bluuebunny/crossref_metadata_2025_split huggingface dataset:
Empirical Bayes methods for estimating hospital‐specific mortality rates
Authors: Thomas Neal , Longford Nicholas T. , Rolph John E. 
Published: April 2007

AbstractWe present alternative methods for estimating hospital‐level mortality rates to those used by the Health Care Finance Administration for Medicare patients. We use an empirical Bayes model to represent the different sources of variation in observed hospital‐specific mortality rates and we use a logistic regression model to adjust for severity differences (in patient mix) across hospitals. In addition to providing a principled derivation of a standard error for the commonly used estimator, our fully model‐based formulation produces much more accurate estimates and resolves the severe problem of multiple comparisons that arises when extreme estimates are used to identify exceptional hospitals. We estimate models for each of four disease conditions using the national Medicare mortality data base which does not contain patient severity descriptors, and mortality data from national samples which do include patient severity descriptors. We find substantial between‐hospital variation in the unadjusted death rates from the national data base. Mortality rates differ substantially with patient severity in our models, but the sample sizes are too small to yield reliable estimates of the between‐hospital variation in adjusted mortality rates.
Extending a Bayesian analysis of the two‐period crossover to allow for baseline measurements
Authors: Grieve Andrew P. 
Published: April 2007

AbstractIn the context of a two‐period crossover study with baseline measurements a graphical method is developed for displaying the dependence of posterior inferences concerning the treatment effect on unavoidable prior beliefs about the ‘correct’ model. The method is a generalization of the approach developed by Grieve for the corresponding case without baselines.
Fitting multivariate polynomial growth curves in two‐period crossover designs
Authors: Grender Julie Myers , Johnson William D. 
Published: April 2007

AbstractWe discuss the statistical analysis of data from two clinical trials using crossover designs. In both studies, response was observed repeatedly over time in each treatment period. The first study involves repeated measurements of a single response variable whereas the second involves bivariate response. Methods are described for fitting polynomial growth curves to achieve data reduction in a two‐stage approach to the analysis of crossover designs. Thus, a multivariate parametric analysis frequently can be conducted even when the sample sizes are somewhat small as is the case in many crossover designs. Hypotheses that are usually of interest in crossover designs can be tested in the second stage of the analysis. Methods for testing the multivariate general linear hypothesis as a basis for statistical inference in such problems are discussed.
Use of surrogate information time for monitoring the effect of treatment on the change in a response variable in clinical trials
Authors: Wu Margaret C. , Lan K. K. Gordon , Connett John E. 
Published: April 2007

AbstractWe discuss the monitoring of clinical trials data and propose two surrogates for total information for use with the spending function approach where there are repeated measurements and we wish to compare the rates of change in a response variable under different treatments. These surrogates are applied to a setting similar to the Lung Health Study. Although the surrogates do not require estimation of the variance parameters, they do require some knowledge of the ratio R of the within‐ to the between‐individual variances. The effects of overestimating and underestimating R are illustrated. In situations of uncertainty we recommend overestimating R, to provide a conservative estimate of information time at each interim analysis.
Surrogate and auxiliary endpoints in clinical trials, with potential applications in cancer and aids research
Authors: Fleming Thomas R. , Prentice Ross L. , Pepe Margaret S. , Glidden David 
Published: April 2007

AbstractSurrogate endpoints have been defined by Prentice as response variables that can substitute for a ‘true’ endpoint for the purpose of comparing specific interventions or treatments in a clinical trial. The applicability of this definition, and of related surrogate endpoint criteria, is discussed, with emphasis on cancer and AIDS research settings. Auxiliary endpoints are defined as response variables, or covariates, that can strengthen true endpoint analyses. Specifically, such response variables provide some additional information on true endpoint occurrence times for study subjects having censored values for such times. Auxiliary variables will very frequently be available, and they may be able to be used without making additional strong assumptions. Approaches to the use of auxiliary variables using ideas based on augmented score and augmented likelihood methods are described.
The interpretation of multiplicative‐model parameters as standardized parameters
Authors: Greenland Sander , Maldonado George 
Published: April 2007

AbstractUnder current conventions, relative‐risk estimates obtained from multiplicative models are interpreted as estimates of a homogeneous, effect. Such interpretations condition on the unverifiable assumption that the relative risk under study is homogeneous, an assumption that is not likely to be correct even if the model fits well. We propose that such estimates are better interpreted as estimates of standardized relative risks, with a bias component that depends on the degree of model misspecification and on the study design. To evaluate our proposal, we present a study of the maximum‐likelihood estimators from Poisson and logistic regression compared to the population‐standardized rate ratio. The results indicate that our proposed interpretation would in practice be more cautious and accurate than the homogeneous‐effect interpretation.
Evaluating risk adjustment by partitioning variation in hospital mortality rates
Authors: Smith David W. 
Published: April 2007

