bluuebunny/crossref_metadata_2025_split huggingface dataset:
Power of testing proportions in small two‐sample studies when sample sizes are equal
Authors: Davis Alan B. 
Published: April 2007

AbstractIn planning experiments having two groups of equal but small size, investigators face the uncertainty of power calculations that rely on asymptotic methods. This paper presents a method for determining power for two‐sided tests. I compare two randomization tests, Fisher's exact test (FET), and the mid‐P (MID), with the uncorrected chi‐square test (CHI). Results show power as a function of relative risk for these methods, and assesses their relative power and type I error rates. MID is shown to have intermediate power between CHI, which is the most powerful, and FET, the least powerful. Situations are shown in which CHI and MID occasionally exceed the nominal level of α.
The application of developments in theoretical biostatistics to epidemiologic research
Authors: Obrams G. Iris , Hjortland Marthana 
Published: April 2007

AbstractReport of a workshop held on 18 July 1991 at the National Institutes of Health in Bethesda, MD. Workshop participants were: William J. Blot, National Cancer Institute, Bethesda, MD; Ronald S. Brookmeyer, Johns Hopkins University, Baltimore, MD; Theodore Colton, Boston University, Boston, MA; Genrose D. Copley, National Cancer Institute, Rockville, MD; Mitchell Gail, National Cancer Institute, Rockville, MD; Nancy L. Geller, National Heart, Lung and Blood Institute, Bethesda, MD; Frank E. Harrell, Jr., Duke University, Durham, NC; Marthana Hjortland, National Cancer Institute, Rockville, MD; Elizabeth Holly, Northern California Cancer Center, Belmont, CA; Michel Ibrahim, University of North Carolina, Chapel Hill, NC; Stephen W. Lagakos, Dana‐Farber Cancer Institute, Boston, MA (written report only); G. Iris Obrams, National Cancer Institute, Rockville, MD; Wasema Rida, National Institute of Allergy and Infectious Diseases, Rockville, MD; Jonathan Samet, New Mexico Tumor Registry, Albuquerque, NM; Robert F. Woolson, University of Iowa, Iowa City, IA; and Mimi C. Yu, University of Southern California, Los Angeles, CA (Chair).
Confidence intervals for a binomial proportion
Authors: Vollset Stein Emil 
Published: April 2007

AbstractThirteen methods for computing binomial confidence intervals are compared based on their coverage properties, widths and errors relative to exact limits. The use of the standard textbook method, x/n ± 1.96√[(x/n) (1 − x/n)/n], or its continuity corrected version, is strongly discouraged. A commonly cited rule of thumb stating that alternatives to exact methods may be used when the estimated proportion p̂ is such that np̂ and n(1 − p̂) both exceed 5 does not ensure adequate accuracy. Score limits are easily calculated from closed from solutions to quadratic equations and can be used at all times. Based on coverage functions, the continuity corrected score method is recommended over exact methods. Its conservative nature should be kept in mind, as should the wider fluctuation of actual coverage that accompanies omission of the continuity correction.
A conditional analysis for two‐treatment multiple‐period crossover designs with binomial or poisson outcomes and subjects who drop out
Authors: McKnight Barbara , Van Den Eeden Stephen K. 
Published: April 2007

AbstractWe propose a conditional analysis for outcome data on numbers of recurrent symptoms arising in a two‐treatment, multiple‐period crossover trial. Conditioning on subject‐specific totals removes any dependence on mean subject‐specific symptom rates and permits the use of standard software to perform regression analysis to examine treatment, period, carryover and interaction effects. The addition of offsets to the regression equations allows the incorporation of data from subjects who do not complete all period in the trial. We apply the proposed method to data from a crossover trial and discuss its advantages and disadvantages.
Marker‐dependent hazard estimation: An application to AIDS
Authors: Fusaro Robert E. , Nielsen Jens P. , Scheike Thomas H. 
Published: April 2007

