bluuebunny/crossref_metadata_2025_split huggingface dataset:
UGDPTrial
Authors: Meinert Curtis 
Published: September 2014

AbstractThe UGDP (University Group Diabetes Program) was a multicenter, randomized, placebo‐controlled trial aimed at assessing the efficacy of antidiabetic treatments in delaying or preventing the long‐term complications of adult‐onset diabetes (type‐2 diabetes). The trial was in the carly vanguard of trials aimed at assessing efficacy of approved treatments for chronic conditions—phase IV trials in the vernacular of trials. The trial involved two oral agents, tolbutamide and phenformin, and matching placebos and two insulin‐assigned treatment groups (insulin dosage as determined by body surface and insulin dosage as needed to “normalized” blood glucose levels). The trial generated a firestorm of controversy in 1970 with presentation and publication of results suggesting that a widely used oral agent—tolbutamide (Orinase, Upjohn, Kalamazoo now part of Pfizer)—was of no value in reducing the risk of morbiarty or in prolonging life. The FDA used its results to revise the label for the drug to include a special warning concerning the possibility cardiovascular risks associated with the drug, but only after a prolonged battle that raged on for more than a decade—a battle that ultimately ended up in front of the U.S. Supreme Court.
Coronary Drug Project
Authors: Canner Paul L. 
Published: September 2014

AbstractThe Coronary Drug Project, which was conducted during 1966 to 1974, was a randomized, double‐blind, placebo‐controlled trial of five lipid‐modifying agents—estrogens at two dosage levels, dextrothyroxine, clofibrate, and niacin—in 8341 men with previous myocardial infarction. The primary efficacy outcome was all‐cause mortality for the entire follow‐up period. The first three named regimens were terminated early because of adverse effects. Only niacin significantly reduced the risk of (1) cardiovascular events during a mean follow‐up of 6.2 years and (2) all‐cause mortality during 6.2 years with study treatment plus an additional 9 years of post‐trial follow‐up. Analyses of treatment–placebo differences with respect to all‐cause mortality, subdivided by level of adherence to the study prescription and by amount of change in serum cholesterol level, could not be properly interpreted because reasons for good/poor adherence and for cholesterol changes in the placebo group were not well defined.
Cox Proportional Hazard Model
Authors: Cai Jianwen , Zeng Donglin 
Published: September 2014

AbstractIn this article, we provide an overview on the Cox proportional hazards model. We discuss the partial likelihood approach for parameter estimation and inference. Some practical issues in using the Cox regression model are discussed, including how to handle tied data, incorporate time‐dependent covariates, and fit the Cox regression model in stratified population. The article also reviews some practical ways for assessing proportional hazards assumption; one authentic example is used to illustrate data analysis using the Cox regression model. Finally, we list a few extensions of the Cox regression model.
Logrank Test
Authors: Hopkins Alan 
Published: September 2014

AbstractThe nonparametric log‐rank test compares two or more independent estimated time‐to‐event curves (survival functions) based on censored data. Right‐censored data originates in clinical trials when measuring time‐to‐an‐event. Time‐to‐infection, time‐to‐disease progression, and time‐to‐death are examples of clinical endpoints that may not be observed on all subjects randomized in a clinical trial because of a fixed follow‐up period or dropout. We shall refer to time‐to‐event data with right censoring as survival data. The log‐rank test is the most commonly used test for comparing survival functions among a class of weighted log‐rank statistics. The log‐rank test is the most powerful nonparametric test to detect proportional hazards alternatives. It is closely related to the Cox regression model.
Intraclass Correlation Coefficient with Emphasis on Clinical Trials
Authors: Campbell Marion K. , Grimshaw Jeremy M. , Piaggio Gilda 
Published: September 2014

