bluuebunny/crossref_metadata_2025_split huggingface dataset:
Problems and prediction in survival‐data analysis
Authors: Henderson Robin 
Published: April 2007

AbstractTwenty‐one years after its appearance, Cox's 1972 paper on ‘Regression models and life tables’ continues to be one of the most frequently cited publications in the scientific and medical literature. The proportional‐hazards model and partial‐likelihood technique have been applied to thousands of data sets, not always appropriately, and have motivated hundreds of theoretical studies, not always relevant. Recent developments are reviewed and continuing problems discussed, especially with respect to predictive inference. The accuracy of model‐based predictions, and how they compare with consultants' judgements are investigated by means of an example.
What do we mean by a statistical model?
Authors: Appleton David R. 
Published: April 2007

AbstractStatisticians are too often satisfied by fitting data rather than investigating the process out of which the data arose. The assumptions on which they base their models may be quite unrealistic, and while it is true that a model should not be more complicated than necessary, neither should it be too simple. Ways of approaching several sets of data from different areas of clinical medicine are considered, and different attitudes to the purpose of modelling highlghted. The transition from smoothing data, through fitting curves, to modelling underlying processes is discussed.
Teaching statistics to non‐specialists
Authors: Simpson Judy M. 
Published: April 2007

AbstractTeaching statistics to non‐specialists is a challenge for which most statisticians are unprepared by their own training within an academic mathematics department. Most statistics courses for medical undergraduates still focus on research statistics, whereas it would be more appropriate to concentrate on statistics relevant to clinical decision‐making about an individual patient. Teaching statistics to Master of Public Health students presents further challenges because of the wide variety of their backgrounds and the greater demands from mature postgraduates. Whatever the audience, however, the same principles apply: medical statistics should be taught as non‐mathematically as possible, only introducing formulae when absolutely necessary and explaining their components; plenty of practical applications should be given; there should be ample opportunity for practice to gain hands‐on experience using both calculators and computers (preferably with MINITAB); and tutorials should be streamed according to perceived mathematical ability, with remedial mathematics teaching available to those who need it.
Life as an academic medical statistician and how to survive it
Authors: Pocock Stuart J. 
Published: April 2007

AbstractThis article aims to review the professional life of academic medical statisticians. Topics covered include training opportunities, the shortage of medical statisticians, the relevance of PhD degrees, the varied working life (teaching, research, computing, refereeing etc.), research priorities across consultancy, collaborations and methodology, professional recognition and the variety of working environments. In conclusion, some tentative suggestions are made on how to survive happily in the world of academic medical statistics.
Adaptive assignment versus balanced randomization in clinical trials: A decision analysis
Authors: Berry Donald A. , Eick Stephen G. 
Published: April 2007

AbstractWe compare balanced randomization with four adaptive treatment allocation procedures in a clinical trial involving two treatments. The objective is to treat as many patients in and out of the trial as effectively as possible. Randomization is a satisfactory solution to the decision problem when the disease in question is at least moderately common. Adaptive procedures are more difficult to use, but might play a role in clinical research when a substantial proportion of all patients with the disease are included in the trial.
Binary regression with continuous outcomes
Authors: Suissa Samy , Blais Lucie 
Published: April 2007

AbstractClinical research often involves continuous outcome measures, such as blood cholesterol, that are amenable to statistical techniques of analysis based on the mean, such as the t‐test or multiple linear regression. Clinical interest, however, frequently focuses on the proportion of subjects who fall below or above a clinically relevant cut‐off value, as a measure of the risk of disease. The customary approach to analyse such data is to dichotomize the continuous outcome measure and use statistical techniques based on binary data and the binomial distribution. In this paper, we use a parametric approach and the framework of generalized linear models to fit various regression models, including the logistic, on the basis of the original continuous outcome. We consider the Gaussian and the three‐parameter log‐normal distributions for the continuous outcome, assessing both precision and bias under various conditions. In simulation analyses, we find that we are unable to fit some of the samples with the ‘dichotomous’ approach, but we can with the ‘continuous’ approach, and that the latter yields estimates between 25 and 85 per cent more efficient than the former. We illustrate the method, programmed using GLIM macros, with data from clinical studies of the risk of hypoxaemia during open thoracic surgery and the risk of nocturnal hypoglycaemia among diabetic children.
Age‐related standards for ordinal data: Modelling the changes in visual acuity from 2 to 9 years of age
Authors: Wade A. M. , Ades A. E. , Salt A. T. , Jayatunga R. , Sonksen P. M. 
Published: April 2007