AbstractThe variation in mortality rates among hospitals has often been described informally as having three major components: patient severity, quality of care and random variation. These informal concepts are characterized formally by partitioning sums of squares and finding their expected values. The partition relates to commonly used tests for whether individual hospitals have unusual mortality rates. Application of the partition to the hospital mortality reports by the Health Care Financing Administration shows that their models for patient risk account for about one‐half the variation among hospital mortality rates. An example using clinical measures of severity accounts for about two‐thirds of mortality variation among hospitals.
Heart rate variability and susceptibility for sudden cardiac death: An example of multivariable optimal discriminant analysis
Authors: Yarnold Paul R. , Soltysik Robert C. , Martin Gary J. 
Published: April 2007

AbstractThe statistical classification problem motivates the search for an analytical procedure capable of classifying observations accurately into one of two or more groups on the basis of information with respect to one or more attributes, and constitutes a fundamental challenge for all scientific disciplines. Although there are many classification methodologies, only optimal discriminant analysis (ODA) explicitly guarantees that the discriminant classifier will maximize classification accuracy in the training sample. This paper presents the first example of multivariable ODA (MultiODA) in medicine, for an application in which we employ three attributes (age and two measures of heart rate variability) to predict susceptibility to sudden cardiac death for a sample of 45 patients. MultiODA outperformed logistic regression analysis on every classification performance index (overall accuracy, sensitivity, specificity, and positive and negative predictive values). In fact, the worst performance result achieved by MultiODA (in total sample or leave‐one‐out validity analysis) exceeded the best performance achieved by logistic regression analysis. We conclude that ODA offers promise as a methodology capable of improving the classification performance achieved by medical researchers, and that clearly merits investigation in future research.
A simple test for spatial pattern in regional health data
Authors: Walter S. D. 
Published: April 2007

AbstractThe rank adjacency statistic D has been used to summarize the spatial autocorrelation in regional health data. It is defined as the average absolute difference in ranks of the data, taken over all possible pairs of adjacent regions. In this paper, the mean and approximate variance are derived for D, including provision for general weighting of regional pairs. Special results are given for the case of binary weights representing regional adjacencies. Empirical analysis with cancer maps from three countries shows excellent properties of a normal approximation to test the significance of D, eliminating the previous need for simulation. The calculations are further illustrated with data on the spatial pattern of urban/rural residence.
Assessing time‐by‐covariate interactions in proportional hazards regression models using cubic spline functions
Authors: Hess Kenneth R. 
Published: April 2007

AbstractProportional hazards (or Cox) regression is a popular method for modelling the effects of prognostic factors on survival. Use of cubic spline functions to model time‐by‐covariate interactions in Cox regression allows investigation of the shape of a possible covariate‐time dependence without having to specify a specific functional form. Cubic spline functions allow one to graph such time‐by‐covariate interactions, to test formally for the proportional hazards assumption, and also to test for non‐linearity of the time‐by‐covariate interaction. The functions can be fitted with existing software using relatively few parameters; the regression coefficients are estimated using standard maximum likelihood methods.
Crossover designs for clinical trials
Authors: Carriere K. C. 
Published: April 2007

AbstractI discuss three‐period crossover designs for an efficient comparison of two test treatments with special application to clinical trials which often have many practical limitations. In this paper I specify a subset of three‐period crossover designs so that the investigators are not left with the problematic two‐period two‐sequence design, should the trials be terminated after the second period. I show that there is a dramatic reduction in variability for estimating the direct and residual treatment effects in three‐period designs compared to two‐period designs. I also show that the universally optimal design with ABB and BAA sequences is unsuitable when a complex form of residual effects is suspected, such as the second‐order residual effects or treatment by period interactions. The design with ABB, BAA, AAB, and BBA sequences is relatively robust to these uncertain model assumptions. I also discuss missing data problems and conclude that, even with a large proportion of missing values, the three‐period design is far more efficient than the two‐period design.
Confidence intervals for weighted proportions
Authors: Waller Jennifer L. , Addy Cheryl L. , Jackson Kirby L. , Garrison Carol Z. 
Published: April 2007

