bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smoothing splines for longitudinal data
Authors: Anderson Stewart J. , Jones Richard H. 
Published: April 2007

AbstractIn a longitudinal data model with fixed and random effects, polynomials are used to model the fixed effects and smoothing polynomial splines are used to model the within‐subject random effect curves. The splines are generated by modelling the data for each subject as observations of an integrated random walk with observational error. The initial conditions for each subject's deviation from the fixed effect curve are assumed to have zero mean and arbitrary covariance matrix which is estimated by maximum likelihood, producing an empirical Bayes estimate. This is in contrast to modelling a single curve using a diffuse prior. An example is presented using unbalanced longitudinal data from a pilot study in breast cancer patients.
A proportional regression model for 20 year survival of colon cancer in Norway
Authors: Zahl Per‐Henrik 
Published: April 2007

AbstractTwenty year survival of all Norwegians with colon cancer registered in a period of 10 years is estimated by both relative survival rates, and with a proportional regression model for the excess intensity. Male colon cancer patients have a significant positive excess mortality at least 20 years after diagnosis, while the excess mortality for females is about zero after 10 years. Stratified analyses for men indicate non‐proportionality throughout the follow‐up period, and when this information is included in the regression model, there are significant effects of age between 60 and 70 years and for pelvic cancer. The use of proportional regression models is also discussed when excess intensities are close to zero or negative.
Experimental quantiles of epidemiological indices in case‐control studies with non‐differential misclassification
Authors: Marinos Aristides Th. , Tzonou Anastasia J. , Karantzas Menelaos E. 
Published: April 2007

AbstractThe formulae for some typical epidemiological indices in case‐control studies with non‐differential misclassification are expressed in terms of two groups (α, β) and (γ, δ) of misclassification probabilities of exposure E and confounder C, respectively, and the initially estimated frequencies. The parameters α and β denote the probability that subjects exposed to E are classified as non‐exposed and the probability that non‐exposed ones will be classified as exposed, respectively. Similarly, δ and γ stand for the probability that those who have been exposed to C will be classified as non‐exposed and the probability that non‐exposed subjects are classified as exposed, respectively. The non‐negativeness of the expressions for the ‘true’ frequencies in terms of the measured ones and the misclassification probabilities leads to the construction of feasibility regions for α, β, γ and δ. For a number of ‘acceptable’ 4‐tuples (α, β, γ, δ), all of which lie inside these feasibility regions, a sequence of feasible values for an epidemiological index is determined, after employing a systematic procedure by means of a ‘searching net’ with increments Δα, Δβ, Δγ, Δδ. The procedure serves to determine the characteristics of the (experimental) cumulative distribution function for any selected epidemiological index. The final stage in exploiting the structure of feasibility regions for α, β, γ and δ is to use the cumulative distribution function to calculate quantiles for the index associated with prescribed probabilities.
Risk scores from logistic regression: Unbiased estimates of relative and attributable risk
Authors: Tomasson Helgi 
Published: April 2007

AbstractIn epidemiology, the risk of disease in terms of a set of covariates is often modelled by logistic regression. The resulting linear predictor can be used to define the extent of risk between extremes, and to calculate an attributable risk for the covariates taken together. As is well known, straightforward use of the linear predictor, on the sample from which it was derived, to obtain estimates the relative and attributable risk will be biased, often seriously. Use of the jack‐knife technique is extended to produce asymptotically unbiased estimates of relative and attributable risks. The asymptotic variances associated with these estimates are derived by using the formulae of conditional variances. They are applied to the results of a case‐control study of stomach cancer.
Calculation of unconditional and conditional reference intervals for foetal size and growth from longitudinal measurements
Authors: Royston Patrick 
Published: April 2007

AbstractMethods for estimating unconditional and conditional reference intervals for foetal size and growth based on longitudinal observations are presented. The methods are based on simple random‐effects regression models and involve transforming both the response and the covariate (timepoint). A dataset from a designed longitudinal study of foetal size is analysed in detail as a motivating example.
Confidence limits for the population prevalence rate based on the negative binomial distribution
Authors: Lui Kung‐Jong 
Published: April 2007

AbstractThis paper shows that the extension of the simple procedure of George and Elston in calculation of confidence limits for the underlying prevalence rate to accommodate any finite number of cases in inverse sampling is straightforward. To appreciate the fact that the length of the confidence interval calculated on the basis of the first single case may be too wide for general utility, I include a quantitative discussion on the effect due to an increase in the number of cases requested in the sample on the expected length of confidence intervals. To facilitate further the application of the results presented in this paper, I present a table that summarizes in a variety of situations the minimum required number of cases for the ratio of the expected length of a confidence interval relative to the underlying prevalence rate to be less than or equal to a given value. I also include a discussion on the relation between Cleman'S confidence limits on the expected number of trials before the failure of a given device and those presented here.
Estimation of a common odds ratio under binary cluster sampling
Authors: Ahn Chul , Odom‐Maryon Tamara 
Published: April 2007

