bluuebunny/crossref_metadata_2025_split huggingface dataset:
Channeling bias in the interpretation of drug effects
Authors: Petri H. , Urquhart J. 
Published: April 2007

AbstractChanneling is a form of allocation bias, where drugs with similar therapeutic indications are prescribed to groups of patients with prognostic differences. Claimed advantages of a new drug may channel it to patients with special pre‐existing morbidity, with the consequence that disease states can be incorrectly attributed to use of the drug. For the study of adverse drug reactions, large databases supply information on comedication and morbidity of patients. For diseases with a stepped‐are approach, the drug history of patients, as available from some databases, can show channeling of drugs to patients with markers of relatively severe disease.
Multiple imputation in health‐are databases: An overview and some applications
Authors: Rubin Donald B. , Schenker Nathaniel 
Published: April 2007

AbstractMultiple imputation for non‐response replaces each missing value by two or more plausible values. The values can be chosen to represent both uncertainty about the reasons for non‐response and uncertainty about which values to impute assuming the reasons for non‐response are known. This paper provides an overview of methods for creating and analysing multiply‐imputed data sets, and illustrates the dramatic improvements possible when using multiple rather than single imputation. A major application of multiple imputation to public‐use files from the 1970 census is discussed, and several exploratory studies related to health care that have used multiple imputation are described.
Variance components and their implications for statistical information in medical data
Authors: Orav E. J. , Louis T. A. , Palmer R. H. , Wright E. A. 
Published: April 2007

AbstractWe discuss general issues concerning the design and analysis of medical experiments involving repeated measures or hierarchical groupings of subjects within larger study units. Depending on the types of questions being investigated, the correlations induced by clustering can have dramatic impact on the effective sample size. The unique aspects of such experiments must be accounted for during analysis and during interpretation of the results. We illustrate these issues by using a variance components model to investigate the role of leadership in medical practice.
The effects of transformations and preliminary tests for non‐linearity in regression
Authors: Grambsch Patricia M. , O'Brien Peter C. 
Published: April 2007

AbstractNon‐linear relationships between two variables are often detected as a result of a preliminary statistical test for linearity. Common approaches to dealing with non‐linearity are to (a) make a linearizing transformation in the independent variable or (b) fit a relationship that is non‐linear in the independent variable, such as including a quadratic term. With either approach, the resulting test for association between the two variables can have an inflated type I error. We consider testing the significance of the quadratic term in a quadratic model as a preliminary test for non‐linearity. Using simulation experiments and asymptotic arguments, we quantify the type I error inflation and suggest simple modifications of standard practice to protect the size of the type I error. In the case of quadratic regression, the type I error will be increased by roughly 50 per cent. The simple strategy of appropriately correcting the α‐level is shown to have minimal loss of power if the relationship is truly linear. In the case of a linearizing transformation, the impact on the type I error will depend on the values of the independent variable and on the set of potential linearizing transformations considered. Simulation results suggest that a procedure which adjusts the test statistic according to the results of the preliminary test may offer adequate protection.
Comparison of different maximum likelihood estimators in a small sample logistic regression with two independent binary variables
Authors: Whaley Fredrick S. 
Published: April 2007

AbstractIn order to examine the bias of the estimate of the log odds ratio in a 2 × 2 contingency table, Walter computed the entire distribution of the estimated log odds ratio using various small sample sizes. This is equivalent to computing the distribution of the estimated parameter b1 in a logistic regression with one independent binary variable. In this paper, the distributions of the estimated parameters b1 and b2 for two independent binary variables are computed for some small sample logistic regressions using six different estimation methods based on maximum likelihood. These estimates are then compared to the true parameter values. The best estimation method depends on the frequency of the outcome of interest and on whether the bias or mean square error is considered more important.
A comparison of two simple hazard ratio estimators based on the logrank test
Authors: Berry G. , Kitchin R. M. , Mock P. A. 
Published: April 2007

AbstractTwo hazard ratio estimators based on the logrank test are investigated using a simulation study. The Pike estimator (ratio of relative death rates) was shown to be consistently less biased than the Peto (1‐step) estimator. The latter has recently been advocated as the method of choice for point and interval estimation.8Both estimators exhibited bias with increasing hazard ratios, although the bias was minimal for effects less than 3. The confidence intervals also did not achieve the nominal coverage with increasing hazard ratios, but again the Pike estimator was superior. The coverage could be improved by recalculation of the variance incorporating the point estimate. For a hazard ratio of less than 3 we recommend the use of the Pike estimator, otherwise it is necessary to use a more complex method of estimation.
Misclassification among methods used for multiple group discrimination‐the effects of distributional properties
Authors: Barön Anna E. 
Published: April 2007

