bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sibling Interaction Effects
Authors: Boomsma Dorret I. 
Published: September 2014

Abstract

Human beings often grow up with siblings and the behavior of one sibling may influence the behavior of the other sibling. In such cases, part of the ‘environment’ of individuals is predictable from the genotypes of their siblings. To detect sibling interactions, which are based on
genotype
, the variances and covariances of siblings reared at different densities (e.g., twins and singletons) or siblings of different degrees of genetic relatedness (e.g., mono‐ and dizygotic twins) may be compared. In the classical
twin design
, sibling interaction effects are suggested by a difference in phenotypic variance between mono‐ and dizygotic twins, and a pattern of twin correlations that is inconsistent with additive genetic influences.
Comorbidity
Authors: Rhee S. H. , Hewitt John K. , Corley R. P. , Stallings M. C. 
Published: September 2014

AbstractComorbidity between psychiatric disorders often exceeds the rate expected by chance alone. Increased knowledge regarding the causes of comorbidity between two psychiatric disorders may have a significant impact on research regarding the classification, treatment, and etiology of both of disorders. We examined the validity of common methods used to test alternative comorbidity models.
Time Series Analysis
Authors: Brockwell P. J. 
Published: September 2014

AbstractThe time‐domain approach to the modeling and forecasting of time series is outlined. Weakly stationary processes are introduced, and an introduction to ARMA processes and their properties is given. Estimation, model selection, and prediction with these models are considered, and extensions to multivariate time series, state‐space models and nonlinear models are indicated.
Bayesian Dose‐Finding Designs in Healthy Volunteers
Authors: Zhou Yinghui 
Published: September 2014

Abstract
In the majority of phase I clinical trials, experimental drugs are administered via predefined dose‐escalation schemes to healthy volunteers to explore the relationship between the dose administered and the primary responses of interest. The responses might be binary outcomes such as adverse events; quantitative summary measures concerning pharmacokinetics (PK), such as the average or maximum plasma concentration over time; or pharmacodynamic (PD), such as heart rate or blood pressure changes from baseline. Conventional dose‐escalation designs can take a long time to complete to investigate every available dose. Also, conventional statistical analyses are not accurate due to small sample sizes: 20 to 80 volunteers are typically used in phase I trials. Adaptive designs have been studied as they can potentially speed up dose‐escalation and have better properties in terms of avoidance of unsafe doses and accuracy of estimation when compared with conventional designs. This review of state‐of‐the‐art adaptive designs illustrates how such a design can be implemented and discusses some practical issues met during implementation.
Clinical Data Coordination
Authors: Kious Cheryl 
Published: September 2014

Abstract
This article provides an overview of the role of the Lead Data Manager, including best practices and considerations in the clinical data management process. The clinical data management process is responsible for taking the raw data provided by investigative sites and converting it into a high‐quality database “…enabling the data to be efficiently analysed and displayed.” The processes outlined are assumed for a regulatory study, and they are divided into the phases of study initiation, study conduct, and study closure.
Clinical Data Management
Authors: McBride Ruth 
Published: September 2014

AbstractClinical data management is data management systems and process focused on providing clean, current and reliable data to support clinical research, especially for clinical trials. Computerized systems have been used since the early 1960's. Current clinical data management systems vary. Many, but not all clinical trials use electronic data capture to collect data from clinical research sites. Efforts are underway to standardize the way clinical data are organized.
Censored Data and Clinical Trials
Authors: Andersen Per Kragh 
Published: September 2014

AbstractThe concepts of independent right censoring and noninformative right censoring in survival analysis are defined. Dependent censoring is a kind of selection bias and adds to biased inference for the survival time distribution. Informative censoring typically leads to inefficient inference. Examples are presented and relations between right censoring and competing risks are discussed. Other kinds of censoring, including interval censoring are touched upon.
Correlation, including intraclass
Authors: Armitage Peter 
Published: September 2014