AbstractThe acquired immunodeficiency syndrome (AIDS) results from infection with the human immunodeficiency virus (HIV). The time of infection is generally unknown since transmission usually occurs during the course of repeated sexual contacts or needle sharing. Brookmeyer and Gail describe the biases that may arise in survival analyses using the recruitment time rather than the unknown infection time as the origin in prevalent cohorts of HIV‐infected individuals. We apply a non‐parametric hazard estimator, introduced by Nielsen, that assumes the hazard of an AIDS diagnosis depends upon the unknown time of infection solely through the value of possibly multidimensional markers of HIV‐disease progression such as CD4+ T lymphocyte cell counts. Essentially, we estimate the hazard for a specific marker value y by dividing the number of occurrences among subjects with marker measurements in a neighbourhood of y by the total risk time in that neighbourhood. We present this estimator, which relies upon kernel estimator techniques to produce a smooth estimate, within a counting process framework. We apply this method to marker data from the San Francisco Men's Health Study.
Pitfalls inherent in retrospective time‐to‐event studies: The example of time to pregnancy
Authors: Weinberg Clarice R. , Baird Donna Day , Rowland Andrew S. 
Published: April 2007

AbstractRetrospective studies of time from initiation of risk (for example, transfusion of HIV‐infected blood) to the occurrence of an endpoint of interest are useful in epidemiology. One example is studies of time to pregnancy, which have evaluated exposures that may affect human fertility. One can reconstruct the non‐contracepting interval required for each woman's most recent pregnancy and then treat the data as if the couples had been studied prospectively. As we illustrate, however, failure‐time models can be dangerously misleading when there have been trends over calendar time in exposures under study. We propose anad hocmethod for evaluating possible effects on fertility despite this bias, by making use of external data on trends in the exposure over time. This approach applies a prospective model and generates an empiricalp‐value, based on comparing the data‐based estimated exposure coefficient with its null distribution estimated by simulation. A second method maximizes a conditional likelihood, and we show that this is equivalent to logistically modelling the relative odds for the subject's exposure as related to the reported time she required to achieve pregnancy.
Generalized logistic models for low—dose response data
Authors: Devidas Meenakshi , George E. Olusegun , Zelterman Daniel 
Published: April 2007

AbstractWe discuss a generalization of the logistic response function of the form Pr(y = 1|x) = {1 + exp( − θ − β'x)}−α, where α > 0. This function coincides with the usual logistic response when the shape parameter α is equal to one. We describe the use of this model for analysing cancer rates in mice for low‐dose exposure to a known carcinogen. When estimating the low‐dose responses, the errors associated with extrapolation are reduced when a priori knowledge about the rates among unexposed individuals is incorporated into the fitting procedures.
Time‐period effects in longitudinal studies measuring average rates of change
Authors: Roe Denise J. , Korn Edward L. 
Published: April 2007

AbstractRandom time‐period effects are unexplained increases or decreases in the observed value for all individuals measured at a particular time point in a longitudinal study. They can be caused by learning effects, changes in equipment, personnel and overall subject co‐operation. We investigate the consequences of time‐period effects in random coefficient regression models, where interest is in the average rate of change (slope) of a continuous outcome. In a study with a single group of subjects, they can lead to conditionally biased estimates of the mean slope and its variance (conditional on the time‐period effects). Calculations suggest that the increase in sample size required to maintain a specified precision of the mean slope estimate over repeated studies may be substantial. In a study with a concurrent control group, however, time‐period effects do not distort the expectation, estimated variance or the distribution of the difference between the mean slopes. With missing data, in addition to time‐period effects, an unbaised estimate of a single mean slope remains problematic, but one can use standard maximum likelihood techniques to obtain consistent estimators of the difference in mean slopes and its variance. This suggests the importance of a concurrent control group when potential time‐period effects are of concern.
Solving the cluster puzzle: Clues to follow and pitfalls to avoid
Authors: Wartenberg Daniel , Greenberg Michael 
Published: April 2007

