bluuebunny/crossref_metadata_2025_split huggingface dataset:
Confidence intervals, hypothesis tests, and sample sizes for the prevented fraction in cross‐sectional studies
Authors: Gargiullo Paul M. , Rothenberg Richard B. , Wilson Hoyt G. 
Published: April 2007

AbstractThe prevented fraction (PF) is the proportion of disease occurrence in a population averted due to a protective risk factor or public health intervention. The PF is not equivalent to the population attributable risk (AR). The AR is appropriate for epidemiologic studies of disease etiology, and for estimating the potential impact of modifying risk factor prevalence. The PF more directly measures the impact of public health interventions, however, and thus is an important evaluation tool. We derived the variance of the estimated PF by using maximum likelihood theory for cross‐sectional studies. We used simulations to compare the performance of confidence intervals based on various transformations of the estimated PF. The logit transformation was the best choice when PF ≥ 0·3, whereas the untransformed estimate was best when PF < 0·3. We present formulae for hypothesis testing and sample size calculations, discuss the issues of interaction and confounding and give two estimators adjusted for confounding.
Statistical issues in the regulation of medicines
Authors: Lewis John A. 
Published: April 2007

AbstractThe recent release within the European Union of a draft Note for Guidance from the Commission for Proprietary Medical Products (CPMP) entitled ‘Biostatistical methodology in clinical trials in applications for marketing authorisations for medicinal products’ has stimulated widespread discussion of the relevant statistical issues. This discussion contains echoes of an earlier debate concerning a closely related U.S. Guideline. The areas of agreement and disagreement between the two guidelines bear close examination as they provide lessons for all statisticians involved in the design and analysis of clinical trials. Notable areas of debate include crossover designs, multi‐centre trials, the intention to treat principle, pre‐specification of statistical analysis and the ability to generalize results to the wider population of patients. The consequences for the routine involvement of statisticians in this area of work in general, and in the regulatory process in particular, are espicially important. This is an illustration of the extent to which drug regulation has influenced, and is continuing to influence, the development of medical statistics throughout the world.
Comparison of additive and multiplicative models for reproductive risk factors and post‐menopausal breast cancer
Authors: Lund Eiliv 
Published: April 2007

AbstractThe mathematical multistage model of carcinogenesis proposed by Breslow and Day indicates that if hormonal risk factors for breast cancer are late‐stage carcinogens then the use of an additive model (excess absolute risk) should be preferrable to the present standard use of the multiplicative model (excess relative risk). To test different models, information from a large prospective study of 431,604 women aged 45–74 years in 1970 with follow‐up to 1985 on reproductive factors and post‐menopausal breast cancer mortality was used to compute goodness‐of‐fit statistics. The relative risk function, ranging from multiplicative to additive, was explored by changing the exponent in a power transformation. The analysis found evidence of better fit by the additive compared with the multiplicative models, consistent with the proposed mathematical model.
Modelling covariate effects in observer agreement studies: The case of nominal scale agreement
Authors: Graham Patrick 
Published: April 2007

AbstractCorrection for chance‐expected agreement has become an accepted technique in the analysis of observer agreement data and may be particularly useful when the level of agreement achieved in different populations is compared. However, formal methods for making comparisons of chance‐corrected agreement or, more generally, for studying the effects of covariates on chance‐corrected agreement have not received much attention. For nominal scale agreement data we show how Tanner and Young's model for observer agreement can be applied to this problem. The models discussed can be fitted using existing software and certain model parameters have interpretations in terms of positive and negative agreement odds ratios. The proposed methodology facilitates investigation of issues such as confounding of covariate effects and interaction between covariates in their effect on chance‐corrected agreement. The methods outlined therefore allow observer agreement data to be analyzed in a manner strongly analogous to the logistic modelling of the association between disease and suspected risk factors. The methods are illustrated using data on the comparability of primary and proxy respondent reports of the primary respondents participation in physically vigorous leisure time activity.
Bayesian sequential monitoring designs for single‐arm clinical trials with multiple outcomes
Authors: Thall Peter F. , Simon Richard M. , Estey Elihu H. 
Published: April 2007