Abstract
In a loose sense, two variables are correlated if changes in one tend to be accompanied by changes in the other. More specifically, correlation implies a linear relationship with superimposed random variation. The product–moment correlation coefficient was introduced by Galton and greatly exploited by Karl Pearson. Its square is the proportion of the variance of one variable “explained” by linear regression on a second variable. Standard tests are described in the article. An important extension is to intraclass correlation for measurements on individuals falling into groups, which measures the association between individuals in the same group.
Control Groups
Authors: Green Stephanie 
Published: September 2014

AbstractUse of appropriate control groups is a critical principle in clinical research methods. The modern randomized controlled trial is the most convincing and reliable method for minimizing bias and demonstrating effectiveness of treatment. Control groups for randomized trials may be patients randomized to no treatment, a placebo, or standard active therapy with or without a placebo. Due to practical and ethical constraints not all clinical questions can be addressed with a well‐controlled randomized trial. Non‐randomized trials can have specific comparator groups or be controlled in the sense that carefully chosen historical information from the literature is used for reference.
Hypothesis
Authors: Rosenkranz Gerd 
Published: September 2014

AbstractIn the following article, the authors briefly review the role of hypotheses in science before statistical hypotheses and their relevance for clinical studies are investigated. The authors explain what statistical hypotheses have in common with scientific hypotheses and how they differ. In particular, testing statistical hypotheses is somewhat different from testing scientific hypotheses, although some general ideas apply to both. Eventually, the importance of formulating testable hypotheses for clinical studies are explored and some specific topics like superiority, equivalence, non‐inferiority, and multiple hypotheses are discussed.
Group‐Randomized Trials: Overview
Authors: Murray David M. 
Published: September 2014

AbstractThe purpose of this article is to put group‐randomized trials (GRTs) in context in terms of other kinds of designs and in terms of the terminology used in other fields, to summarize their development in public health, to characterize the range of public health research areas that now employ GRTs, to characterize the state of practice with regard to the design and analysis of GRTs, to consider their future in public health research, and to review the steps required to plan a new GRT.
Statins
Authors: F. Wilson Peter 
Published: September 2014

AbstractThe roles of statins to decrease blood cholesterol levels and to reduce risk for cardiovascular disease in the blood are described. Mechanism of action, safety, efficacy, drug interactions, and results from major randomized clinical trials are the major topics discussed. Recent data are summarized and provide an understanding of the clinical role of use of statins as agents for primary and secondary prevention of atherosclerotic cardiovascular disease. Recent comprehensive reviews have identified no relative risk differences for statins in the prevention of cardiovascular diseases for persons <65 years versus >65 years, men versus women, treated hypertension versus no hypertension, history of diabetes mellitus versus no diabetes mellitus, LDL‐C at baseline <135 mmol/L versus ≥135 mg/dl.
Diagnostic Studies
Authors: Schwenke Carsten 
Published: September 2014

AbstractDiagnostic studies are performed to investigate the clinical usefulness of a new diagnostic procedure, for example, a new contrast agent for imaging or a new laboratory test to detect antibodies or antigens in blood. Sometimes, these studies are invented to substitute the standard procedure by an improved or less expensive procedure, which is not inferior in terms of efficacy and has some benefit compared with the standard procedure. Before starting a diagnostic study, first the unit for analysis should be defined in the terms “experimental unit” and “observational unit.”
Regression Models to Incorporate Patient Heterogeneity
Authors: O'Quigley John 
Published: September 2014

AbstractAlthough we anticipate heterogeneity among patients included in a dose finding study, given the typically small number of patients available, it is common to group all the patients under a single heading. However, careful modeling can still enable us to make gains when dealing with clearly defined subgroups. Rather than carry out separate independent studies, it is possible via the use of simple regression models to include all patients under one heading while still allowing for the possibility that the groups behave differently. Different schedules of treatment may also lead to different behaviors and, under a working assumption that the groups behave similarly, it is important to have a mechanism that will indicate to us that the accumulating evidence suggests the groups are behaving differently. Regression models can enable us to do this. Certain studies involve more than a single grouping covariable, an example is the two prognostic groups based on heavily and lightly pretreated patients, together with a variable that labels the scheduling. For three different schedules and two prognostic groups, we have a total of six subgroups. Separate analyses are not feasible, and a regression model can be of great use in such situations.
Vaccine
Authors: F. Chan Ivan S. 
Published: September 2014