AbstractDozens of methods have been proposed for the identification of disease clusters, although only a few are used routinely in published investigations. New methods, although designed to exploit some particular aspect of the data or use some specific statistical tool, are rarely compared thoroughly in terms of power or performance. Users, when confronted with the multitude of methods available, often select methods arbitrarily, basing choices on software availability, ease of implementation, or use experience rather than considerations of statistical power, possible alternative hypotheses )that is, cluster structure( and likely confounding. In this review, we extend our typology of disease clustering methods and apply it to many of the extant methods identifying strengths, weaknesses and unique features of the methods. We conclude with recommendations for which methods should be applied to which types of situations.
Disease clusters, maxima, and P‐values
Authors: Grimson Roger C. 
Published: April 2007

AbstractThis paper presents combinatorial )exact( methods that are useful in the analysis of disease cluster data obtained from small environments, such as buildings and neighbourhoods. Maxwell–Boltzmann and Fermi–Dirac occupancy models are compared in terms of appropriateness of representation of disease incidence patterns )space and/or time( in these environments. The methods are illustrated by a statistical analysis of the incidence pattern of bone fractures in a setting wherein fracture clustering was alleged to be occurring. One of the methodological results derived in this paper is the exact distribution of the maximum cell frequency in occupancy models.
Assessing directional effects in spatial data
Authors: Oden Neal L. 
Published: April 2007

AbstractA variable is measured at two locations separated by a given distance. Are the values more similar to each other if the locations are oriented in one direction than another? This question has application to studies of human genetics, epidemics, and acid rain. One obvious analytic approach, regression on latitude and longitude, fails when data are non‐directional )isotropic( but spatially autocorrelated. Moreover, although non‐zero slope implies similarity between neighbours, the converse is not true. IDIFF, a statistic derived from Moran's coefficient of spatial autocorrelation, is developed to detect general directional effects that apply to the collection of data points. Simulations suggest that, when data have isotropic spatial autocorrelation but are incorrectly assumed to be independent, IDIFF will at worst reject too little. IDIFF has good power to distinguish epidemics that spread non‐directionally from those that spread in a favoured direction.
Analysing post‐alarm data in a monitoring system in order to accept or reject the alarm
Authors: Chen R. , Connelly R. R. , Mantel N. 
Published: April 2007

AbstractIn a disease monitoring system embracing several diseases and communities, alarms indicating a significant increase in disease incidence will frequently occur even though the false alarm rate has been set quite low. An approach is presented by which an alarm is determined as either confirmed or rejected according to data observed subsequent to the alarm. The suggested procedure is expected to weed out a substantial proportion )say about 75 per cent( of the false alarms at the expense of some delay in detecting a true alarm.
Properties of Tango's index for detecting clustering in time
Authors: Rayens Mary Kay , Kryscio Richard J. 
Published: April 2007

AbstractTango proposed an index for detecting disease clustering in time applicable to grouped data obtained from a population that remains fairly stable over the study period. In this paper, we show that Tango's index is a two‐dimensional U‐statistic having an asymptotic normal distribution. To apply this result in the finite sampling situation, an Edgeworth expansion is used and is shown to be at least as accurate as Tango's best result in approximating the tails of his test statistic under the null hypothesis. This is extended to show that the Edgeworth expansion can be used to approximate the power of Tango's test statistic under selected alternatives to randomness. A power study based on simulations is conducted to compare the power of Tango's index to that of three of its competitors.
Power of the scan statistic for detection of clustering
Authors: Wallenstein Sylvan , Naus Joseph , Glaz Joseph 
Published: April 2007