AbstractMethods of multiple group discriminant analysis have not been fully studied with respect to classification into more than two populations when the covariate distributions are normal or non‐normal. The present study examines the classification performance of several multiple discrimination methods under a variety of simulated continuous normal and non‐normal covariate distributions. The methods include polychotomous logistic regression, multiple group linear discriminant analysis, kernel density estimation, and rank transformations of the data as input into the linear function. The parameters of interest were distance among populations, configuration of population mean vectors (collinear or forming the vertices of a regular simplex), skewness, kurtosis and bimodality. Simulation of the last three parameters was by log‐normal, sinh−1normal and a two‐component mixture of normal distributions, respectively. Results with three trivariate populations show that for all distributions, logistic discrimination classifies close to the optimal under Neyman‐Pearson allocation. These results suggest that logistic discrimination is preferable to other widely‐used methods for multiple group classification with non‐normal data, and is comparable to classification by multiple linear discrimination with normal data.
The standard error of Cohen's Kappa
Authors: Garner J. Barry 
Published: April 2007

AbstractThis paper gives a standard error for Cohen's Kappa, conditional on the margins of the observed r × r table. An explicit formula is given for the 2 × 2 table, and a procedure for the more general situation. A parsimonious log‐linear model is suggested for the general case and an approximate confidence interval for kappa is based on that model. Numerical illustrations are given comparing the standard results with exact conditional results and those of the present paper. The conclusions are, first, that the standard error of kappa under the null hypothesis (that kappa is zero) should not be used except when the null is plausible; secondly, that the usual formula for the standard error appears appropriate except in the unusual circumstance of kappa taking large negative values; and, thirdly, in small samples the distribution of the estimate of kappa appears very non‐symmetric, and it is preferable to base confidence intervals on transformations of kappa. A PC program with source code implementing the suggested procedures is available from the author.
Planning controlled clinical trials on the basis of descriptive data analysis
Authors: Abt Klaus 
Published: April 2007

AbstractIn controlled clinical trials the problem of multiplicity of desired inferential statements finds attention at an increasing rate. In this paper the previously proposed concept of Descriptive Data Analysis (DDA), situated between Confirmatory and Exploratory Data Analysis, is applied to the planning aspects of controlled trials for which the problem of multiplicity exists. The (non‐Bayesian) DDA planning concept should provide the investigator with tools to draw final conclusions from data of several variables possibly observed at several time points in possibly several groups of subjects by combining his pre‐trial medical experience with descriptive inferential statements (confidence intervals and test results) at nominal significance levels. DDA also provides for confirmatory statements concerning individual null hypotheses and partially global hypotheses.
Determination of optimal designs using linear models in crossover trials
Authors: Lasserre Virginie 
Published: April 2007

AbstractThis study determines the optimal designs for crossover trials with fixed size of population, in order to estimate contrasts between two treatment effects, for various numbers of periods. The problem is approached in the framework of a linear model, taking into consideration the structure of a variance matrix constructed from two random sources: linked‐to‐subject random effect and linked‐to‐measurement random error. Different models, as well as different optimality criteria, are considered. For example, when two periods are planned, for the criterion of the minimization of the variance of the treatment‐effect estimator, in the presence of residual effect, the optimal design corresponds to four groups of sequences, (T1, T2), (T2, T1), (T1, T1), (T2, T2); without residual effect it corresponds to two groups, (T1, T2), (T2, T1). Optimal designs are given for the comparison of two treatments when using three or four periods.
Some extensions to a new approach for interim analysis in clinical trials
Authors: Falissard Bruno , Lellouch Joseph 
Published: April 2007

AbstractThere already exists many methods for group sequential analysis of clinical trials. A new approach1 suggests to preserve the overall type I error α by requiring that a succession of interim analyses be significant at the α level. In this paper, this procedure is extended. A rule for early acceptance of H0 is presented, with an extension for flexible number and times of analyses. A real example is given.
Analysis of phase II clinical trials in haematology and oncology: Comparison of the triangular test to the usual methods
Authors: Benichou Jacques , Bellissant Eric , Chastang Claude 
Published: April 2007