Abstract
Vaccines are primarily developed to prevent infectious diseases. They have been given to millions of people worldwide and contributed significantly to the improvement in public health. This article provides an overview of the clinical development of vaccines in terms of efficacy, immunogenicity, and safety assessment. The high expectation for the benefit‐to‐risk ratio required for developing preventive vaccines generates some unique challenges in the design and analysis of vaccine clinical trials. We will briefly discuss these challenges and illustrate with real examples.
Clinical Trials to Support Prescription to Over‐the‐Counter Switches
Authors: Brass Eric P. 
Published: September 2014

Abstract
In contrast to prescription drugs, over‐the‐counter (OTC) drugs require the consumer to make decisions about the use of the drug without the aid of a health‐care professional. Clinical research studies designed to assess the ability of consumers to use the OTC drug label to guide their treatment with the drug are an essential element of OTC regulatory decision making. Label comprehension studies test the label's communication of key messages to consumers. Actual use studies elucidate consumer behaviors when using the proposed OTC drug in a simulated OTC environment. Examples of how these studies can be used to inform program development and decision making are illustrated by case studies.
Disease Trials on Pediatric Patients
Authors: Bauchner Howard 
Published: September 2014

Abstract
Randomized clinical trials are the cornerstone of the Cochrane Collaboration and recommendations from the USPSTF. Data suggest that there are less RCTs involving children than adults. Unique obstracts that researchers face when conducting RCTs which include children as subjects include: limited number of children with major medical diseases; issues related to ethics, and measurable objective health outcomes.
Ethical Issues in International Research
Authors: Lie Reidar K. 
Published: September 2014

Abstract
This article discusses four recent controversies in international collaborative research: the standard of care controversy, the obligation to provide ancillary care to clinical trial participants, the obligation to continue to provide trial interventions after completion of the research, and the requirement to be responsive to the health care needs of the host country. In each case the central positions and arguments in the debate are presented.
The Belmont Report
Authors: Zucker Deborah 
Published: September 2014

Abstract

The Belmont Report summarizes the basic ethical principles and guidelines identified by the commission to assist in the protection of human subjects in research. The report focuses on three main areas, (
1
) the boundaries between research and practice, (
2
) the basic ethical principles that should underlie the conduct of research (autonomy, beneficence, justice) and the protection of human subjects, and (
3
) the application of these principles into practice. The report outlines general recommendations regarding obtaining informed consent, the assessment of risk and benefit, and the recruitment of participants. The Belmont Report lays out many of the ethical considerations that, to this day, are used in formulating regulations and in ensuring the protection of human study volunteers.
Gene Therapy
Authors: Ginn Samantha L , Alexander Ian E 
Published: September 2014

Abstract
Conducting a clinical trial poses a unique set of challenges that must be addressed to ensure the safety of human subjects. In this article, we discuss issues that relate in particular to gene therapy clinical trials. These issues include the choice of gene delivery system, the need for extensive preclinical testing to ensure the feasibility and safety of the approach, and careful monitoring of subjects for short‐ and long‐term toxicity associated with the gene transfer protocol.
Cell Line
Authors: Sivakumaran Theru A. , Iyengar Sudha K. 
Published: September 2014

AbstractA cell line is obtained after the first subculture of primary cell culture that is established from freshly isolated tissues. Until the establishment of a primary culture, cells are usually heterogeneous and still closely represent the parent cell types and express tissue‐specific properties. After several passages, the cell line will die out because of senescence or escape senescence to become a continuous cell line, then to become a valuable resource, particularly if it is preserved after characterization and validation. Cell lines are used to study the normal physiology or biochemistry of the cells in disease and health, in drug screening as well as in studies of cell toxicity mechanisms. Today, cell lines may also assist stem‐cell therapy.
DNA
Bank
Authors: Sivakumaran Theru A , Iyengar Sudha K 
Published: September 2014

