bluuebunny/crossref_metadata_2025_split huggingface dataset:
Statistical methods for estimating attributable risk from retrospective data
Authors: Whittemore Alice S. 
Published: April 2007

AbstractThis paper extends Levin's measure of attributable risk1 to adjust for confounding by aetiologic factors other than the exposure of interest. One can estimate this extended measure from case‐control data provided either (i) from the control data one can estimate exposure prevalence within each stratum of the confounding factor; or (ii) one has additional information available concerning the confounder distribution and the stratum‐specific disease rates. In both cases we give maximum likelihood estimates and their estimated asymptotic variances, and show them to be independent of the sampling design (matched vs. random). Computer simulations investigate the behaviour of these estimates and of three types of confidence intervals when sample size is small relative to the number of confounder strata. The simulations indicate that attributable risk estimates tend to be too low. The bias is not serious except when exposure prevalence is high among controls. In this case the estimates and their standard error estimates are also highly unstable. In general, the asymptotic standard error estimates performed quite well, even in small samples, and even when the true asymptotic standard error was too small. By contrast, the bootstrap estimate2 tended to be too large. None of the three confidence intervals proved superior in accuracy to the other two. Thus there appears no advantage in using the log‐based interval suggested by Walter3′4 which is always longer than the simpler symmetric interval.
Relative risks of diseases in the presence of incomplete penetrance and sporadics
Authors: Majumder Partha P. , Chakraborty Ranajit , Weiss Kenneth M. 
Published: April 2007

AbstractIn most diseases of contemporary interest to epidemiology, the importance of familial risk factors is often assessed by relative risk measures computed from family history data. In this paper, we examine the efficiency of relative risk measures in discerning the genetic background of a disease. It is shown that even for a simple (one locus, two alleles) genetic disease, incomplete penetrance of the disease allele and/or occurrence of sporadics (i.e. non‐heritable cases caused by environmental risk factors) often yields a rather small value of relative risk. This means that a small value of relative risk need not necessarily imply that genetic factors are unimportant in the aetiology of the disease. Furthermore, it is shown that the relative risk measures are complicated functions of disease gene frequency, the penetrance parameter and the sporadic rate. A rejection of genetic involvement for a disease on the basis of a single relative risk estimate may, therefore, be erroneous.
Reducing classification errors in cohort studies: The approach and a practical application
Authors: Schulz Kenneth F. , Cates Willard , Grimes David A. , Selik Richard M. , Tyler Carl W. 
Published: April 2007

AbstractClassification errors of dependent variables can distort the results of observational studies. To reduce misclassification from our multicentre observational study of abortion complications, we extended the methodology of Lawrence and Greenwald9 for use in situations of unequal sample sizes and implemented both an office review and a field review. We reabstracted 424 reported complications and a random sample of 364 reported non‐serious cases from 12 institutions participating in our study. In total, 30 per cent of the reported serious complications turned out to be misclassified: the office review detected 74 per cent of the total number of misclassifications with the remainder found in the field review. Because, with our particular data base, we estimated expending only 15 per cent of the total resources with our office effort, this represented the most cost‐efficient approach to reducing classification errors. By eliminating the false positives from our study, we forced the specificity to 1·00 which produced both an unbiased estimate of the relative risk and an increase of 4 per cent to 63 per cent in the power of our study.
The association between HLA antigens and the presence of certain diseases
Authors: Green J. R. , Chiew Mui Kui , Low Heng Chin , Woodrow J. C. 
Published: April 2007

AbstractThe association between the presence or absence of two HLA antigens and coeliac disease, thyrotoxicosis and ankylosing spondylitis has been studied in patients and controls. The simple empirical logistic model and a refinement due to Dyke and Patterson have been used to examine which conditional associations are important.
The analysis of a sequential clinical trial for the comparison of two lung cancer treatments
Authors: Whitehead J. , Jones D. R. , Ellis S. H. 
Published: April 2007

AbstractMethods adopted in the analysis of data from a randomized controlled trial of a new treatment for inoperable lung cancer are described. The trial design employed the sequential logrank test using length of survival from time of randomization as the principal outcome measure. We describe the practical arrangements for regular inspections of the accumulating data as required by the sequential design, and also for more extensive but less formal interim analyses, and present some of the results. On termination of the trial the survival patterns of the two treatment groups were compared using methods of analysis developed specifically to allow for the sequential nature of the trial design. The role of prognostic factors was investigated using fixed sample methods which are approximately independent of the sequential design.
The logistic analysis of epidemiologic prospective studies: Investigation by simulation
Authors: Lilienfeld David E. , Pyne David A. 
Published: April 2007