Abstract
Increasingly researchers are recognizing that in many situations, using a cluster‐randomized trial (in which groups of individuals are randomized rather than individuals directly) may be more appropriate than an individually randomized trial. Cluster‐randomized trials are more complex to design, however, primarily because observations on individuals within the same cluster tend to be correlated. The statistical measure of this correlation within clusters is known as the intraclass or intracluster correlation coefficient (ICC). In this article, we discuss the measurement and impact of the ICC on the design and analysis of cluster‐randomized trials. In particular we outline how an ICC can be calculated, the likely magnitude of ICCs in specific settings, the factors that influence the ICC estimate, how an ICC should be reported, and the impact of the ICC on sample‐size requirements and analysis.
Aligned Rank Test
Authors: McNeil Rebecca B. , Woolson Robert F. 
Published: September 2014

AbstractAligned rank tests are an intuitive rank‐based approach to the evaluation of an experimental treatment effect in blocked or stratified data. Their interblock ranking foundation yields an efficiency advantage relative to comparable tests constructed from intrablock rankings such as Friedman's test. Both exact and asymptotic tests of the null hypothesis are available with this approach. We present a general overview of the history and construction of aligned rank tests, and provide an example in selected data from a clinical trial of high‐dose vitamin D supplementation.
Nonparametric Methods
Authors: Wolfe Douglas A. 
Published: September 2014

AbstractThe motivation behind distribution‐free hypothesis tests and confidence intervals based on signs and ranks is discussed and standard text references are provided. Distribution‐free test and confidence interval procedures for the one‐sample, two‐sample, one‐way analysis of variance, two‐way analysis of variance, and independence settings are presented. Point estimation techniques, including Hodges–Lehmann estimators and contrast estimation, and multiple comparisons procedures are described. Basic references for censored data approaches and bootstrapping are provided.
Generalized Estimating Equations: Introduction
Authors: Hardin James W. , Hilbe Joseph M. 
Published: September 2014

AbstractCorrelated datasets develop when multiple observations are collected from a sampling unit (e.g., repeated measures of a bank over time, or hormone levels in a breast cancer patient over time), or from clustered data where observations are grouped based on a shared characteristic (e.g., observations on different banks grouped by zip code, or on cancer patients from a specific clinic). The generalized linear model framework for independent data is extended to model correlated data via the introduction of second‐order variance components directly into the independent data model's estimating equation. This generalization of the estimating equation from the independence model is thus referred to as a Generalized Estimating Equation (GEE). This article discusses the foundation of GEEs as well as how user‐specified correlation structures are accommodated in the model‐building process. This article also discusses the relationship and similarity to the underlying generalized linear model framework and we point out alternative approaches to GEEs for modeling correlated data such as fixed‐effects models and random‐effects models.
Over the Counter (
OTC
) Drugs
Authors: Brass Eric P. 
Published: September 2014

Abstract
Regulatory decision making and public health efforts are dependent on clinical research to define the ability of consumers to self‐manage treatment regimens with over‐the‐counter drugs. Label compression studies evaluate the ability of consumers to understand the key communication messages of a prototype drug label. Self‐selection studies assess the ability of consumers to use the label to determine whether use of the proposed drug is appropriate for them based on their individual health status. Actual use studies allow assessment of consumers' ability to decide whether to use a proposed over‐the‐counter drug, and they evaluate how consumers manage the treatment regimen. These clinical research methods pose unique challenges as they balance the need for rigorous assessments with efforts to facilitate unbiased participant decision making to understand how the drug would be used in the open marketplace without professional supervision.
Dermatology Trials
Authors: Feldman Steven R. , Clark Adele R. 
Published: September 2014

AbstractOne characteristic of many skin disease clinical trials is that they involve topical treatments. The direct application of potent topical medications can have excellent efficacy while minimizing the potential for systemic side effects. A limitation of trials in dermatology is that topical treatment tends to be more difficult for patients to use than oral medications. An advantage of dermatologic trials is that the outcomes are readily visible on the skin. Investigator and patient assessed outcomes can be used. A trial of a topical clobetasol illustrates the rapid effects of the drug and the relative safety over the short term.
Central Nervous System (CNS)
Authors: Sommer David B. , Goldstein Larry B. 
Published: September 2014

