baseline hazards or various covariate effects (as in a spatially-varying coefficients model). In this paper, we propose a multivariate conditionally autoregressive (MCAR) model as a mixing distribution for these random effects, as a way of capturing correlation across both the regions and the elements of the random effect vector for any particular region. We then extend this model to permit analysis of temporal cohort effects, where we use the term "temporal cohort" to mean a group of subjects all of whom were diagnosed with the disease of interest (and thus, entered the study) during the same time period (say, calendar year). We show how our spatiotemporal model may be efficiently fit in a hierarchical Bayesian framework implemented using Markov chain Monte Carlo (MCMC) computational techniques. We illustrate our approach in the context of county-level breast cancer data from 22 annual cohorts of women living in the state of Iowa, as recorded by the Surveillance, Epidemiology, and End Results (SEER) database. Hierarchical model comparison using the Deviance Information Criterion (DIC), as well as maps of the fitted county-level effects, reveal the benefit of our approach.

Adult↗

PubMed2005

Generalized hierarchical multivariate CAR models for areal data.

In the fields of medicine and public health, a common application of areal data models is the study of geographical patterns of disease. When we have several measurements recorded at each spatial location (for example, information on p>/= 2 diseases from the same population groups or regions), we need to consider multivariate areal data models in order to handle the dependence among the multivariate components as well as the spatial dependence between sites. In this article, we propose a flexible new class of generalized multivariate conditionally autoregressive (GMCAR) models for areal data, and show how it enriches the MCAR class. Our approach differs from earlier ones in that it directly specifies the joint distribution for a multivariate Markov random field (MRF) through the specification of simpler conditional and marginal models. This in turn leads to a significant reduction in the computational burden in hierarchical spatial random effect modeling, where posterior summaries are computed using Markov chain Monte Carlo (MCMC). We compare our approach with existing MCAR models in the literature via simulation, using average mean square error (AMSE) and a convenient hierarchical model selection criterion, the deviance information criterion (DIC; Spiegelhalter et al., 2002, Journal of the Royal Statistical Society, Series B64, 583-639). Finally, we offer a real-data application of our proposed GMCAR approach that models lung and esophagus cancer death rates during 1991-1998 in Minnesota counties.

Bayes Theorem↗

PubMed2004

Comparison of Bayesian, classical, and heuristic approaches in identifying acute disease events in lung transplant recipients.

This study compares a typical heuristic algorithm with classical and Bayesian regression models in ascertaining the presence of acute bronchopulmonary disease events in lung transplant recipients. These models attempt to predict whether an epoch will end in an event, based on the preceding two weeks of data. The data consist of 150 two-week epochs of daily to biweekly spirometry and symptom covariates for 30 subjects over 60 subject-years. Seventy-five 'event' epochs end on a day when an acute bronchopulmonary disease event is documented in the medical record; 75 randomly selected 'non-event' epochs end on a day when no event is documented. The data are partitioned by randomly assigning 15 subjects for training and the remaining 15 subjects for testing. For cross-validation, a second random partition is generated from the same data set. The statistical models are trained and tested on both partitions. For the heuristic algorithm, its historical event classifications on the same test cases are used. Classification performance on both partitions of all models is compared using receiver operating characteristic curves, sensitivity and specificity, and a Shannon information score. Data partition did not appreciably affect statistical model performance. All statistical models, unlike the heuristic algorithm, performed significantly different than chance (family significance < 0.05, Pearson independence chi-square, Bonferroni multiple correction), and better than the heuristic algorithm. The best models were Bayesian changepoint models. Through a clinically oriented discussion, a case classified by all of these algorithms is presented, suggesting the clinical usefulness of the Bayesian approach compared with the classical and heuristic approaches.

Acute Disease↗

PubMed2004

Parametric spatial cure rate models for interval-censored time-to-relapse data.

Several recent papers (e.g., Chen, Ibrahim, and Sinha, 1999, Journal of the American Statistical Association 94, 909-919; Ibrahim, Chen, and Sinha, 2001a, Biometrics 57, 383-388) have described statistical methods for use with time-to-event data featuring a surviving fraction (i.e., a proportion of the population that never experiences the event). Such cure rate models and their multivariate generalizations are quite useful in studies of multiple diseases to which an individual may never succumb, or from which an individual may reasonably be expected to recover following treatment (e.g., various types of cancer). In this article we extend these models to allow for spatial correlation (estimable via zip code identifiers for the subjects) as well as interval censoring. Our approach is Bayesian, where posterior summaries are obtained via a hybrid Markov chain Monte Carlo algorithm. We compare across a broad collection of rather high-dimensional hierarchical models using the deviance information criterion, a tool recently developed for just this purpose. We apply our approach to the analysis of a smoking cessation study where the subjects reside in 53 southeastern Minnesota zip codes. In addition to the usual posterior estimates, our approach yields smoothed zip code level maps of model parameters related to the relapse rates over time and the ultimate proportion of quitters (the cure rates).