AbstractThe visual acuities of 1140 children between the ages of 2 and 9 years were measured using the Sonksen—Silver Acuity System (SSAS) from a distance of 3 metres. Ordered logistic regression was used to construct smoothly changing age‐related reference ranges for the proportions of children at different ages expected to have acuities in each of three ordinal categories. Exponential functions of age fitted the data better than polynomials. The resulting reference ranges can be used to derive the ages at which a given percentage of children achieve each of the acuity cut‐offs. However, since it is unlikely that all children will be available for testing at the exact appropriate age, two possible alternatives are introduced and discussed. First, a new ordinal category that would result in a chosen outcome at a pre‐selected age is calculated. Secondly, because it may be more feasible to use the current SSAS system whilst varying the distance of measurement, age‐related standards showing the distances required to give constant pass‐rates with the usual letter‐sizes are also presented.
Non‐parametric estimation for the difference or ratio of median failure times for paired observations
Authors: Jung Sin‐Ho , Su John Q. 
Published: April 2007

AbstractWe propose a non‐parametric method to calculate a confidence interval for the difference or ratio of two median failure times for paired observations with censoring. The new method is simple to calculate, does not involve non‐parametric density estimates, and is valid asymptotically even when the two underlying distribution functions differ in shape. The method also allows missing observations. We report numerical studies to examine the performance of the new method for practical sample sizes.
A simulation study of estimators for rates of change in longitudinal studies with attrition
Authors: Wang‐Clow Fong , Lange Mary , Laird Nan M. , Ware James H. 
Published: April 2007

AbstractMany longitudinal studies and clinical trials are designed to compare rates of change over time in one or more outcome variables in several groups. Most such studies have incomplete data because some patients drop out before completing the study. The missing data may induce bias and inefficiency in naive estimates of important parameters. This paper uses Monte Carlo methods to compare the bias and efficiency of several two‐stage estimators of the effect of treatment on the mean rate of change when the missing data arise from one of four processes. We also study the validity of confidence intervals and the power of hypothesis tests based on these estimates and their standard errors. In general, the weighted least squares estimator does relatively well, as does an analysis of covariance type estimator proposed by Wu et al. The best estimates of variance components are based on complete cases or maximum likelihood.
A comparison of methods that characterize pulses in a time series
Authors: Mauger David T. , Brown Morton B. , Kushler Robert H. 
Published: April 2007

AbstractThere is general recognition that some glands secrete hormones primarily as a series of pulses. One can generally classify the proposed methods of pulse identification and characterization as either (i) criterion‐based, that is, they use a criterion such as a test statistic to identify a rise and/or fall in hormone level within a moving window, or (ii) model‐based, that is, they specify a statistical model for the time‐varying portion of the signal and estimate its parameters.Using simulated data, we compare and contrast seven criterion‐based methods and three model‐based methods. The model‐based methods perform better in estimating the parameters of interest; they are most effective with the sampling rate chosen so that there are 3–5 samples taken during the half‐life of the hormone. At higher sampling rates the methods may overidentify pulses (false positives) and at lower sampling rates they may miss pulses (false negatives), both of which lead to biased estimates for the parameters of the signal.
Methodological and statistical problems in the construction of composite measurement scales: A survey of six medical and epidemiological journals
Authors: Coste Joël , Fermanian Jacques , Venot Alain 
Published: April 2007

AbstractComposite measurement scales (CMS) are increasingly used in medicine to measure complex phenomena or concepts such as disease risk and severity, physical and psychological functioning and quality of life. To investigate the methodology currently used in the construction of CMS, we examined 46 studies recently published in six major medical and epidemiological journals. Important measurement properties such as measurement level, content and construct validity and reliability are often neglected. Statistical methods, particularly multivariate methods are frequently misused; verifications of model relevance and assumptions, and cross‐validations to avoid overfitting are seldom performed. We propose recommendations for the construction and the presentation of CMS, to help authors and investigators to report and choose, respectively, measurement instruments for a complex phenomenon.
Comparison of several goodness‐of‐fit tests for the kappa statistic based on exact power and coverage probability
Authors: Basu Srabashi , Basu Ayanendranath 
Published: April 2007

AbstractWe present a general approach based on chi‐square goodness‐of‐fit tests for performing tests of significance about the kappa statistic. We make recommendations for the particular goodness‐of fit test to use under different situations. We also compare the test statistics in terms of the generated confidence intervals about the kappa statistic.
A random‐effects regression model for meta‐analysis
Authors: Berkey C. S. , Hoaglin D. C. , Mosteller F. , Colditz G. A. 
Published: April 2007