AbstractWe performed a Monte Carlo computer simulation of the Walker‐Duncan logistic regression technique in a typical epidemiologic prospective setting and analysed the results with respect to the accuracy and reliability of the regression estimates and the associated statistical significance tests (Z‐tests). The results strongly suggest that the estimates were neither accurate nor reliable. The magnitude of the difference between the average estimated regression coefficient and its true population value did not necessarily decrease as the sample size increased. The average estimated standard deviation of the estimate of the regression coefficient either overestimated or underestimated the actual standard deviation, the former occurring most, but not all, of the time. The significance tests (a two‐tailed Z‐test with a significance level of 0.05) had actual type I errors ranging from 0.00 to 0.24 for different samples. This approach is therefore inadequate as an epidemiologic tool for analysis of a Framingham‐type prospective study. Further simulation studies are indicated.
Ensuring data quality in medical research through an integrated data management system
Authors: Marinez Yolan N. , McMahan C. Alex , Barnwell George M. , Wigodsky Herman S. 
Published: April 2007

AbstractAn effective data management system ensures high quality research data by making certain of the proper execution of the study design. This paper presents the components of a data management system and describes procedures for use in each component of the system to obtain high quality data. We discuss the interrelationship among the components of the data management system and the relationship of the data management system to other parts of the research project. We identify underlying principles in design and implementation of a data management system to ensure high quality data.
Interpretation of time trends in disease rates in the presence of generation effects
Authors: Gardner M. J. , Osmond C. 
Published: April 2007

AbstractA comparison is made of indices used for assessing trends in mortality rates over time, and results for four types of cancer are used as examples. The approaches contrasted as measures of change over calendar years of time are the use of crude death rates, standardized death rates (both direct and indirect approaches) and period effects estimated from an age‐period‐cohort model (including and excluding the cohort factor). The use of these indices is also compared with examination of the original age‐specific rates. Similar analyses are made of the equivalent indices for examining changes between different birth cohorts over time.Submodels of the age‐period‐cohort model are shown to be algebraically and empirically related to age‐standardized indices. The use of the full model, as contrasted to standardization methods, can result in important modifications to the interpretation in some cases. However, there are difficulties with this approach, as with all models, if applied uncritically. It is a wise precaution to examine the age‐specific rates in each instance to ensure that any summary index is appropriate.
A study of three techniques for time‐space clustering in hodgkin's disease
Authors: Chen Rina , Mantel Nathan , Klingberg Marcus A. 
Published: April 2007

AbstractThree techniques for disease time‐space clustering analysis, those of Knox, Mantel and Ederer‐Myers‐Mantel, were applied to simulated data so as to study their sensitivities. The simulated data corresponded to three alternative non‐null models for the distribution, transmission and development of Hodgkin's disease (HD) which were formulated in accordance with the results of published studies.The results indicate that the three techniques may not be sufficiently sensitive to the clustering in a real data set of HD cases. Therefore, the inconclusive results obtained to date with regard to clustering of HD may be related to the low power of the statistical techniques employed.
Comparison of two statistical techniques for the surveillance of birth defects through a monte carlo simulation
Authors: Barbujani Guido , Calzolari Elisa 
Published: April 2007

AbstractIn a multi‐hospital surveillance system for detecting increases in congenial malformation rates, the performance of the set technique applied individually to hospitals and of the cumulative sum (Cusum) technique applied to the aggregate of hospitals has been compared using a Monte Carlo method. The Cusum technique appears to be both more sensitive to real increases and less likely to issue false alarms.
A classification of scottish infants using latent class analysis
Authors: Pickering R. M. , Forbes J. F. 
Published: April 2007

AbstractThis paper illustrates the use of latent class analysis to classify 50,000 infants into a small number of classes or case types, as a preliminary to a study of the allocation of neonatal hospital resources throughout Scotland. Information, extracted from a detailed neonatal discharge record, was summarized by 11 clinical and diagnostic catagorical variables. Statistical models incorporating 1 to 6 latent classes were then estimated using the EM algorithm. The 4 class model was chosen because it provided a good description of the data and the resulting classes had a medical interpretation. The factors influencing the choice of model are discussed and goodness of fit tests are presented. The stability of the classes was also investigated using random halves of the data and an earlier comparable data set.
Inter‐rater agreement among orthodontists in a blocked experiment
Authors: Korn Edward L. , Baumrind Sheldon 
Published: April 2007

