bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monitoring maternity services by postal questionnaire: Congruity between mothers' reports and their obstetric records
Authors: Martin Claudia J. 
Published: April 2007

AbstractThe validity of information obtained from women about the medical aspects of their childbearing experiences has been assessed by comparing the responses of 223 mothers on a postal questionnaire with information extracted from their medical records. When discrepancies between the two sources were found, it was not assumed that the hospital records were correct. Instead, attempts were made to ascertain why the data might be inconsistent by re‐checking the records and by contacting the mothers again. There appeared to be five main reasons for discrepancies: mothers' limited knowledge or understanding of certain procedures; problems of interpretation and definition; occasionally inaccurate or missing information in the medical records; underreporting of sensitive information by mothers and, finally, questions which were misunderstood or misinterpreted by the mothers. For most items, however, good agreement between the mothers and hospitals was obtained, and the results indicate that valid information over a range of events can be obtained using postal surveys of mothers.
A cost‐function approach to the design of reliability studies
Authors: Eliasziw Michael , Donner Allan 
Published: April 2007

AbstractWe present a method to determine the number of subjects, k, and number of repeated measurements, n, that minimize the overall cost of conducting a reliability study, while providing acceptable power for tests of hypotheses concerning the reliability coefficient p. Tables showing optimal choices of k and n under various cost constraints also appear. We discuss design considerations for reliability studies in light of the results.
Hypothesis testing of time‐dependent recurrent events
Authors: Niland Joyce C. , Thomas Duncan , Azen Stanley P. 
Published: April 2007

AbstractThis paper examines the problem of hypothesis testing for comparison of time‐dependent recurrent events between categorical exposure groups. We compare three methods (a summary chi‐square test based on a generalization of the simple Poisson distribution, a chi‐square test based on a generalization of the compound Poisson distribution, and a test on risk scores based on individual observed to expected ratios), when the dependent variable may be autocorrelated within an individual, and disease risk may be heterogeneous among subjects. We present a simulation study and an application to a cohort of sickle cell anaemia patients. With autocorrelation or heterogeneous risk present, the simple chi‐square test is inappropriate, while the other two methods perform well. An attraction of the risk score method is its relative ease of application.
Reporting clinical trials from the viewpoint of a patient's choice of treatment
Authors: Hilden Jørgen 
Published: April 2007

AbstractThose who report a clinical trial should acknowledge the right of the ‘consumer’ to make decisions based on his own valuation of the beneficial and adverse effects which rival treatments may have. Suppose a new patient is inclined to trade one unit of benefit for c units of complication. Then he should (should not) be given the treatment if his estimated utility gain, χ1 ‐cχ2, is positive (negative) and statistically significant according to the data of the trial: here χ1 (χ2) denotes the observed average benefit (complication level). If the estimated gain is not statistically significant, the data do not allow any firm recommendation. This c‐dependent recommendation in general cannot be determined from inspection of a joint confidence region for the two means concerned. Therefore investigators should present the outcome of the significance test as a function of c (inverted inference). Typically there are several types of adverse effect or benefit, in which case the quantity c must be generalized into a vector of personal relative utility weights.
Models of the healthy worker effect in industrial cohorts
Authors: Bell C. M. J. , Coleman D. A. 
Published: April 2007

AbstractThe patterns of standardized mortality ratios (SMRs) with duration of follow‐up and duration of service which are characteristic of the healthy worker effect in occupational cohort studies have been simulated using models of selection and survivor effects. The models support current theories about the population dynamics which underlie the selection effect but highlight inadequacies in our understanding of the survivor effect. Several possible models were tested and methods of generating appropriate data are discussed. It is important that the healthy worker effect should be better understood as it can present so large a problem in the interpretation of occupational risk estimates.
The use of lung function tests in identifying factors that affect lung growth and aging
Authors: Buist A. Sonia , Vollmer William M. 
Published: April 2007

AbstractLung function tests are used both clinically, in assessing disease, and epidemiologically, in identifying those factors which influence the growth and aging process of the lungs. The user must beware of several common pitfalls in the use of these tests. however. First, the commonly used tests of lung function can only identify patterns of dysfunction, not specific pathologic processes. Second, these tests are subject to many sources of intra‐ and inter‐subject variability, making it difficult to dissect out the signal (for example, the rate of lung aging in adults) from the noise which may greatly exceed the signal. Finally, the analysis of longitudinal pulmonary function data is complicated by the alinearity of the growth and aging process, missing data, variable follow‐up times, information censoring mechanisms and covariate processes, and problems in defining abnormality.
Effects of mismodelling and mismeasuring explanatory variables on tests of their association with a response variable
Authors: Lagakos S. W. 
Published: April 2007