AbstractWe investigate methods for the construction of confidence intervals for a proportion in a stratified two‐stage sampling design with few events occurring in a small number of large, unequal size strata. The critical aspect is the incorporation of the weighting scheme into the construction of a single overall confidence interval. With small numbers of events, the binomial based methods may be inadequate since the normal approximation is not valid. Computer simulations compare coverage probability and bias for five methods of obtaining confidence intervals for proportions by combining: (1) binomial variances; (2) confidence intervals based on the F‐distribution approximation to the cumulative binomial; (3) the binomial variance method with exact confidence limits when a zero prevalence occurs in any stratum; (4) confidence intervals based on the F‐distribution using a rescaling factor; and (5) the binomial variance method with exact confidence limits using a rescaling factor. The method that performs best in terms of coverage probability is the combination of stratum specific confidence intervals based on the F‐distribution using a rescaling factor. The methods involving the binomial variance tend to be negatively biased and the methods based on the F‐distribution tend to be positively biased. Application of these methods with data from a study of adolescent depression that employs a stratified two‐stage sampling design is consistent with these results.
Causal estimation of time‐varying treatment effects in observational studies: Application to depressive disorder
Authors: Lavori Philip W. , Dawson Ree , Mueller Timothy B. 
Published: April 2007

AbstractClinicians recognize three phases of the treatment of major depression: an acute phase to control disabling symptoms, a continuation phase to avoid relapses of a single episode, and a preventive phase to avoid recurrences of new episodes over time. With no directly measurable trace of the underlying pathological process, the distinction is based arbitrarily on the passage of time in remission. The clinician who has successfully treated a patient with antidepressant medications in the acute phase has a critical clinical decision to make for the continuation and preventive phases: whether to continue to prescribe the medication, for how long, and at what dose. This decision, like most clinical decisions in psychiatry, is not yet completely determined by the results of randomized clinical trials. Only a handful of such trials have been completed, covering just a fraction of the possible maintenance strategies (defined by treatment drop times). For many reasons, observational studies of the outcome of naturally occurring treatment choices play an important supporting role, helping to extend the reach of completed studies and to design new studies. Causal inference from observational studies has usually been considered in the context of a decision among a few fixed alternatives at a single time. The particular causal effect of interest in the maintenance of remission dictates that treatment be studied over remission time. This challenges the causal analysis of the observational study. We present issues arising from assessing temporal treatment effects due to non‐randomized treatment assignment over time. We use data from a large observational study of the course of affective illness, to illustrate an approach to this problem.
Estimating patterns of CD4 lymphocyte decline using data from a prevalent cohort of HIV infected individuals
Authors: Vittinghoff Eric , Malani Hina Mehta , Jewell Nicholas P. 
Published: April 2007

AbstractIn natural history studies of chronic disease, it is of interest to understand the evolution of key variables that measure aspects of disease progression. This is particularly true for immunological variables among persons infected with the human immunodeficiency virus (HIV). The natural time scale for such studies is time since infection. Most data available for analysis, however, arise from prevalent cohorts, where the date of infection is unknown for most or all individuals. As a result, standard curve fitting algorithms are not immediately applicable. Here we propose two methods to circumvent this difficulty. The first uses repeated measurement data to provide information not only on the level of the variable of interest, but also on its rate of change, and is based on the principal curves algorithm of Hastie and Stuetzle. The second uses an external estimate of the expected time since infection. Both methods use locally‐weighted linear smoothers, and are applied to data from a prevalent cohort of HIV‐infected homosexual men, giving estimates of the average pattern of CD4 lymphocyte decline. These methods apply to natural history studies that use data from prevalent cohorts where the time of disease origin is uncertain, provided availability of certain information from external sources.
A constrained empirical bayes estimator for incidence rates in areas with small populations
Authors: Devine Owen J. , Louis Thomas A. 
Published: April 2007

AbstractMaps that show the geographic distribution of incidence rates can be useful tools for analysing spatial variation in mortality and morbidity. To attain the necessary geographic resolution, however, production of such maps often requires estimation of incidence in areas with small populations where the observed rates may be highly unstable. Manton et al. have presented an empirical Bayes stabilization procedure in which the observed rate is combined with an area‐specific estimate of the underlying incidence. The approach allows for the mapping of outcomes with varied and possibly unknown etiologies without necessitating covariate dependent modelling of the expected rate. The empirical distribution of a collection of these estimates, however, may not provide an adequate description of the dispersion among the true rates. As a result, decisions based on the histogram of the empirical Bayes estimates may be suspect. We propose a modified version of the approach in which the mean and sample variance of the ensemble of estimates are constrained to equal the appropriate moments of the posterior distribution. The resulting collection of constrained empirical Bayes estimators has nearly the stability of the unconstrained approach and provides an improved estimator of the true rate distribution. We illustrate use of the estimator by producing stabilized county‐level maps of U.S. fire‐ and burn‐related mortality rates and validate the analytic results using a simulation analysis.
Corrections for exposure measurement error in logistic regression models with an application to nutritional data
Authors: Kuha Jouni 
Published: April 2007