AbstractMany meta‐analyses use a random‐effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random‐effects regression approach for the synthesis of 2 × 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random‐effects regression method performs well in the context of a meta‐analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within‐study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non‐zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta‐analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta‐analysis of continuous outcomes when covariates are present.
Association models for periodontal disease progression: A comparison of methods for clustered binary data
Authors: Have Thomas R. Ten , Landis J. Richard , Weaver Susan L. 
Published: April 2007

AbstractWe investigate population‐averaged (PA) and cluster‐specific (CS) associations for clustered binary logistic regression in the context of a longitudinal clinical trial that investigated the association between tooth‐specific visual elastase kit results and periodontal disease progression within 26 weeks of follow‐up. We address estimation of population‐averaged logistic regression models with generalized estimating equations (GEE), and conditional likelihood (CL) and mixed effects (ME) estimation of CS logistic regression models. Of particular interest is the impact of clusters that do not provide information for conditional likelihood methods (non‐informative clusters) on inferences based upon the various methodologies. The empirical and analytical results indicate that CL methods yield smaller test statistics than ME methods when non‐informative clusters exist, and that CL estimates are less efficient than ME estimates under certain conditions. Moreover, previously reported relationships between population‐averaged and cluster‐specific parameters appear to hold for the corresponding estimates in the presence of these clusters.
Using sign score regression models to select variables in case‐control studies
Authors: O'Gorman Thomas W. 
Published: April 2007

AbstractThis paper evaluates the performance of four variable selection methods suitable for case‐control studies. Two of the methods are logistic regression and the rank transformed version of it which uses the ranks of the explanatory variables in place of the original observations. The third method is based on Kendall's τb correlations. I propose a fourth method, a sign score regression model to select variables. To evaluate these four methods, I generate many data sets for a case group and a control group with the use of several different distributions and covariance matrices. I evaluate the methods on their ability to select correctly the variables related to case‐control status while not selecting the unrelated variables. Using this criterion, the sign score regression method and the τb method are more effective than the other two methods with uncorrelated or weakly correlated variables. The sign score regression method is more effective than the τb method for all simulations that use normal variables and for some that use log‐normal variables. Overall, the sign score regression method is the most effective variable selection method for data sets that have low or moderate correlations between variables.
The development of the series of U.S. cancer atlases: Implications for future epidemiologic research
Authors: Mason Thomas J. 
Published: April 2007

AbstractAn historic perspective on the development of the National Cancer Institute's series of cancer atlases is provided. Emphasis is placed on the role which emergent questions concerning environmental determinants of cancer played in the acquisition and utilization of data resources. Studies which were fielded as a consequence of the atlases are highlighted. The legacy of the collective effort of many persons who worked on the development of the cancer atlases – the facilitation of many current epidemiologic investigations – is discussed.
Probabilistic linkage of large public health data files
Authors: Jaro Matthew A. 
Published: April 2007

AbstractProbabilistic linkage technology makes it feasible and efficient to link large public health databases in a statistically justifiable manner. The problem addressed by the methodology is that of matching two files of individual data under conditions of uncertainty. Each field is subject to error which is measured by the probability that the field agrees given a record pair matches (called themprobability) and probabilities of chance agreement of its value states (called theuprobability). Fellegi and Sunter pioneered record linkage theory. Advances in methodology include use of an EM algorithm for parameter estimation, optimization of matches by means of a linear sum assignment program, and more recently, a probability model that addresses bothmanduprobabilities for all value states of a field. This provides a means for obtaining greater precision from non‐uniformly distributed fields, without the theoretical complications arising from frequency‐based matching alone. The model includes an interative parameter estimation procedure that is more robust than pre‐match estimation techniques. The methodology was originally developed and tested by the author at the U.S. Census Bureau for census undercount estimation. The more recent advances and a new generalized software system were tested and validated by linking highway crashes to Emergency Medical Service (EMS) reports and to hospital admission records for the National Highway Traffic Safety Administration (NHTSA).
On combining dose—response data from epidemiological studies by meta‐analysis
Authors: Smith S. Jay , Caudill Samue L. P. , Steinberg Karen K. , Thacker Stephen B. 
Published: April 2007