AbstractModern clinical trials have revolutionized the practice of neurology. This introduction examines some of the issues and challenges involved in therapeutic trials for six common conditions that affect the central nervous system (ischemic stroke, Alzheimer's disease, migraine headache, epilepsy, Parkinson's disease, and multiple sclerosis) and explores how trials in these areas influence clinical practice. Examples of ongoing trials in each of these areas are also identified.
Update in Hyperlipidemia Clinical Trials
Authors: Chien Kuo‐Liong 
Published: September 2014

AbstractCurrent large‐scale clinical trial evidence clearly demonstrates that statins can reduce cardiovascular mortality and morbidity in hyperlipidemic patients. These clinical trial results clearly indicate that cholesterol reduction can reduce cardiovascular risks significantly. However, several issues are still under debate, such as subgroup benefits, lipid subtypes, and new outcome measures. Progress on study design, surrogate endpoints, outcomes measures, and new drugs improve the knowledge of hyperlipidemia treatment. In this review, the recent advances on clinical trials of hyperlipidemia will be discussed and some hints on recent updated information will be provided.
Institutional and Independent Review Boards
Authors: Fish Susan S. 
Published: September 2014

Abstract
Institutional Review Boards (IRBs), or Independent Ethics Committees (IECs) as they are known around the world, are a cornerstone of the system of protections human research subjects. An IRB is a committee that functions as a regulatory and ethics review board for research that involves human subjects. In countries that regulate research in humans, IRBs/IECs are required to approve research studies prior to performing any research intervention, which includes screening, on any subject. In countries without research regulations, IRBs/IECs may also review research. This article focuses mainly on IRBs that are regulated by U.S. agencies.
Ethical Challenges Posed by Cluster Randomization
Authors: Klar Neil , Donner Allan 
Published: September 2014

AbstractThe purpose of this article is to discuss the ethical challenges posed by cluster randomized trials, which is done by revisiting the development of ethical guidelines that have been largely developed in the context of individually randomized clinical trials. A recurring theme underlying the article is that the relative absence of ethical guidelines for cluster randomized trials appears to have created a research environment in which the choice of randomization unit may determine whether informed consent is deemed necessary before random assignment. It seems questionable, on both an ethical level and a methodological level, whether the unit of randomization should play such a critical role in obtaining informed consent. Editors should require all reports of randomized trials to include mention of Institutional Review Board (IRB) approval and to discuss subject consent. For cluster randomized trials, however, additional discussion of procedures used to obtain informed consent is warranted.
Multistage Genetic Association Studies
Authors: Thomas Duncan , Conti David 
Published: September 2014

AbstractThe practice of individualized medicine for therapy or prevention could be informed by studies of genetic modifiers of treatment effects. This special case of gene–environment interaction studies offers several unique opportunities by exploiting random assignment of treatments. Two basic approaches are possible, one based on genetic studies added to a preexisting randomized controlled trial, the other based on targeted intervention studies added to a preexisting genetic cohort study. We focus in particular on multistage sampling designs that exploit the prior information from the parent study to improve the statistical efficiency of the study of genetic modifiers of treatment effects. In particular, we discuss different contexts in which either design might be used—a study of a single candidate gene, an entire pathway, or a genome‐wide association scan.
Longitudinal Data
Authors: Li Hung‐Ir 
Published: September 2014