AbstractPhase II cancer clinical trials are non‐comparative trials which are designed to determine whether the response rate p to the treatment under study is greater than a certain value p0, that is, to test H0, given by p ⩽ p0 against H1 given by p > p0.By choosing type I error α and the power 1 ‐ β and by specifying H1, (that is, by choosing a clinically relevant improvement p1), one can compute the number of patients N to be included for a fixed‐sample approach. Various other approaches have been proposed such as multistage methods and Wald's continuous sequential probability ratio test (SPRT).As an alternative approach, we extended the triangular test (TT), proposed by Whitehead for comparative trials, to the situation of non‐comparative trials with a binary outcome. We expressed H0 and H1 in terms of the log odds‐ratio statistics, namely log {p (1 ‐ p0)/p0 (1 ‐ p)}. With this choice, the two statistics of interest, Z and V, have simple expressions: Z is the difference between the observed number of positive outcomes and the expected number under H0 and V is the variance of Z under H0. After every group of n patients, Z is plotted against V, and the trial proceeds until a boundary is crossed.In our simulations, type I error α and the power 1 ‐ β were close to nominal values with the TT and the average sample size was close to Wald's continuous SPRT and compared favourably with the multistage methods proposed by Herson and Fleming.Given its statistical properties and its easy use, the TT should be considered for planning and analysing cancer phase II trials.
Floating absolute risk: An alternative to relative risk in survival and case‐control analysis avoiding an arbitrary reference group
Authors: Easton D. F. , Peto J. , Babiker A. G. A. G. 
Published: April 2007

AbstractWe discuss the problem of describing multiple group comparisons in survival analysis using the Cox model, and in matched case‐control studies. The standard method of comparing the risk in each group with a baseline group is unsatisfactory because the standard errors and confidence limits relate to correlated parameters, all dependent on precision within the baseline group. We describe the construction of standard errors for the parameters of all groups, without the need to select a baseline group. These standard errors can be regarded as relating to roughly independent parameters, so that groups can be compared efficiently without knowledge of the covariances. The method should assist in graphical presentation of relative risks, and in the combination of results from published studies. Two examples are presented.
Assessing the influence of reversible disease indicators on survival
Authors: Andersen Per Kragh , Hansen Lars Sommer , Keiding Niels 
Published: April 2007

AbstractIn a randomized clinical trial of the effect of prednisone versus placebo on survival in patients with liver cirrhosis, follow‐up visits were scheduled after 3,6 and 12 months of treatment and thereafter once a year. At each follow‐up theprothrombin index, a measure of liver function, was recorded and scored as either low or normal. The interaction between treatment and prothrombin index was analysed using a three‐state illness‐death model with recovery. The continuous‐time Markov process model with constant or piecewise constant intensities suggested by Kay allows inference to proceed even though the status of the disease indicator, here the prothrombin index, is only known at the time of each visit and not between visits. We compare the analysis with the theoretically incorrect, but practically rather common approximation, where the status of the disease indicator is assumed to remain constant from one visit until just before the next. Under this approximation both standard parametric methods and non‐parametric approaches developed by Aalen and Johansen are available.
Generalized Norton‐Simon models of tumour growth
Authors: Heitjan Daniel F. 
Published: April 2007

AbstractThis paper considers the analysis of serial data on the growth of tumours in laboratory rodents. I propose a model ‐ a generalization of the tumour growth model of Norton and Simon ‐ that leads to a rich family of growth and decay curves. The model assumes that unperturbed growth follows the generalized logistic form; it accommodates time‐varying treatment effects through an effective dose function. I fit two such models to data on a human prostate tumour growing in nude mice and compare the fitted curves and dose functions with a non‐parametric curve and dose function estimated from a cubic spline model. All three models account for both random animal effects and autocorrelation. Monte Carlo results suggest that (a) maximum likelihood estimates of growth parameters are biased, although not severely, and (b) standard errors are conservative in small samples but become increasingly accurate in larger samples.
Approximate multinormal probabilities applied to correlated multiple endpoints in clinical trials
Authors: James Susan 
Published: April 2007

AbstractClinical trials with multiple endpoints incur increased familywise type I errors. The Bonferroni correction is a common method used to modify the p‐values to account for multiple significance testing. For independent endpoints the Bonferroni method is slightly conservative whereas with high correlation the conservatism is extreme, as demonstrated by Pocock et al. This paper presents a procedure which allows for the correlation present, whilst adjusting the multiple p‐values. The method is based on an approximation derived for multinormal probabilities.
Simple tests of homogeneity of controls in matched studies
Authors: Gart John J. 
Published: April 2007

AbstractConsider matched case‐control studies with a pair of distinguishable controls, say neighbourhood and hospital, wherein one of the controls may be missing. Liang and Stewart derive an efficient test for the possible difference in controls for the complete data case. They suggest inefficient tests for the situation where some triplets are incomplete. Levin, and Risch and Tibshirani, derive efficient tests for the incomplete triplet case by the methods of maximum likelihood estimator (Wald) tests and likelihood ratio tests, respectively. All these tests require the use of compute algorithms. Using score theory, we derive a simple efficient test for complete triplets which only requires solving a quadratic equation. Furthermore, we show that the usual normal approximation to McNemar's test is equivalent to the score test and is thus fully efficient as well. For incomplete triplets, score theory leads to a fully efficient test which only requires the solution of a cubic equation. We propose a nearly efficient test which is even more simply computed. The example of Liang and Stewart illustrates application of the results.
Three measures for simultaneously evaluating benefits and risks using categorical data from clinical trials
Authors: Chuang‐Stein Christy , Mohberg Noel R. , Sinkula Michaline S. 
Published: April 2007