AbstractFive orthodontists were asked to predict for 64 patients a particular dichotomous outcome of treatment based on pre‐treatment X‐ray films. The orthodontists rated the cases in blocks of size 4‐6 with the knowledge of the number of positive outcomes in each block. We discuss the reasons why this blocked design is appropriate whenever clinicians are asked to rate cases which have not been randomly selected from a clinical practice similar to their own. We give a simple description of the inter‐rater agreement for this type of blocked experiment as well as a procedure to test that the agreement is no better than that expected by random independent assignment.
Birth cohort analysis using irregular crosssectional data: A technical note
Authors: Schifflers E. , Smans M. , Muir C. S. 
Published: April 2007

AbstractCancer motality and morbidity data are usually collected and published by calendar time period and by age class. Transformation of the age‐specific incidence or mortality rates into those for birth cohorts is readily undertaken when one age class of each time period corresponds to a given cohort: a requirement that is often not satisfied. The authors propose a method for the computation of birth cohort age‐specific incidence rates given irregular cross‐sectional data. The procedure is based on a cross‐sectional interpolation of cumulated population and case figures, from which cohort rates can be derived. Using the method several examples of trends in cancer incidence by birth cohort are discussed.
Treatment allocation methods in clinical trials: A review
Authors: Kalish Leslie A. , Begg Colin B. 
Published: April 2007

AbstractA comprehensive review of methods for allocation of treatments to patients in clinical trials is presented. Attention is restricted to controlled, prospective trials, as opposed to comparisons involving historical or other ‘external’ controls. We describe the features of each method and classify them according to whether their primary focus is randomization, efficiency, or balance with respect to prognostic factors. Methods which prevent bias, ensure an efficient treatment comparison and are simple to implement will contribute to the ability of clinical trials to provide precise and valid treatment comparisons. We assess critically the extent to which the methods achieve these goals, review the relationships of the allocation methods with subsequent analyses of the trial results, discuss current usage and provide guidelines for choice of method.
The use of logit models to investigate social and biological factors in infant mortality. I: Methodology
Authors: Murrells T. J. , Smith T. M. F. , Catford J. C. , Machin D. 
Published: April 2007

AbstractInfant mortality data for England and Wales, cross classified by mother's age, parity and social class, have been published on two occasions, the first giving the relevant data for 1949/50, the second for 1975, some 25 years later. Published analyses of these separate data sets have been based on graphical and tabular methods. This paper describes the statistical methodology appropriate to the use of logit models to investigate these data sets and shows how such models may be used to supplement the more informal approach. The stillbirth data for 1975 are used for illustration. Paper II, which follows, explores the stillbirth data in greater detail, with emphasis on the changes between 1949/50 and 1975. Subsequent papers describe neonatal and post‐neonatal deaths using the same framework. A final paper summarizes the overall changes in infant mortality by means of a multilogit model.
The use of logit models to investigate social and biological factors in infant mortality. II: Stillbirths
Authors: Murrells T. J. , Catford J. C. , Smith T. M. F. , Machin D. 
Published: April 2007

AbstractInfant mortality data for England and Wales cross‐classified by mother's age, parity and social class have only been published for 1949/50 and 1975. Appropriate statistical methodology for the analysis of such data has been described in Paper I, which used the 1975 stillbirth data for illustration. This paper examines the stillbirth data from both years by formally incorporating the year of data collection into the statistical model, enabling changes in the age, parity and social class effects with time to be investigated. Despite a marked reduction in stillbirth mortality from 21.1 to 10.1 per thousand over the period, the relative contribution of social class has increased. In particular there has been a relative increase in risk for mothers in the lower social classes. By contrast the effects of age and parity, although remaining important, have diminished over the period.
Empirical bayes estimation of cancer mortality rates
Authors: Tsutakawa Robert K. , Shoop Gary L. , Marienfeld Carl J. 
Published: April 2007

AbstractMortality rates for specific cancer types and age‐sex groups computed for large numbers of cities typically show extreme fluctuation. This is primarily due to the rare occurrence of specific cancer deaths in most of the small and moderate size cities during a fixed time period. Assuming a Poisson death process, we use an empirical Bayes method to obtain adjusted rates that are more stable for comparison of cities and prediction of future mortality. We have chosen stomach and bladder cancers in Missouri cities to illustrate the problems, techniques and results.
Linear models for the analysis of variability in factorial designs: An application to anthropometric indicators
Authors: Bossi Anna , Milani Silvano 
Published: April 2007