Abstract
Longitudinal data have been the focus in various research such as economy, social science, public health, and biomedical research in the past few decades. These data comprise outcome measures or responses prospectively collected or naturally accrued repeatedly from the same subjects across multiple time points along with subject characteristic measures. In some observational studies in epidemiology/public heath, the data accrued over a long period of time as recorded in medical charts and are retrospectively retrieved. The data are used to answer questions regarding the change or progression of the disease over time and/or potential risk factors for the disease. Although in other studies, the existing data could be used to identify subgroups with different background characteristics or traits. The identified groups are then followed up for their health conditions for a certain period of time prospectively. The data from these groups are then compared with answer questions set out for the studies. In the clinical research and development settings, most of the longitudinal data are collected via prospectively designed clinical trials in order to assess the changes in efficacy and/or safety of certain medical intervention, drug, biologics, or devices. The longitudinal data from clinical trials in phase III of product development are intended to confirm that the clinical benefit or desired changes over time are primarily due to the target product, can be maintained for an adequate amount of time, and are applicable to a well‐defined population before regulatory approval is granted and the product is registered for public use. This article provides a summary of the definitions, features, and various types of longitudinal data resulting from clinical trials in phase III medicinal product development. The respective characteristics and the related issues along with the rules of thumb in the selection of the statistical methods for the analyses will also be provided.
Confidence Intervals in Clinical Trials
Authors: Reiczigel Jeno 
Published: September 2014

AbstractA confidence interval (CI) or interval estimate for an unknown parameter θ is a “from‐to” range calculated from the sample so that it contains θ with a high—usually 90%, 95%, or 99%—probability. CIs can be given for unknown population parameters like the mean, variance, median, proportion, or for model parameters like a regression coefficient, odds ratio, correlation coefficient, or for functions of the above‐mentioned parameters like, for example, the variance/mean ratio, the difference of two proportions or two means, and so on. In clinical studies, CIs are typically given for the mean or median treatment effect, proportion of cure, difference in these outcome measures comparing two treatments, and so on. Practically for anything for which an estimate from the sample is meaningful at all, a CI is also meaningful. For curves like a regression line or a ROC curve, interval estimation results in a so‐called confidence band.
Discriminant Analysis with Biomedical Examples
Authors: Wernecke Klaus‐D. 
Published: September 2014

Abstract
This article describes discriminant analysis. First, a brief overview is provided followed by discussions of allocation rules and their error rates, Bayes allocation rules, discrimination using normal models, nonparametric discrimination, logistic discriminant analysis, future methods, and judging reliability. Finally, a Section on software packages concludes the article.
Frailty Models
Authors: Wienke Andreas 
Published: September 2014

AbstractThe notion of frailty provides a convenient way to introduce random effects, association, and unobserved heterogeneity into models for survival data. In its simplest form, a frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or related individuals. In essence, the frailty concept goes back to the work of Greenwood and Yule on “accident proneness.” The term frailty itself was introduced by Vaupel et al. in univariate survival models, and the model was substantially promoted by its application to multivariate survival data in a seminal paper by Clayton (without using the notion “frailty”) on chronic disease incidence in families.
Interval Censored: Introduction
Authors: Gerds Thomas A , Meier‐Hirmer Carolina 
Published: September 2014

AbstractIn survival or time‐to‐event analysis, interval censoring occurs if the time of an event is theendpointof interest that cannot be observed exactly for each patient but occurred chronologically between two follow‐ups. Information of the event status is typically collected by periodically performing clinical examinations or laboratory tests. Events of interest can be the onset of a disease, infection, the recurrence of a tumor, and other changes in the course of a disease. For example, in aclinical trial/study, the progression of a tumor can only be assessed at periodic examination times, e.g., every 6 months. The true time‐to‐progression is hidden somewhere between two adjacent examination times, e.g., between 12 and 18 months. Another typical example is the infection time, e.g., of HIV, when for the diagnosis the result oflaboratory testsis needed. The time‐to‐infection is included in an interval defined by the dates of acquisition. Two sources of censoring can be allocated. External censoring is due to the study design, which prohibits instantaneous access of the event of interest because the patient is not all the time medically supervised. An example for externally introduced censoring is the arrangement of yearly instead of monthly or daily examinations. On the other hand, the censoring can be due to the natural limits of the experiment. For instance, if the event of interest is the onset of a tumor, or anadverse drug reactionbecoming visible only after a certain limit of toxication is exceeded.
Kaplan‐Meier Plot
Authors: Holländer Norbert 
Published: September 2014