AbstractWe present a Bayesian approach for monitoring multiple outcomes in single‐arm clinical trials. Each patient's response may include both adverse events and efficacy outcomes, possibly occurring at different study times. We use a Dirichlet‐multinomial model to accommodate general discrete multivariate responses. We present Bayesian decision criteria and monitoring boundaries for early termination of studies with unacceptably high rates of adverse outcomes or with low rates of desirable outcomes. Each stopping rule is constructed either to maintain equivalence or to achieve a specified level of improvement of a particular event rate for the experimental treatment, compared with that of standard therapy. We avoid explicit specification of costs and a loss function. We evaluate the joint behaviour of the multiple decision rules using frequentist criteria. One chooses a design by considering several parameterizations under relevant fixed values of the multiple outcome probability vector. Applications include trials where response is the cross‐product of multiple simultaneous binary outcomes, and hierarchical structures that reflect successive stages of treatment response, disease progression and survival. We illustrate the approach with a variety of single‐arm cancer trials, including bio‐chemotherapy acute leukaemia trials, bone marrow transplantation trials, and an anti‐infection trial. The number of elementary patient outcomes in each of these trials varies from three to seven, with as many as four monitoring boundaries running simultaneously. We provide general guidelines for eliciting and parameterizing Dirichlet priors and for specifying design parameters.
A Bayesian group sequential design for a multiple arm randomized clinical trial
Authors: Rosner Gary L. , Berry Donald A. 
Published: April 2007

AbstractGroup sequential designs for randomized clinical trials allow analyses of accruing data. Most group sequential designs in the literature concern the comparison of two treatments and maintain an overall prespecified type I error. As the number of treatments increases, however, so does the probability of falsely rejecting the null hypothesis. Bayesian statisticians concern themselves with the observed data and abide by the likelihood principle. As long as previous analyses do not change the likelihood, these analyses do not change Bayesian inference. In this paper, we discuss a group sequential design for a proposed randomized clinical trial comparing four treatment regimens. Bayesian ideas underlie the design and posterior probability calculations determine the criteria for stopping accrual to one or more of the treatments. We use computer simulation to estimate the frequentist properties of the design, information of interest to many of our collaborators. We show that relatively simple posterior probability calculations, along with simulations to calculate power under alternative hypotheses, can produce appealing designs for randomized clinical trials.
Linking public health data using geographic information system techniques: Alaskan community characteristics and infant mortality
Authors: Andes Nancy , Davis Jack E. 
Published: April 2007

AbstractThis article identifies geographical contexts important in differentiating infant mortality in Alaska and uses spatial processing models to link and analyse multi‐source data. Information characterizing geographical locations are collected from Alaska's vital statistics for the years 1982–91 and the 1990 Census. Geographic information system (GIS) techniques are applied to identify spatially homogeneous regions, assess spatial compatibility across databases, and allocate geographical units across boundaries. A primary goal of this paper is to encourage spatial linkage and analysis techniques for vital statistics and census data. By demonstrating the interplay of tabular, graphical, and mapping techniques on Alaskan infant mortality, this analysis describes procedures for conducting epidemiological research with data spatially defined at distinct geographical levels.
Evaluating the quality of anonymous record linkage using deterministic procedures with the New York state aids registry and a hospital discharge file
Authors: Muse Alison G. , Mikl Jaromir , Smith Perry F. 
Published: April 2007

AbstractLinkage of same‐person records across multiple databases relies on high‐quality, uniformly available identifying information. These data quality issues become increasingly important when personal names are not available for record linkage. Using deterministic decision criteria, we linked records from two population‐based files in the absence of personal names. The sensitivity of anonymous record linkage procedures ranged from 32 to 85 per cent for the two years studied, and the positive predictive value (PPV) ranged from 14 to 99 per cent. Decreasing sensitivity and PPV were primarily attributed to (1) errors in computerized identifying information and (2) the deterministic decision criteria specified for record linkage. An evaluation of the contribution of personal names to the quality of record linkage found no measurable impact.
Verification of information in a large medical database using linkages with external databases
Authors: Shevchenko Ivan P. , Lynch John T. , Mattie Angela S. , Reed‐Fourquet Lori L. 
Published: April 2007

AbstractEfforts to utilize Uniform Hospital Discharge Data Sets (UHDDS) for epidemiological studies have been hampered by the limitations of those databases. The purpose of this paper is to illustrate that linking to external databases can provide the verification necessary to overcome many of those limitations. This method has dramatically altered study design at the Connecticut Hospital Research and Education Foundation and has provided an efficient method for specifying data collection weaknesses within the resident databases.
Multiple hypothesis tests in multiple investigations
Authors: Caudill Samuel P. , Hill Robert H. 
Published: April 2007

AbstractInferential statistical methods have traditionally been based on the assumption that one experiment is performed and that interest centres on one or more predetermined hypothesis tests. Exploratory research, on the other hand, often involves multiple hypotheses or repeated investigations under similar or different conditions or both. Several techniques have been proposed to deal with multiple or simultaneous hypothesis testing in single investigations, and procedures to combine observed significance levels for an individual hypothesis test from two or more investigations have been suggested. In this paper we propose a method for identifying important results from multiple statistical tests in multiple investigations. The method is illustrated by using high performance liquid chromatography to identify potential aetiologic contaminants in L‐tryptophan samples.
Feasibility of contour mapping epidemiological data with missing values
Authors: Nobre Flavio F. , De Macedo Marcia M. A. 
Published: April 2007