AbstractWe consider three commonly‐used statistical tests for assessing the association between an explanatory variable and a measured, binary, or survival‐time, response variable, and investigate the loss in efficiency from mismodelling or mismeasuring the explanatory variable. With respect to mismodelling, we examine the consequences of using an incorrect dose metameter in a test for trend, of mismodelling a continuous explanatory variable, and of discretizing a continuous explanatory variable. We also examine the consequences of classification errors for a discrete explanatory variable and of measurement errors for a continuous explanatory variable. For all three statistical tests, the asymptotic relative efficiency (ARE) corresponding to each type of mis‐specification equals the square of the correlation between the correct and fitted form of the explanatory variable. This result is evaluated numerically for the different types of misspecification to provide insight into the selection of tests, the interpretation of results, and the design of studies where the ‘correct’ explanatory variable cannot be measured exactly.
Missing data in longitudinal studies
Authors: Laird Nan M. 
Published: April 2007

AbstractWhen observations are made repeatedly over time on the same experimental units, unbalanced patterns of observations are a common occurrence. This complication makes standard analyses more difficult or inappropriate to implement, means loss of efficiency, and may introduce bias into the results as well. Some possible approaches to dealing with missing data include complete case analyses, univariate analyses with adjustments for variance estimates, two‐step analyses, and likelihood based approaches. Likelihood approaches can be further categorized as to whether or not an explicit model is introduced for the nonresponse mechanism. This paper will review the use of likelihood based analyses for longitudinal data with missing responses, both from the point of view of ease of implementation and appropriateness in view of the non‐response mechanism. Models for both measured and dichotomous outcome data will be discussed. The appropriateness of some non‐likelihood based analyses is briefly considered.
Estimating hidden morbidity via its effect on mortality and disability
Authors: Woodbury Max A. , Manton Kenneth G. , Yashin Anatoli I. 
Published: April 2007

AbstractThe applicability of the theory of partially observed finite‐state Markov processes to the study of disease. morbidity, and disability is explored. A method is developed for the continuous updating of parameter estimates over time in longitudinal studies analogous to Kalman filtering in continuous valued continuous time stochastic processes. It builds on a model of filtering of incompletely observed finite‐state Markov processes subject to mortality due to Yashin et al. The method of estimation is based on maximum likelihood theory and the incompleteness in the observation of the process is dealt with by applying missing information principles in maximum likelihood estimation.
Sample size and power for case‐control studies when exposures are continuous
Authors: Lubin Jay H. , Gail Mitchell H. , Ershow Abby G. 
Published: April 2007

AbstractIn estimating the sample size for a case‐control study, epidemiologic texts present formulae that require a binary exposure of interest. Frequently, however, important exposures are continuous and dichotomization may result in a ‘not exposed’ category that has little practical meaning. In addition, if risks vary monotonically with exposure, then dichotomization will obscure risk effects and require a greater number of subjects to detect differences in the exposure distributions among cases and controls. Starting from the usual score statistic to detect differences in exposure, this paper develops sample size formulae for case‐control studies with arbitrary exposure distributions; this includes both continuous and dichotomous exposure measurements as special cases. The score statistic is appropriate for general differentiable models for the relative odds, and, in particular, for the two forms commonly used in prospective disease occurrence models: (1) the odds of disease increase linearly with exposure; or (2) the odds increase exponentially with exposure. Under these two models we illustrate calculation of sample sizes for a hypothetical case‐control study of lung cancer among non‐smokers who are exposed to radon decay products at home.
Assessing diagnostic reliability and estimating incidence rates associated with a strictly progressive disease: Dental caries
Authors: Espeland Mark A. , Murphy William C. , Leverett Dennis H. 
Published: April 2007

AbstractThis paper presents a general approach for simultaneously assessing, from serial data, diagnostic consistency, interrater reliability and incidence of a strictly progressive disease. Observed data are viewed as incomplete: diagnostic errors are not distinguished from true diagnoses. We introduce a broad class of models to separate rater errors from underlying patterns of disease incidence. The analysis can include covariates and risk factors. We provide variance expressions for parameter estimates. Categorical data for estimating the incidence of dental caries serve as an example.
The probability that long‐term mean diastolic blood pressure is extreme: Tables for black individuals
Authors: Wilson P. David , Hebel J. Richard 
Published: April 2007