AbstractThe Kaplan–Meier plot or Kaplan–Meier curve is the standard method to describe and compare graphically the overall survival of groups of patients. This method is applied to survival time, which is defined as time from treatment start or date of randomization to death or last date of follow up, and, more general, to any type of time‐to‐event data, such as time‐to‐disease progression, time‐to‐detoriation, or time‐to‐first toxicity. Sometimes, the time‐to‐event is also called failure time. In the sequel, the term “survival time” is used as a synonym for any time‐to‐event.
Linear Model: Overview
Authors: Burkholder Iris , Edler Lutz 
Published: September 2014

AbstractLinear Models for simple and multiple regression, Analysis of Variance (ANOVA), and Analysis of Covariance (ANCOVA) have broad and often direct application in clinical trials. Various extensions and generalizations of the Linear Model have been developed more recently often motivated by medical research problems. Most prominent are the loglinear models for the evaluation of count data; the logistic regression model for the evaluation of the influence of clinical factors on a dichotomous outcome as, for example, cancer incidence; and, last but not least, the Cox regression model for the analysis of survival data.
Designs with Randomization Following Initial Study Treatment
Authors: Dunger‐Baldauf Cornelia 
Published: September 2014

Abstract
Clinical study designs with randomization after the initial study treatment have been used in diverse disease areas such as arrhythmia, Alzheimer's disease, schizophrenia, migraine, headache, cancer, acquired immunodeficiency syndrome, and irritable bowel syndrome. They are useful in situations where the placebo‐controlled parallel group trial would be difficult to perform, such as in the investigation of long‐term efficacy of a drug when long‐term placebo treatment is not feasible, to address specific scientific questions, or to evaluate complex treatment schemes. Clinical studies with randomization after the initial study treatment are conducted in at least two stages. In the first stage, patients might either commence with a single treatment in an open‐label fashion, or they might be randomized to several treatments, possibly including a control. A treatment‐free interval might follow. Depending on the scientific question addressed by the trial, entry in the second trial stage might depend on the patient's benefit from the initial treatment. Further randomized trial stages can be added in the same fashion. The application of the approach will be discussed in detail for randomized withdrawal designs, randomized delayed start designs, and randomized retreatment designs.
N
of 1 Randomized Trial
Authors: Avins Andrew L. , Neuhaus John 
Published: September 2014

Abstract
“N‐of‐1 clinical trials” are randomized, multiple‐period crossover trials carried out in individual patients. Most clinical trials are conducted for the purpose of producing information generalizable to a larger population, but N‐of‐1 trials are usually conducted to inform a single patient and a clinical‐care provider about the effects of a specific therapy for that patient. Generalizing beyond that patient is usually not the intent of an N‐of‐1 study, though multiple N‐of‐1 trials may be combined to produce results with greater external validity. Like all crossover studies, there is a limited range of conditions and treatments that are amenable to investigation with an N‐of‐1 design, and practical barriers often exist to wider implementation of these studies in the typical clinical setting. Nevertheless, N‐of‐1 trials provide a potential means for providing more evidence‐based individualized therapeutic decision‐making in clinical practice.
Min Test
Authors: Laska Eugene M , Meisner Morris J 
Published: September 2014

AbstractThe Min test is a statistical tool used to test a null hypothesis such asHoi: νi≤ 0 for at least oneiversus the alternative H1: νi > 0 fori = 1, 2,…,K. This is sometimes called the sign‐testing problem. If every α‐level test of Hoi: νi≤ 0 individually rejects thei‐th null hypothesis at the α level, then the global null hypothesis is rejected according to the Min test. No adjustment for multiplicity is required. Such hypotheses arise in many clinical trials when it is desired to establish that a set of inequalities on the parameters simultaneously holds. Examples include testing whether an identified treatment is best on single or multiple endpoints; whether one treatment is equivalent to or noninferior to a control; and whether a dose of a combination is synergistic.