AbstractTwo correction methods are considered for multiple logistic regression models with some covariates measured with error. Both methods are based on approximating the complicated regression model between the response and the observed covariates with simpler models. The first model is the logistic approximation proposed by Rosner et al., and the second is a second‐order extension of this model. Only the mean and covariance matrix of the true values of the covariates given the observed values have to be specified, but no distributional assumptions about the measurement error are made. The parameters related to the conditional moments are estimated from a separate validation data set. The correction methods considered here are compared to other methods proposed in the literature. They are also applied to a multiple logistic model describing the effect of nutrient intakes on the ratio of serum HDL cholesterol to total cholesterol. The data constitute baseline data from an epidemiological cohort study, in which a separate pilot study has been carried out to obtain validation information. In the example the corrected parameter estimates from the two approximate models are very similar. Both differ considerably from the naive logistic estimates, indicating a large effect of the measurement error. The various assumptions required by the correction methods are also discussed.
Analysis of repeated categorical data using generalized estimating equations
Authors: Lipsitz Stuart R. , Kim Kyungmann , Zhao Lueping 
Published: April 2007

AbstractMoment methods for analysing repeated binary responses have been proposed by Liang and Zeger, and extended by Prentice and Zhao and Prentice. In these estimating equations, models are proposed for the correlation between the repeated binary responses. We extend Liang and Zeger's method to models for the correlation between repeated nominal or ordinal categorical responses; in particular, when the repeated responses are binary, our methods reduce to Liang and Zeger's method. Our method is illustrated with two datasets. One dataset contains repeated observations of self‐assessment of arthritis, an ordered variable with three categories, collected during a randomized comparative study of alternative treatments of patients with rheumatoid arthritis. The second dataset is a longitudinal study of the health effects of air pollution, in which the repeated ordered multinomial response is the wheezing status (no wheeze, wheeze with cold, wheeze apart from cold) of a child at ages 9, 10, 11 and 12 years.
Assays for recombinant proteins: A problem in non‐linear calibration
Authors: Giltinan David M. , Davidian Marie 
Published: April 2007

AbstractQuantification of protein levels in biological matrices such as serum or plasma frequently relies on the techniques of immunoassay or bioassay. The relevant statistical problem is that of non‐linear calibration, where one estimates analyte concentration in an unknown sample from a calibration curve fit to known standard concentrations. This paper discusses a general framework for calibration inference, that of the non‐linear mixed effects model. Within this framework, we consider two issues in depth accurate characterization of intra‐assay variation, and the use of empirical Bayes methods in calibration. We show that proper characterization of intra‐assay variability requires pooling of information across several assay runs. Simulation work indicates that use of empirical Bayes methods may afford considerable gain in efficiency; one must weigh this gain against practical considerations in the implementation of Bayesian techniques. We illustrate the methods discussed using a cell‐based bioassay for the recombinant hormone relaxin.
Survival analysis and neural nets
Authors: Liestbl Knut , Andersen Per Kragh , Andersen Ulrich 
Published: April 2007

AbstractWe consider feed‐forward neural nets and their relation to regression models for survival data. We show how the back‐propagation algorithm may be used to obtain maximum likelihood estimates in certain standard regression models for survival data, as well as in various generalizations of these. Examples concerning malignant melanoma and post‐partum amenorrhoea during lactation are used as illustration. We conclude that although problems with the substantial number of parameters and their interpretation remain, the feed‐forward neural network models are flexible extensions to the standard regression models and thereby candidates for use in prediction and exploratory analyses in larger data sets.
A fourier series approach for comparing groups of subjects on ambulatory blood pressure patterns
Authors: Somes Grant W. , Harshfield G. A. , Arheart K. L. , Miller S. T. 
Published: April 2007

AbstractWe develop an approach to the statistical analysis of 24‐hour ambulatory blood pressure monitoring where we represent each subject's profile by a different mathematical model. We first smooth the data and then use a Fourier series approach to determine the best model for each subject. We then estimate summary variables based on each subject's model to compare distinct groups of subjects. In comparing 15 adult black male hypertensives to eight adult black male normotensives we found that the two groups differ on the shift away from the mesor for both the systolic and the diastolic blood pressure profile.