AbstractThis paper has two aims. One is to evaluate, in a set of 9740 ‘reference’ neonates born in five Italian centres (Trieste, Milan, Parma, Rome, Bari) during 1973‐1976 the effects of gestational age and birthplace on the variability of crown‐heel length and head circumference at birth; this is a preliminary step in the construction of ‘Italian’ intrauterine growth standards for such anthropometric measures. The other aim is to put the various techniques for the analysis of variability into a common framework of Linear Models, either Generalized or Classical, to compare their main features and provide some indications about their application.
Small sample estimation of log odds ratios from logistic regression and fourfold tables
Authors: Walter S. D. 
Published: April 2007

AbstractSchaefer has proposed a method to correct the maximum likelihood logistic regression coefficients for bias in small samples. We show here that this reduces, in the special case of a single dichotomous regression variable, to an earlier result of Haldane, for the estimation of a single log odds ratio. This paper reviews various estimators for the log odds ratio in a fourfold table, and compares the properties of two by complete enumeration in sets of tables with small sample sizes. The first of these, also due to Haldane, is based on the addition of 1/2 to each cell of the table; the second is where 1/2 is added to all cells of the table only if a zero frequency arises.We evaluate the bias and mean squared error of both of these estimators in sets of tables with various sample sizes snd odds ratios. Haldane's estimator usually has lower bias and MSE, and so we do not in general recommend the practice of adding 1/2 only as necessary. Exceptions might occur if one has gooda prioriestimates of the outcome probabilities in the two samples under comparison.
The use of an autoregressive model for the analysis of longitudinal data in epidemiologic studies
Authors: Rosner Bernard , Muñoz Alvaro , Tager Ira , Speizer Frank , Weiss Scott 
Published: April 2007

AbstractKorn and Whittemore1,2 have presented methods for analyzing longitudinal data where the number of observations per individual is large relative to the number of variables considered for each subject. However, this is often not the case in epidemiologic studies, since one usually collects data at relatively few time points, and the quantity of data collected for each individual at each time point is typically extensive. We present here an autoregressive model for analyzing longitudinal data of this type for the case of a continuous outcome variable. Some of the important features of this model are that one can (1) in the same analysis, consider both independent variables that are time‐dependent and those that are fixed over time, (2) partially use data for an individual where some examinations are missing, (3) assess relationships between changes in outcome and exposure over short periods of time, (4) use ordinary multiple regression methods. Anderson3 has considered this type of model, but, to our knowledge, the model has never been applied to biostatistical problems. We illustrate these methods with data from a longitudinal study that seeks to identify the role of personal cigarette smoking on changes in pulmonary function in children.
Interim analyses in clinical trials: Classical vs. bayesian approaches
Authors: Berry Donald A. 
Published: April 2007

AbstractThis paper concerns interim analysis in clinical trials involving two treatments from the points of view of both classical and Bayesian inference. I criticize classical hypothesis testing in this setting and describe and recommend a Bayesian approach in which sampling stops when the probability that one treatment is the better exceeds a specified value. I consider application to normal sampling analysed in stages and evaluate the gain in average sample number as a function of the number of interim analyses.
On tests for equality of predictive values fortdiagnostic procedures
Authors: Bennett B. M. 
Published: April 2007

AbstractThis paper concerns comparisons of the efficiency of several diagnostic tests, as characterized by the measures of sensitivity (ξ), specificity (η) and predictive value (ρ). We show that hypotheses concerning the equality of predictive values relate only to hypotheses concerning ξ and η and that we can test these by approximate χ2statistics. Data for the cases of t = 2 or 3 diagnostic tests illustrate the method.
Measures of explained variation for survival data
Authors: Korn Edward L. , Simon Richard 
Published: April 2007

AbstractThe predictive power of a set of prognostic variables in a survival time model is a concept distinct from the statistical significance of the variables or the adequacy of the model fit. In this paper we discuss the importance of quantifying the predictive power of a prognostic model, and suggest measures of explained variation as a possible quantification. The important features of our approach are that (1) the measures are completely model‐based; (2) a specification of the time range of interest is easily incorporated; and (3) the null models used for comparison are derived as mixtures of the predicted distributions.
Further studies in the variability of pock counts
Authors: Breslow Norman 
Published: April 2007

AbstractThe problem of estimating viral activity from pock counts that exhibit a substantial degree of overdispersion is revisited from the viewpoint of quasilikelihood with unknown parameters in the variance function. Score statistics are developed for tests and interval estimates of both mean and variance parameters. These yield somewhat different conclusions than do methods based exclusively on point estimates and standard errors, especially for the variance parameters. A Monte Carlo study confirms that, in the absence of strong prior information about the shape of the error distribution, inferences on variances are highly unstable in small to moderate samples.