AbstractUsing data from a meta‐analysis of the effects of oestrogen replacement therapy on the development of breast cancer, we compared alternative methods for combining dose—response slopes from epidemiological studies. We evaluated issues related both to summarizing data from single studies and to combining results from multiple studies. Findings related to the analysis of individual dose—response studies include: (1) a method of weighing studies that gives greater influence to dose—response slopes that conform to the linear relation of relative risk to duration can lead to large differences in calculated weights as a function of non‐linearity; (2) a regression model using a variable‐intercept resulted in a mean dose—response slope that increased as much as threefold when compared with the values obtained with a zero‐intercept model. When combining results from multiple studies, we found: (1) calculating standard errors of mean dose—response slopes by methods that allow for both among‐study and within‐study variability (a random‐effects type model) gave values different from a method that assumes homogeneity and equal within‐study precision (a fixed‐effects model); (2) the random‐effects model gives mean and standard error results most similar to a bootstrap resampling method as increasing heterogeneity is observed (however, this model could give biased mean estimates compared with the bootstrap method); (3) a components‐of‐variance model compares favourably with the bootstrap and is easier to apply than the random‐effects model. Based on these findings, we recommend the use of methods which incorporate heterogeneity to guard against underestimating the standard error. However, caution is urged because bias in point estimates can occur if extreme heterogeneity is present. Two other observations affect the interpretation of data combined from multiple studies. First, inclusion into a model of quality scores assigned by blinded reviewers had little effect on the mean dose—response slope and its standard error. Second, the number of studies required to achieve desired statistical power, varies with effect size.
Meta‐analytic approaches to dose‐response relationships, with application in studies of lung cancer and exposure to environmental tobacco smoke
Authors: Tweedie R. L. , Mengersen K. L. 
Published: April 2007

AbstractThis paper outlines several meta‐analytic approaches to the assessment of quantal dose—response relationships; that is, to the evaluation of an increase in the level of exposure to an agent and the associated relative risk of a disease when this is investigated over a number of different studies. Analysis is developed at two levels: first, a consistent method of evaluating the dose—response relationship is applied to each study, and second, an overall picture is obtained by comparing and combining these relationships. At the first stage, for an individual study, dose—response assessment involves choices of model and appropriate tests for trend, which are influenced by such issues as dose measurement and use of the unexposed group. At the second stage, different methods for pooling results across studies must be considered. These depend on the choices made in the first stage of analysis, with additional attention paid to heterogeneity, and possible bias due to studies included in meta‐analysis. We describe these meta‐analytic approaches for three methods of evaluating dose response. The approaches are illustrated by evaluating the relationship between lung cancer and levels of exposure to environmental tobacco smoke (ETS). The strength of this relationship has been a point of debate in recent assessment of evidence for an overall carcinogenic effect of ETS exposure. We find little indication of a consistent dose response, a result explained in terms of recent models for cancer and passive smoking developed by Darby and Pike, the current meta‐analysis results of overall risk‐ratios of current studies in Tweedie and Mengersen, and misclassification models developed by the United States Environmental Protection Agency (EPA).
A dual frame design for sampling elderly minorities and persons with disabilities
Authors: Ezzati Trena M. , Hoffman Keith , Judkins David R. , Massey James T. , Moore Thomas F. 
Published: April 2007

AbstractMultiple data sources are sometimes available as potential sampling frames for population surveys, and in some situations the use of a multiple frame sample design is more advantageous than using a single sampling frame. The use of multiple sampling frames, however, has variance and bias implications, as well as sampling, data collection, and logistical considerations. These issues are addressed for a proposed dual frame sampling approach in the National Health Interview Survey (NHIS). The results of an investigation of the sampling efficiencies and operational issues in supplementing the NHIS area frame sample with a sample of elderly African and Hispanic Americans and persons with disabilities selected from Social Security Administration files are presented.
Development and applications of a city‐level alcohol availability and alcohol problems database
Authors: Mackinnon David P. , Scribner Richard , Taft Katharine A. 
Published: April 2007

AbstractData on alcohol availability and problems in all cities in Los Angeles County were collected from several different sources and linked together to form a Local Alcohol Availability Database (LAAD). The two major purposes of the project are to provide a city‐level alcohol availability and alcohol‐related problems database needed by local community alcohol policy planners and to collect the data necessary for research on the relationship between these measures. The prevalence of drunk driving arrests is displayed on a map. We describe how the LAAD has been used to guide alcohol policy decisions. A fixed year and city effects regression model suggests that outlet density is positively related to several alcohol‐related problems.