AbstractRandomized clinical trials are typically conducted to compare the efficacy (benefits) and side effects (risks) of two or more treatments. One can use results from such trials to decide on a preferable treatment that reflects one's own evaluation of the benefits and risks. To facilitate the necessary decision making, we propose in this paper three measures for simultaneously assessing benefits and risks. All three measures use weights that reflect the relative importance of the various treatment outcomes to an individual. Two of them carry the flavour of benefit/risk ratios, while the third generalizes Hilden's measure which incorporates patients' preferences. The proposed measures and procedures are illustrated using data from a phase III clinical trial of antihypertensive compounds.
A comparison of methods for calculating a stratified kappa
Authors: Barlow William , Lai Mei‐Ying , Azen Stanley P. 
Published: April 2007

AbstractInvestigators use the kappa coefficient to measure chance‐corrected agreement among observers in the classification of subjects into nominal categories. The marginal probability of classification may depend, however, on one or more confounding variables. We consider assessment of interrater agreement with subjects grouped into strata on the basis of these confounders. We assume overall agreement across strata is constant and consider a stratified index of agreement, or ‘stratified kappa’, based on weighted summations of the individual kappas. We use three weighting schemes: (1) equal weighting; (2) weighting by the size of the table; and (3) weighting by the inverse of the variance. In a simulation study we compare these methods under differing probability structures and differing sample sizes for the tables. We find weighting by sample size moderately efficient under most conditions. We illustrate the techniques by assessing agreement between surgeons and graders of fundus photographs with respect to retinal characteristics, with stratification by initial severity of the disease.
The environmental accident at ‘Schweizerhalle’ and respiratory diseases in children: A time series analysis
Authors: Helfenstein Ulrich , Ackermann‐Liebrich Ursula , Braun‐Fahrländer Charlotte , Wanner Hans Urs 
Published: April 2007

AbstractDuring an investigation concerned with the relationship between air pollution and respiratory diseases in children, the ‘Schweizerhalle’ accident occurred when unknown amounts of pollutants were discharged into the environment. In that investigation, two series of medical data were collected during one year: (a) The daily relative number of preschool children, exhibiting diseases of the respiratory tract, who either came to the outpatients' clinic of the Children's Hospital or were reported by paediatricians in Basle; (b) The daily number of respiratory symptoms per child, observed in a group of randomly selected preschool children. The purpose of the present time series analysis is the assessment of possible change in these series after the environmental accident. The nature of the change is studied by complementary approaches. First, a forecast arising from models identified in the preaccident period is compared with the actual data. Thereafter, intervention models which adequately and parsimoniously represent the change are identified. Finally, an identification of a change‐point is performed.
A time series construction of an alert threshold with application to S. Bovismorbificans in France
Authors: Watier Laurence , Richardson Sylvia , Hubert Bruno 
Published: April 2007

AbstractEpidemiological surveillance can be used to initiate preventive measures and Salmonella infection is a prime example of application. It is important that any unusual increase of reported cases should be detected as rapidly as possible. Reliable forecasts based on a suitable model of the temporal structure of a pathological indicator are necessary for the elaboration of an alert threshold. We first discuss the specific problems associated with the identification and the estimation of the temporal structure of indicators of infectious diseases when they present both epidemic peaks and an underlying stationary structure. Then we consider how an alert threshold can be defined. Our application concerns infections caused by S. bovismorbificans in France for which an alert threshold is constructed and tested both retrospectively and prospectively.
On the estimation of total variability in assay validation
Authors: Chow Shein‐Chung , Tse Siu‐Keung 
Published: April 2007

AbstractIn the pharmaceutical industry, an assay method is considered validated if the accuracy and precision for an assay meet some acceptable limits. This paper discusses the assessment of assay precision in terms of the estimation of total variability of an assay from a one‐way random effects model which is often considered in assay validation. We propose a general class of estimators that includes the analysis of variance estimator and the maximum likelihood estimator. We derive the optimal estimator, in terms of smallest mean squared error, within this class and consider an approximate version of this optimal estimator. We report on a Monte Carlo simulation to study its finite sample performance. We also present two examples to illustrate the use of the proposed methodology.