AbstractWe present results from a simulation study for the estimation of a common odds ratio in multiple 2 × 2 tables when the data are correlated within clusters. We model the correlation of the data by the beta‐binomial distribution. Through a simulation study, we compare the Mantel—Haenszel estimator with Rao and Scott'S estimator in terms of their biases, observed variances, relative efficiencies of their variances and 95 per cent coverage proportions. We limit the simulation study to the case where there are the same number of subjects in each cluster and the same number of observations in each row of each stratum. When ρ = 0, we recommend use of the Mantel—Haenszel estimator γMH with an unadjusted variance and Rao and Scott'S estimator γ with a pooled design effect. In general, when ρ >0, we recommend the Mantel—Haenszel estimator γMH with an adjusted variance and Rao and Scott'S estimator γ with a pooled design effect.
Biostatistical concepts and methods in the legal setting
Authors: Gastwirth Joseph L. , Greenhouse Samuel W. 
Published: April 2007

AbstractBiostatistical concepts and methods apply to various problems arising in actual U.S. legal cases. These involve: measures of association, assessing the potential effect of omitted variables and the Peters—Belson approach to regression. In particular, we present the inapplicability of Fisher's exact test in the case where the process determining the marginal sample sizes is not independent of the hypothesis under study by the 2 × 2 table. We adapt Cornfield's procedure to assess whether the omission of another factor could have contributed to a non‐significant finding of the effect of the major factor under investigation. Finally, we demonstrate the relevance and utility of the Peters—Belson regression methodology to equal pay and promotion cases.
Modelling frailty in area mortality
Authors: Congdon Peter 
Published: April 2007

AbstractThis paper investigates the impact on area life tables of the specification of unobserved frailty. Frailty specification may affect both the regression effects of area and individual level covariates, and lead to changes in the value of summary mortality parameters, such as life expectancy. The paper also investigates how frailty affects life tables for specific causes of death, especially lung cancer and heart disease. Implications for choice between different model specifications, both in terms of age dependence and frailty, are discussed. The focus is on registered deaths recorded by age, birthplace and by small area within a borough of Greater London.
On the use of a pilot sample for sample size determination
Authors: Browne Richard H. 
Published: April 2007

AbstractTo compute the sample size needed to achieve the planned power for a t‐test, one needs an estimate of the population standard deviation δ. If one uses the sample standard deviation from a small pilot study as an estimate of δ, it is quite likely that the actual power for the planned study will be less than the planned power. Monte Carlo simulations indicate that using a 100(1 − γ) per cent upper one‐sided confidence limit on δ will provide a sample size sufficient to achieve the planned power in at least 100(1 − γ) per cent of such trials.
Decision and detection limits for linear homoscedastic assays
Authors: Dunne Adrian 
Published: April 2007

AbstractThis paper focuses on the effect of estimating the calibration parameters on the decision and detection limits for linear homoscedastic assays. Assay decision and detection limits are defined. For linear homoscedastic assays expressions for these limits are derived when the calibration parameters are known and unknwon. These expressions, together with others previously reported in the literature, are used to calculate limits for a particular example of a cyclic AMP assay. The various methods of computing decision and detection limits are compared in a simulation study. The results demonstrate the importance of taking into consideration the variation in the estimators of the calibration parameters.
Sample size determination in stratified trials to establish the equivalence of two treatments
Authors: Nam Jun‐Mo 
Published: April 2007

AbstractWhen designing a trial to establish that a new treatment is as effective as a standard one, the conventional test procedure and sample size based on a null hypothesis of no difference between two treatments is inappropriate. Several authors have investigated test statistics and corresponding sample sizes based on the null hypothesis that the standard treatment is more effective than the new by at least some specific value for a single 2 × 2 table. This paper considers a trial that involves several 2 × 2 tables and presents an approximate formula for the sample size required to obtain a given power of a one‐tailed score test for a null hypothesis of a specific common non‐zero difference between two treatments across strata. I show that the sample size for a trial based on an unstratified test is always larger than that based on a stratified test when the design is balanced.
Confidence interval estimates of an index of quality performance based on logistic regression models
Authors: Hosmer David W. , Lemeshow Stanley 
Published: April 2007

AbstractThis paper considers an index of hospital quality performance defined as the ratio of the observed number deaths to the number predicted by a fitted logistic regression model. We study tests and confidence intervals under two different scenarios depending on the availability of an estimate of the covariance matrix of the coefficints from the fitted logistic regression model. We propose parametric as well as bootstrap‐based confidence intervals. We apply the methods to an analysis of the performance of 27 intensive care units.
Prediction and decision making using Bayesian hierarchical models
Authors: Stangl Dalene K. 
Published: April 2007