AbstractWith use of a large data base and a statistical model for diagnosis developed earlier, we have constructed the among‐individual conditional distribution of the long‐term mean diastolic blood pressure, md, of an individual, given the means of several measurements of systolic and diastolic blood pressure on the individual. Using this conditional distribution we provide tables and a simple algorithm for computation of the conditional probability that md for an individual exceeds a specified value (for example, 90 mmHg), given the measured values of systolic and diastolic pressure.
A note on sample size computation for testing interactions
Authors: Lachenbruch Peter A. 
Published: April 2007

AbstractThis paper discusses a simple method to compute sample sizes for testing interactions in analysis of variance. It uses orthogonal contrasts to determine the power. For the 2 × 2 table this gives a single degree of freedom contrast for consideration. For larger designs, one must consider a set of contrasts. The use of Bonferroni or Scheffé critical values is discussed. This method allows the user to compute easily a sample size without recourse to the non‐central F distribution.
The analysis of survival data from a central cancer registry with passive follow‐up
Authors: Tallis G. M. , Leppard P. , O'Neill T. J. 
Published: April 2007

AbstractWe present analyses for survival data obtained from a central cancer registry with passive follow‐up. This method of data collection has the potential to produce unknown random losses which would affect estimates of survival. We show that non‐parametrically estimated conditional distributions remove any effect of these unknown losses and that a compound mixture model estimates their magnitude. Lung cancer data are used to illustrate the procedures.
Planning the size of survival time clinical trials with allowance for stratification
Authors: Moussa Mohamed A. A. 
Published: April 2007

AbstractApproximate allocation designs for comparing two survival distributions are considered and compared with the exact solution. The influence of incorporating covariates by stratification, accrual and follow‐up duration on trial size and power is investigated. The Bernstein and Lagakos design is suited for exponential survival due to its generality and allowance for stratification while the Freedman design is convenient for distribution‐free survival in view of its simplicity. With other specifications fixed, the saving in sample size becomes minimal as the accrual period or post‐accrual follow‐up time units expand above three. Required sample size also increases dramatically when the number of strata exceeds three.
The hidden effect of time
Authors: Altman Douglas G. , Royston J. Patrick 
Published: April 2007

AbstractIt is customary to regard datasets as homogeneous with respect to the order of collection of the measurements. Examples are given in which this assumption is breached. Hidden time trends have implications for the design of studies, their analysis and interpretation. It is suggested that, if the order of observations is known, a plot by time should be performed, perhaps using a cusum.
A new method of estimation of interobserver variation and its application to the radiological assessment of osteoarthrosis in hip joints
Authors: Kjærsgaard‐Andersen Per , Christensen Finn , Schmidt Steen Asmus , Pedersen Niels Wisbech , Jøensen Bent 
Published: April 2007

AbstractFour frequently referenced radiological methods for assessment of osteoarthrosis in hip joints are compared with respect to their interobserver variation. One hundred hip joints were rated by four observers. Each observer applied all four methods to all the radiographs. The results were analysed by a new statistical method, which allowed detailed assessment of the nature and extent of interobserver variation. Significant differences in terms of intraobserver disagreement were found, and Heripret's method was the best in this respect. All four methods showed a significant degree of systematic disagreement among the observers. This suggests that the interobserver variation might be reduced by confronting the observers with the results of the statistical analysis of their judgements.
Modelling the liver function data in diabetes
Authors: Chen T. Timothy 
Published: April 2007

AbstractWe have enrolled diabetic patients into a long‐term follow‐up study that entails visits to the eye clinic every six months at which time use of a classification scheme determines whether or not the patient is normal regarding liver function. Since the study is still in progress, not all patients have complete follow‐up data. We fit models of complete symmetry to the multi‐visit binary data with supplemental margins and also investigate the data as stationary Markov chains with testing for stationarity and determination of the order of chain. The final model, a synthesis of both approaches, has potential use in future clinical trials of new drugs for diabetic patients.
Longitudinal versus cross‐sectional estimation of lung function decline–further insights
Authors: Vollmer William M. , Johnson Larry R. , McCamant Lynn E. , Buist A. Sonia 
Published: April 2007

AbstractThis paper explores the extent to which differences in longitudinal versus cross‐sectional inference may be influenced by the choice of statistical models. Using lung function data on 524 working men, we first compare the goodness‐of‐fit and implication for longitudinal decline of a variety of cross‐sectional models. We then compare the predicted longitudinal patterns from these models with those observed over a period of four years. In general, both approaches provide qualitatively, if not quantitatively, similar messages concerning the relative effects of smoking and age on lung function decline. Nonetheless, we acknowledge the existence of real selection and cohort effects. Although we recognize the utility of cross‐sectional designs, we discourage quantitative comparisons between studies, especially longitudinal versus cross‐sectional.
‘Optimal’ designs for drug, neurotransmitter and hormone receptor assays
Authors: Dunn G. 
Published: April 2007