AbstractThe scan statistic is the maximum number of events in an interval of fixed length w as the subinterval moves over the entire time frame. Previous research derived the null distribution of the scan statistic under the conditional model which assumed that the total number of events was fixed, and under the unconditional model which let the total number of events be a random variable. This paper derives approximations for the power of the scan test for a pulse alternative. Under this alternative, the relative risk of disease on a subinterval )τ, τ + w(, τ unknown, is θ‐fold as high as it is for other subintervals of length w. Two sets of approximations are given for each model. The first approximation gives highly accurate results, but requires use of a personal computer. The second procedure can be performed on a hand‐held calculator and appears very accurate for the cases examined.
Approximations for the tail probabilities and moments of the scan statistic
Authors: Glaz Joseph 
Published: April 2007

AbstractThe scan statistic is used to test the hypothesis that the observed events occur at random )uniformly distributed( in time or space versus the hypothesis that they cluster within a moving window of sizew. To implement the testing procedure based on the scan statistic its tail probabilities have to be effectively evaluated. In this article a survey of results on the approximations of the distribution of the scan statistic and its moments is presented. Numerical results comparing these approximations are also given. Numerous references with applications in epidemiological studies using the scan statistics are mentioned. Related scan statistics that have been used in many other interesting applications are listed in the reference section. The article concludes with the presentation of unsolved problems related to the scan statistic.
Effect of relative risk and cluster configuration on the power of the one‐dimensional scan statistic
Authors: Sahu Sujit K. , Bendel Robert B. , Sison Cristina P. 
Published: April 2007

AbstractThe scan test for clustering in time is based on the maximum number of events in an interval )window( of width w as the window moves across the entire time frame. Power estimates of the scan statistic are simulated for a variety of epidemiologically motivated situations. Two cluster configurations are used: a rectangular pulse, and a triangular pulse designed to emulate environmental contamination. For a rectangular pulse, the relative risk R of disease in the cluster region in R‐fold as high as it is for the background region. The power is strongly influenced by the sample size, the relative risk, and the width or duration of the cluster region, whereas the effect of the cluster configuration is small. Using a 5 per cent significance level, a relative risk of 4, a standardized cluster duration of 0·10, a relative window width of 1·5, and a )non‐random( sample size of 50, the simulated power is approximately 80 per cent, indicating that the minimum sample size in the cluster region for adequate power is in the 12–32 range for values of the parameters used in this study.
The effects of scale on tests for disease clustering
Authors: Waller Lance A. , Turnbull Bruce W. 
Published: April 2007

AbstractSurveillance of a large geographic region for ‘clusters’ of adverse health events, particularly cancers, often involves searching for raised incidence in the vicinity of prespecified putative sources of hazard. For reasons of practicality or of confidentiality, incidence and population data are usually only available aggregated over subregions or ‘cells’. The performance of statistical procedures designed to detect the presence of clusters can be highly sensitive to the level of aggregation, that is to the choice of partition of the region into the cells. We investigate this sensitivity in the cases of three recently proposed procedures, namely those of Besag and Newell, Stone, and Waller et al. For illustration, we use leukaemia incidence data for 1978–82 in a region of upstate New York, with inactive hazardous waste sites containing trichloroethylene acting as suspected sources.
Assessing spatial patterns in disease rates
Authors: Walter S. D. 
Published: April 2007

AbstractWe describe the empirical performance of three indices of spatial autocorrelation )Moran'sI, Geary'scand a rank adjacency statisticD( in the analysis of regional cancer incidence data. Heterogeneity in regional population sizes and age structure leads to variable precision in estimated rates; the usual methods for assessingI, candD, which ignore such heterogeneity, are shown to be liberally biased, especially forcandD. The power of these indices to detect likely disease patterns is estimated by simulation; the power is quite variable, depending on the exact pattern assumed, althoughItends to have the highest power. The null distributions appear quite robust in small samples, even when several regions have no observed case. Preliminary work on the Ontario cancer registry showed generally unimportant effects on the spatial analysis of variation in case registration rates or missing residence data.
Disease mapping using empirical bayes and bayes methods on mortality statistics in the Netherlands
Authors: Heisterkamp S. H. , Doornbos G. , Gankema M. 
Published: April 2007