AbstractThis paper uses Bayesian hierarchical models to analyse multi‐centre clinical trial data where the outcome variable of interest is continuous, but not normally distributed, and where censoring has occurred. The goal of such an analysis is the same as for any subgroup analysis, to provide survival estimates for specific subgroups as well as for the population and to provide estimates of the degree of heterogeneity between subgroups. An analysis of the Collaborative Study of Long‐Term Maintenance Drug Therapy in Recurrent Affective Illness, a multi‐centre clinical trial funded by the National Institute for Mental Health's Pharmacologic Research Branch, serves to illustrate the proposed methodology. A feature of this data set is that one treatment group was withdrawn from medication at the time of randomization. The paper contains comparison of models, one that accounts for the drug washout period through the use of a changepoint model as well as a comparison of results across several choices of prior parameter values. In addition, the paper considers sensitivity to model choice and priors in a decision theory context.
Re‐evaluating design specifications of longitudinal clinical trials without unblinding when the key response is rate of change
Authors: Shih Weichung Joseph , Gould A. Lawrence 
Published: April 2007

AbstractThe design of clinical trials often requires knowledge of quantities such as between‐ and within subject variances about which only imprecise information exists. To provide assurance that the study has the desired power to detect a minimum clinically meaningful difference between treatment groups, Gould, Gould and Shih, and Shih have recommended obtaining relevant information from the trial at an interim stage without unblinding. Wittes and Brittain provided a similar recommendation, but viewed the portion up to the interim stage as an (internal) pilot study and required unblinding. This paper considers the problem of re‐evaluating the design specifications in longitudinal clinical trials when the key response is the rate of change (slope). The proposed method aims to re‐evaluate the sample size and study duration in a way that maintains the trial's blinding, using an EM algorithm. Simulation results show that the effect on type I error rate in negligible, but the potential gain in power can be substantial. The procedure is simple to use in practice, as it does not unblind patients treatment identifications, and, since it does not unveil the relative efficacy of treatments, it fulfils the requirement of a valid ‘administrative’ (interim) analysis.
Designing a cost‐effective clinical trial
Authors: Hornberger John C. , Brown Byron Wm. , Halpern Jerry 
Published: April 2007

AbstractResearchers and administrators must decide which clinical trials are worth doing and how many subjects are needed for a trial. We calculated sample size considering the costs of implementing the results of the trial and the trial costs using (1) Neyman—Pearson methods and (2) a Bayesian cost—benefit method. We illustrate these methods in a clinical trial sponsored by the National Institutes of Health that compares two levels of blood urea nitrogen clearance by haemodialysis for patients with end‐stage renal disease. When applied to evaluations of research proposals, these methods may help researchers to decide whether to begin a study, and, if so, how many subjects to enrol in it. These methods should be especially useful for large studies intended to inform health policy.
Sample size determination for comparing more than two survival distributions
Authors: Ahnn Sang , Anderson Stewart J. 
Published: April 2007

AbstractWe examine the asymptotic properties of the Tarone and Ware and Harrington and Fleming classes of test statistics under alternative hypotheses when there are comparisons between more than two survival distributions in the presence of arbitrary right censoring. When we assume equal censoring distributions across treatment groups and proportional hazards, we derive the sample size formula for testing the equality ofk⩾ 2 survival distributions using the logrank test. This work extends Schoenfeld's derivation for comparing two survival distributions and also generalizes the results of Makuch and Simon. We also derive the sample size formula for testing monotone dose‐response using Tarone's trend test. We then investigate the practicality of the formula in various situations by presenting empirical power with use of Monte Carlo simulations. In addition, with stratification present, we derive the sample size formula for the stratified logrank test, which is an extension of Palta and Amini.
A class of tests for detecting ‘general’ and ‘focused’ clustering of rare diseases
Authors: Tango Toshiro 
Published: April 2007

AbstractThis paper proposes a class of tests applicable to the detection of two types of disease clustering: ‘focused’ and ‘general’ clustering. The former assesses the clustering of observed cases around the fixed point and the latter does not have any prior information on the centre of clustering. The proposed test for ‘general’ clustering is a generalization of the index for temporal clustering proposed by Tango in that it adjusts for differences in population densities and also in population distributions among categories of the counfounders such as age and sex. Simulation study shows that the proposed ‘general’ test outperformed the average distance method of Whittemore et al. in most of the cluster models considered.
Exploratory tools for disease mapping in geographical epidemiology
Authors: Cislaghi Cesare , Biggeri Annibale , Braga Mario , Lagazio Corrado , Marchi Marco 
Published: April 2007