AbstractData of epidemiologic interest often occur as spatial information during each of several time periods. In most cases data are available from a set of regions or localities which can be viewed as points in a plane. Although contour mapping is useful for displaying these data, the lack of data for all data points in a region may lead to erroneous interpretation. In this paper we use simulation to investigate the impact of missing data points for contour mapping using two distinct simulated spatial—time distributions for epidemiologic variables. A model for the occurrence of malaria in localities randomly distributed in one region is chosen as the prototype for data generation.
The price and purity of cocaine: The relationship to emergency room visits and death, and to drug use among arrestees
Authors: Hyatt Raymond R. , Rhodes William 
Published: April 2007

AbstractThis paper examines the relationship of the price and purity of cocaine to emergency room visits and deaths associated with cocaine use and to cocaine use among arrestees. A model is developed for the trend in cocaine prices at the retail level during the period January 1986 to December 1991. This trend is then compared to information on emergency room visits, medical examiner reports, and the percentage of arrestees testing positive for cocaine use over the same period. The principal conclusion is that there is a significant negative relationship between the estimated street price of cocaine and the level of related medical emergencies and deaths and the number of arrestees who test positive for cocaine.
Statistical issues in assessing comorbidity
Authors: Kraemer Helena Chmura 
Published: April 2007

AbstractI define three separate and distinct types of non‐random comorbidity: epidemiologic, clinical and familial. These might exist singly or in any combination for a pair of disorders. The focus is on their definition and the measurement and interpretation of the types of comorbidity of most common concern: epidemiologic comorbidity. I discuss certain sources of epidemiologic comorbidity such as shared risk factors, or diagnostic ‘fuzziness’, and I indicate the directions of research design and analyses to disclose such sources.
Logistic regression in case‐control studies: The effect of using independent as dependent variables
Authors: Nagelkerke Nico J. D. , Moses Stephen , Plummer Francis A. , Brunham Robert C. , Fish David 
Published: April 2007

AbstractIn case‐control studies, cases are sampled separately from controls. In such studies the primary analysis concerns the estimation of the effect of covariables on being a case or a control. To explore causal pathways, further secondary analysis could concern the relationships among the covariables. In this paper the validity of such secondary analysis is addressed. In particular, the use of multiple logistic regression in case‐control studies where the dependent variable is not the case/control indicator is explored. It is shown that only under very restrictive conditions will sample regression coefficients correctly estimate their true value. In many situations, it may be valid to regress one covariable on others in the control group, but not in the case group or the combined sample. This principle is illustrated by a study of sexually transmitted disease in Kenya.
Percentile regression analysis of correlated antibody responses
Authors: Moulton Lawrence H. , Holt Elizabeth A. , Job Jayakaran S. , Halsey Neal A. 
Published: April 2007

AbstractAntibody responses following vaccination usually are analysed by comparing geometric mean concentrations across levels of relevant covariates and by comparing the proportions of vaccinees responding. In the regression setting, the analyses are done on log‐transformed concentrations, estimating geometric mean responses conditional on a vector of covariates. More detailed analyses examining the relationship of covariates to different parts of the response distribution may be performed through the application of asymmetric least squares estimation of regression percentiles. We present a method for accounting for correlation in percentile regression analyses of longitudinal antibody response data. We illustrate the procedures with measles antibody response data from Haitian children who participated in a randomized trial of high titre vaccines. The strongest dose and strain effects were seen in the low end of the antibody concentration distributions.
The log transformation is special
Authors: Keene Oliver N. 
Published: April 2007

AbstractThe logarithmic (log) transformation is a simple yet controversial step in the analysis of positive continuous data measured on an interval scale. Situations where a log transformation is indicated will be reviewed. This paper contends that the log transformation should not be classed with other transformations as it has particular advantages. Problems with using the data themselves to decide whether or not to transform will be discussed. It is recommended that log transformed analyses should frequently be preferred to untransformed analyses and that careful consideration should be given to use of a log transformation at the protocol design stage.
AN EXTENSION OF THE CONTINUAL REASSESSMENT METHODS USING A PRELIMINARY UP‐AND‐DOWN DESIGN IN A DOSE FINDING STUDY IN CANCER PATIENTS, IN ORDER TO INVESTIGATE A GREATER RANGE OF DOSES
Authors: Møller Susanne 
Published: April 2007