AbstractData from the 64 public health service districts in the Netherlands, describing the health status of the Dutch population, were used to construct maps for several causes of deaths. The choice and estimation of the relative risk measure is described. The prior expected number of deaths was modelled using a Poisson regression approach based on a model with main effects of district and age. Desirable properties of risk parameters for disease mapping are that they both reflect the level of risk and cope with the instability in the observed measure caused by the numbers at risk in each district. Different techniques for estimation of parameters were applied: empirical Bayes estimation )EB( using a non‐parametric prior and a gamma prior, and a Bayesian approach )B( with a uniform prior. For the parametric EB also a constrained estimator was used. The EB techniques studied in this paper shift or smooth the values of the risk parameter towards a global mean. In the Bayesian method applied here, spatial dependence among districts can be modelled, that is the estimates are smoothed towards a local mean. The three EB estimates gave by and large similar results, although the constrained EB estimate smoothed less, as was expected. The Bayesian estimates smoothed the estimates more or less similarly to the constrained EB.
Aggregation of existing geographic regions to diminish spurious variability of disease rates
Authors: Morris Robert D. , Munasinghe Rajika L. 
Published: April 2007

AbstractThe availability of large data sets together with the growth in power and storage capabilities of computers have made the analysis of the spatial distribution of disease rates an increasingly important tool in public health research. Use of existing geographic divisions or groupings tends to result either in unstable estimates of disease rates if the corresponding populations are small or in loss of spatial resolution if the areas are unnecessarily large. This paper describes a computer algorithm for combining existing geographic areas into regions with populations large enough to diminish spurious variability in disease rates while limiting the loss in resolution. The method is demonstrated using Medicare hospital admissions data for pneumonia and central nervous system cancer. Disease rates were calculated for both predefined regions and those generated by the algorithm and their frequency distributions were compared. The algorithm produces more stable rates over a variety of diseases and provides substantially more flexibility than the use of predefined aggregations.
Synthetic cancer variables and the construction and testing of synthetic risk maps
Authors: Jacquez Geoffrey M. , Kheifets Leeka I. 
Published: April 2007

AbstractCancer cluster investigations are usually univariate in nature; they focus on a particular cancer, such as leukaemia, and attempt to determine whether excess risk is associated with a suspected cancer‐causing agent. Although several causes of death )such as leukaemia, lymphoma, Hodgkin's( may be considered, the approach is univariate because the causes of death are analysed sequentially and independently of one another. This approach is consistent with a one‐cause one‐effect model. Rarely, however, is the action of a carcinogen manifested at only one body site, and correlations among causes of death are the norm rather than the exception. A multiple effects model is therefore appropriate, and the multivariate nature of cancer mortality data should be exploited when exploring geographic pattern in cancer risks. This paper describes such an approach. We construct maps based on a principal components analysis of cancer mortality rates from different geographic areas. The resulting principal components are called synthetic cancer variables )SCVs(, and maps of the SCV scores are synthetic risk maps )SRMs(. These maps quantify geographic variation in cancer risk at several body sites simultaneously, and may be analysed for )1( spatial structure and )2( geographic association with potential risk factors. As an example, we use synthetic risk maps to determine whether high‐risk counties in Illinois cluster near nuclear facilities. Much work remains to be done, but synthetic cancer risk maps appear to be a useful tool for quantifying geographic pattern and multivariate structure in cancer mortality.
Mixture models and disease mapping
Authors: Schlattmann Peter , Böhning Dankmar 
Published: April 2007

AbstractThe analysis and recognition of disease clustering in space and its representation on a map is one of the oldest problems in epidemiology. Some traditional methods of constructing such a map are presented. An alternative approach using mixture models to identify population heterogeneity and map construction within an empirical Bayes framework is described. For hepatitis B data from Berlin in 1989, a map is presented and the different methods are evaluated using a parametric bootstrap approach.