AbstractIn the present paper a new shrinkage estimator of relative risk, useful in disease mapping, is compared with the empirical and full Bayes estimators, using death certificate data for lung cancer (males and females) and breast cancer (females) 1982–1988 in the Emilia—Romagna region (Italy). The estimates are obtained averaging the relative risks obtained from recursive partitioning of the set of observed values. The number of partitions is fixed in advance and behaves as a smoothing parameter, the lower the number the higher the degree of shrinkage toward the overall mean of the observed values.
Tests for directional space—time interaction in epidemiological data
Authors: Lawson Andrew B. , Viel Jean Francois 
Published: April 2007

AbstractSpatial incidence of disease is often recorded with time of occurrence as an ordering label. This ordering can be used to provide distance based tests for joint clustering of cases in space and time. Two different tests are proposed: one where a control disease is available, and the other where only standardized rates within census regions are available.
Traffic accident mapping in Bangkok metropolis: A case study
Authors: Na Ayuthya Rampai Suksawasdi , Böhning Dankmar 
Published: April 2007

AbstractResults from an analysis of traffic accidents from a study of the police records of four police stations in the Bangkok metropolis are presented. The main emphasis in this study was put on the development of a measure for traffic accident density. The traffic flow was estimated at the various study locations by trained experts. By relating the frequency of traffic accidents found in the police records to the independently estimated traffic flow, a measure for traffic accident occurrence, the traffic accident density, was constructed and estimated for the 47 areas in four study locations. The spatial risk structure was estimated and three risk groups were identified. Each areas was classified into its associated risk group, and traffic accident maps for the four study locations in the Bangkok metropolis are provided here. The implication for prevention strategies is discussed.
Incidence of leukaemia in young people around the La hague nuclear waste reprocessing plant: A sensitivity analysis
Authors: Viel Jean‐François , Pobel Dominique , Carré André 
Published: April 2007

AbstractThe objectives are to investigate the incidence of childhood leukaemia in young people around the French nuclear waste reprocessing plant (La Hague, Normandy) for the period 1978–1992, and to check the sensitivity of the results to the choice of disease clustering tests and reference rates. The performance of three statistical procedures are compared: a conventional approach; a Poisson maximum test, and an extraction mapping technique. On the whole, the results are in general agreement and demonstrate the apparent existence of a distinct cluster of childhood leukaemia, contiguous and south‐east of the La Hague nuclear facility.
MCMC methods for putative pollution source problems in environmental epidemiology
Authors: Lawson Andrew B. 
Published: April 2007

AbstractThis paper demonstrates the use of the Gibbs Sampler and other Markov Chain Monte Carlo (MCMC) methods in two applications in environmental epidemiology. The first example concerns the application of a Metropolis—Hastings/Gibbs sampler to a Cox process with a direction‐dependent cluster variance parameter. The second example consists of the estimation of the posterior (spatial) distribution of a putative location.
Multiple comparisons procedures for comparing several treatments with a control based on binary data
Authors: Chuang‐Stein Christy , Tong Donald M. 
Published: April 2007

AbstractIn this paper, we examine three approaches for comparing several treatments with a control with use of binary response data. The first approach relies on asymptotic theory applied to the Freeman—Tukey transformation of the observed proportions. The second finds an acceptance region based on the binomial distributions estimated under the joint null hypotheses. The third approach applies Dunnett's procedure to the binary data. We evaluated the actual overall type I error rates of the Freeman—Tukey test and Dunnett's procedure using both simulation and binomial calculations while we assessed those of the binomial approach using simulation. Based on their capability to preserve the desirable overall type I error rate, we provide recommendations regarding the choices among the three approaches for various occasions. In addition, we provide comments on the power of these three procedures.
Comparison of median survival times with adjustment for covariates
Authors: Karrison Theodore 
Published: April 2007

AbstractBrookmeyer and Crowley derived a non‐parametric confidence interval for the median survival time of a homogeneous population by inverting a generalization of the sign test for censored data. The 1 – α confidence interval for the median is essentially the set of all valuestsuch that the Kaplan—Meier estimate of the survival curve at timetdoes not differ significantly from one‐half at the two‐sided α level. Su and Wei extended this approach to the two‐sample problem and derived a confidence interval for the difference in median survival times based on the Kaplan‐Meier estimates of the individual survival curves and a ‘minimum dispersion’ test statistic. Here, I incorporate covariates into the analysis by assuming a proportional hazards model for the covariate effects, while leaving the two underlying survival curves virtually unconstrained. I generate a simultaneous confidence region for the two median survival times, adjusted to any selected value,z, of the covariate vector using a test‐based approach analogous to Brookmeyer and Crowley's for the one‐sample case. This region is, in turn, used to derive a confidence interval for the difference in median survival times between the two treatment groups at the selected value ofz. Employment of a procedure suggested by Aitchison sets the level of the simultaneous region to a value that should yield, at least approximately, the desired confidence coefficient for the difference in medians. Simulation studies indicate that the method provides reasonably accurate coverage probabilities.