AbstractIn a phase I clinical trial in cancer patients, the drug involved had one known main adverse effect, which also occurs spontaneously in cancer patients with a fairly high frequency. Experiments in rats have shown marked effects of the drug on tumour growth in high doses, but also dose‐dependent toxicity. Consequently, the aim of the study was to determine a dose with a prespecified, acceptable rate of toxicity. As a traditional design could result in inaccurate conclusions, use of the continual reassessment method (CRM) was considered. Twelve dose levels were chosen, allocating to the first patient the lowest, but safe, dose. It is likely that the target dose is far above that, and that CRM then would escalate too fast, skipping certain levels. To ensure that all dose levels inferior to the target dose was tried, some combined methods were proposed: (1) an extension of the design, combining the CRM with a preliminary up‐and‐down design in order to reach the neighbourhood of the target dose during a successive escalation, and (2) a restriction on the CRM of never escalate more than a single dose level. Simulations showed the extended CRM to be superior by making it possible to investigate a greater range of doses using fewer patients, and to provide more accurate estimates.
ESTIMATING THE MINIMUM THERAPEUTICALLY EFFECTIVE DOSE OF A COMPOUND VIA REGRESSION MODELLING AND PERCENTILE ESTIMATION
Authors: Filloon Thomas G. 
Published: April 2007

AbstractEstimation of the minimum effective dose (MED) of a compound from a typical placebo‐controlled dose‐response study can be carried out in several ways. Current approaches compare treatment doses against a placebo dose via selected contrasts to determine the lowest dose level at which the contrast is statistically significant. Ruberg shows how this approach works for various choices of contrasts. Instead, the MTED (minimum therapeutically effective dose) might be defined as the lowest dose level that yields a therapeutic benefit to patients, on average, or to a given percentage of patients. One could use a lower confidence limit for the mean approach to determine the dose yielding (on average) a therapeutic benefit via regression modelling of the continuous treatment dose response. More importantly, a patient relevant approach would be to actually estimate regression percentiles or construct regression tolerance intervals. Such an approach addresses the question ‘What percentage of patients are receiving therapeutic (yet safe) doses’, instead of just knowing that a dose is effective ‘on average’, and thus would help to understand the distribution of patient responses at a given treatment dose. This further enables one to better evaluate the efficacy versus safety trade‐off as dose increases and provides better guidelines for prescribed dose levels.
USE OF THE REPEATED CROSS‐OVER DESIGNS IN ASSESSING BIOEQUIVALENCE
Authors: Liu Jen‐Pei 
Published: April 2007

AbstractWe consider applications of the repeated 2 × 2 cross‐over design to evaluating bioequivalence between the two formulations. The repeated 2 × 2 cross‐over design allows us not only to assess bioequivalence on average bioavailability and to examine the subject‐by‐formulation interaction but also to obtain independent unbiased estimates of intrasubject variability. One consequence of unequal intrasubject variabilities is that the sum of squares of intersubject residuals and the sum of squares of subject‐by‐formulation residuals are not independent. We also discuss the relative merits of this design as compared to the standard 2 × 2 cross‐over design without repeated measurements in terms of precision and sample size with respect to the ratio of the number of subjects to the repeated measurements per subject. We investigate other uses of the 2 × 2 cross‐over in examining the bioequivalence between the two different dosing regimens. Possible applications of other repeated cross‐over designs to bioequivalence for more than two formulations are explored. A numerical example illustrates the proposed procedure.
DEFINING, MONITORING AND COMBINING SAFETY INFORMATION IN CLINICAL TRIALS
Authors: Enas Gregory G. , Goldstein David J. 
Published: April 2007

AbstractAssessment of clinical trial safety data for industry, regulatory agencies, medical practitioners and patients requires definition and measurement, monitoring, and overall analysis. Prospective ‘safety’ definitions and reliable measurement tools reduce inefficient data collection and improve the validity of resulting analyses. Statistical tools can help investigators monitor safety data from controlled clinical trials and help improve post‐marketing surveillance. Also, when evaluating overall safety, one needs to assess all available information by combining information from many trials and other sources. Planning for this combined assessment, incorporating flexibility to assess unanticipated yet important nuances in individual trials, may be more important than the actual statistical analysis method used. A keen awareness of the future needs of consumers of this infomation is quite important. Some current proposals to combine safety information will be discussed.
COMPARING THE BIVARIATE EFFECTS OF TOXICITY AND EFFICACY OF TREATMENTS
Authors: Tuber‐Bitter Pascale , Bloch Daniel A. , Raynauld Jean‐Pierre 
Published: April 2007

AbstractMedical studies often involve comparing the toxicity and efficacy of drugs. Separately evaluating toxicity and efficacy, the usual practice, does not correspond to how doctors manage patients and does not use the information provided in their bivariate relationship. This paper presents methods for analysing the bivariate data. One method is based on assessing the benefit for patient values to lie in different regions of the toxicity–efficacy plane. A second method includes patient thresholds for tolerating drugs. We propose dividing the toxicity–efficacy plane into regions where patients are likely to tolerate the drug. Several statistics are defined on these regions for measuring the toxic–therapeutic relationship, and the bootstrap is proposed for estimating their variances. We illustrate with treatment information available on rheumatoid arthritis patients.