AbstractThe present paper reports the use of computer simulations of ligand–receptor interactions to evaluate the robustness of D‐optimal designs for receptor assays when these designs are based on unrealistic models for ligand binding. Particular attention is paid to the assumed distribution of the measurement errors together with complications caused by ‘non‐specific’ binding of a radioligand to a tissue or cell preparation. It is the latter factor which suggests that an optimal design for estimation of the parameters of a simple hyperbolic dose–response curve is nowhere near to being optimal in practice.
Identifying prognostic factors in binary outcome data: An application using liver function tests and age to predict liver metastases
Authors: Makuch Robert W. , Rosenberg Philip S. , Mulshine James 
Published: April 2007

AbstractA frequent issue confronting the medical scientist is the detection of important prognostic variables, or covariates, that affect the outcome variable. In this paper we propose specific guidelines for the analysis of binary outcome data based on recent developments in statistical methodology and on less formal graphical techniques. By incorporating these methods into the process of fitting the standard logistic regression model, one can assess covariate selection and model specification more thoroughly and obtain a more balanced view of a model's predictive capabilities. We apply the methods to the evaluation of non‐invasive liver function tests and age to predict the presence of liver metastases in patients with small cell lung cancer.
Prediction of outcome of anaesthesia in patients over 40 years: A multifactorial risk index
Authors: Tiret L. , Hatton F. , Desmonts J. M. , Vourc'h G. 
Published: April 2007

AbstractThe prediction of outcome of anaesthesia in patients over 40 years of age was assessed using a multifactorial index based on current preoperative factors recorded prospectively. The study was conducted using a representative sample of anaesthetizations (except for cardiac surgery) including 517 cases with major complication (occurring during or within 24 hours of anaesthesia) and a one in fifty random sample comprising 1538 cases without complication. A split sample approach was adopted and a logistic regression model was applied to two subsets of similar size. Four preoperative factors were significantly associated with the occurrence of complications: ASA physical status, age, surgical procedure (major/minor) and type (elective/emergency). Goodness‐of‐fit of the model was assessed using another sample of 332 cases with complication and a different subset of 987 cases without complication. The model fitted the data well (p = 0.15).
Latent variable models for the analysis of medical data with repeated measures of binary variables
Authors: Blackwood Larry 
Published: April 2007

AbstractConsideration of within‐subject dependencies is a key issue in modelling binary repeated measures medical data. Borrowing from recent developments in sociology and psychology, we demonstrate the applicability of a latent variable approach to the analysis of such data. In particular we present the Rasch model as a basic model for representing the relationship of subject and treatment parameters. The latent variable approach is useful in providing a theoretical framework for specifying dependencies exactly and also as a base for considering more complicated relationships between repeated measures variables.
Identifying important results from multiple statistical tests
Authors: Parker R. A. , Rothenberg R. B. 
Published: April 2007

AbstractWhen many statistical tests are performed simultaneously, the overall chance of a type I error (incorrect rejection of a true null hypothesis) can substantially exceed the nominal error rate used in each individual test. Numerous techniques exist to adjust results of individual tests to control this problem. In general, these techniques apply a more stringent criterion of statistical significance (a smaller P‐value) to each individual test than normally needed to maintain the experimentwise type I error. With an analysis that seeks to identify results for further research, however, such a conservative technique may not be appropriate. We present a new approach that uses a mixture of several distributions to model the set of P‐values or of test statistics. One component models the results consistent with a failure to reject the null hypothesis, while the other distribution(s) in the mixture represent results inconsistent with the null hypothesis. These latter results may not achieve statistical significance based on a conventional P‐value. We illustrate the use of the method on national mortality data and on several data sets analysed previously.
Application of multiple time series analysis to the estimation of pneumonia and influenza mortality by age 1962–1983
Authors: Stroup Donna F. , Thacker Stephen B. , Herndon Joy L. 
Published: April 2007

AbstractWe employed multiple time series analysis to estimate the impact of influenza on mortality in different age groups, using a procedure for updating estimates as current data become available from national mortality data collected from 1962 to 1983. We compared mortality estimates that resulted from a multivariate model for epidemic forecasting with those obtained from univariate models. We found more accurate prediction of deaths from all age groups using the multivariate model. While the univariate models show an adequate fit to the data, the multivariate model often enables earlier detection of epidemics. Additionally, the multivariate approach provides insight into relationships among different age groups at different points in time. For both models, the largest excess mortality due to pneumonia and influenza during influenza epidemics occurred among those 65 years of age and older. Although multiple time series models appear useful in epidemiologic analysis, the complexity of the modelling process may limit routine application.