Algorithms↗

PubMed2003

Late detection of breast and colorectal cancer in Minnesota counties: an application of spatial smoothing and clustering.

Cancers detected at a later disease stage are associated with significantly higher mortality risk. Lack of uniformity in county-level cancer detection rates is thus of substantial interest to state health departments and other public health professionals. In this paper, we perform several spatial analyses of breast and colorectal cancer detection data for Minnesota counties for 1995-1997. We look for outliers and clusters in the late detection rates using a number of techniques: (i). applying various mapping schemes, (ii). smoothing the data using Bayesian methods implemented via Markov chain Monte Carlo, and (iii). applying maximum likelihood techniques to test for the presence of clusters and to identify the most likely clustersOur results suggest a fairly uniform spatial pattern in both sets of detection rates. Spatially smoothed rates did not reveal clusters of counties with significantly higher late detection risk, nor were county-level covariates (such as income, education, and race) particularly helpful in explaining the rates. However, our spatial clustering approach (using the scan statistic) did produce statistically significant clusters of counties which may indicate differences of practical importance for public health.

Bayes Theorem↗

PubMed2003

Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota.

The use of survival models involving a random effect or 'frailty' term is becoming more common. Usually the random effects are assumed to represent different clusters, and clusters are assumed to be independent. In this paper, we consider random effects corresponding to clusters that are spatially arranged, such as clinical sites or geographical regions. That is, we might suspect that random effects corresponding to strata in closer proximity to each other might also be similar in magnitude. Such spatial arrangement of the strata can be modeled in several ways, but we group these ways into two general settings: geostatistical approaches, where we use the exact geographic locations (e.g. latitude and longitude) of the strata, and lattice approaches, where we use only the positions of the strata relative to each other (e.g. which counties neighbor which others). We compare our approaches in the context of a dataset on infant mortality in Minnesota counties between 1992 and 1996. Our main substantive goal here is to explain the pattern of infant mortality using important covariates (sex, race, birth weight, age of mother, etc.) while accounting for possible (spatially correlated) differences in hazard among the counties. We use the GIS ArcView to map resulting fitted hazard rates, to help search for possible lingering spatial correlation. The DIC criterion (Spiegelhalter et al., Journal of the Royal Statistical Society, Series B 2002, to appear) is used to choose among various competing models. We investigate the quality of fit of our chosen model, and compare its results when used to investigate neonatal versus post-neonatal mortality. We also compare use of our time-to-event outcome survival model with the simpler dichotomous outcome logistic model. Finally, we summarize our findings and suggest directions for future research.

Adult↗

PubMed2003

On the precision of the conditionally autoregressive prior in spatial models.

Bayesian analyses of spatial data often use a conditionally autoregressive (CAR) prior, which can be written as the kernel of an improper density that depends on a precision parameter tau that is typically unknown. To include tau in the Bayesian analysis, the kernel must be multiplied by tau(k) for some k. This article rigorously derives k = (n - I)/2 for the L2 norm CAR prior (also called a Gaussian Markov random field model) and k = n - I for the L1 norm CAR prior, where n is the number of regions and I the number of "islands" (disconnected groups of regions) in the spatial map. Since I = 1 for a spatial structure defining a connected graph, this supports Knorr-Held's (2002, in Highly Structured Stochastic Systems, 260-264) suggestion that k = (n - 1)/2 in the L2 norm case, instead of the more common k = n/2. We illustrate the practical significance of our results using a periodontal example.

Bayes Theorem↗

PubMed2002

Using hierarchical spatial models for cancer control planning in Minnesota (United States).

OBJECTIVE: Region-specific maps of cancer incidence, mortality, late detection rates, and screening rates can be very helpful in the planning, targeting, and coordination of cancer control activities. Unfortunately, past efforts in this area have been few, and have not used appropriate statistical models that account for the correlation of rates across both neighboring regions and different cancer types. In this article we develop such models, and apply them to the problem of cancer control in the counties of Minnesota during the period 1993-1997. METHODS: We use hierarchical Bayesian spatial statistical methods, implemented using modern Markov chain Monte Carlo computing techniques and software. RESULTS: Our approach results in spatially smoothed maps emphasizing either cancer prevention or cancer outcome for breast, colorectal, and lung cancer, as well as an overall map which combines results from these three individual cancers. CONCLUSIONS: Our methods enable us to produce a more statistically accurate picture of the geographic distribution of important cancer prevention and outcome variables in Minnesota, and appear useful for making decisions regarding targeting cancer control resources within the state.

Bayes Theorem↗