Abstract

Most diseases are multifactorial and are caused by a combination of genetic and environmental factors. Similarly, the way a person responds to a drug is also a complex trait that is influenced by many different genes. Paradigms to identify causative genetic variants responsible for disease or response to drugs are well described in the literature, and the following essential elements are required for successful completion of such studies: (
1
) rapid detection of genetic variation; (
2
) large number of samples of high quality with well‐characterized clinical data from participants, which includes longitudinal follow‐up for effect of treatment; (
3
) advanced informatics for storage and retrieval of samples and data; and (
4
) establishment of correlation between clinical and genetic data through modern statistical techniques. Although the basic conventions for sample collection in gene mapping were already established, it was only after completion of Human Genome Project and the initiation of the International HapMap Project that the need for large databases of well‐phenotyped study participants collected under unified protocols with DNA available was expressed. These endeavors resulted in the establishment of national and international DNA banks aimed initially at mapping specific disease entities, but later encompassing more global undertakings. Establishment of these DNA biobanks has not been without ethical, legal, and personal controversies, although their value to the biomedical research community is indisputable. The overall outcome of these initiatives is that the promise of personalized medicine is nearby, particularly for identification of the genetic variations responsible for drug response. We anticipate that clinical trials could perform genetic prescreening to exclude people who are unresponsive to treatment, which reduces side effects and increases the effectiveness of the drug for particular subgroups. This model is already being implemented for the P450 class of drugs, with the hope of more to come.
Flexible Designs
Authors: Posch Martin , Bauer Peter , Brannath Werner 
Published: September 2014

AbstractFlexible designs allow for mid‐trial design adaptations in an adaptive interim analysis based on all information collected while controlling the type I error rate. Examples of such adaptations are, for example, sample size reassessment, addition or dropping of interim analysis, or dropping of treatment arms. The crucial feature of the adaptive procedure is that the adaptation rules need not be specified in advance.
Imputation
Authors: Cook Samantha R. , Rubin Donald B. 
Published: September 2014

AbstractMissing data are a common problem with datasets in clinical trials. Imputation, or filling in the missing values, is an intuitive and flexible way to handle incomplete data. Here the authors present several imputation strategies and their theoretical background, as well as some current examples and advice on computation. Multiple imputation is a statistically valid strategy for handling missing data.
Regression to the Mean in Clinical Trials
Authors: Davis Clarence E. 
Published: September 2014

Abstract
When a person is selected for having an extreme measurement (e.g., high blood pressure), a second measure of the variable will, on average, be closer to the mean of the population distribution. This function is called regression to the mean. It is used as a justification for a randomized control group in clinical trials. It may also affect recruitment in clinical trials in which potential participants are selected because they hold an extreme value on some variable.
Multiple Comparisons ‐ Recent Advances
Authors: Proschan Michael A. 
Published: September 2014

AbstractBecause clinical trials are very expensive, investigators often want to make several comparisons in the same trial. But, performing several statistical tests at significance level α will increase the probability of at least one false positive declaration. The situation is analogous to throwing many darts at a target; even a completely unskilled dart thrower will eventually hit the bull's eye. Many multiple comparison adjustment methods control the probability of at least one false positive by “raising the bar” for each separate comparison. However, raising the bar increases the chance of missing a true effect and increases the sample size needed to maintain the same power. The tension between controlling the chance of at least one false positive and missing a true effect makes multiplicity adjustment a subject of great controversy in clinical trials. Although clinical trialists have differing opinions about how much adjustment is needed, certain settings exist most would agree that adjustment is needed and others in which most would agree that it is not. This article attempts to shed light on situations in which multiplicity adjustments are needed and the most common methods of adjustment in different clinical trial scenarios, which include multiple arms, multiple endpoints, subgroup analysis, and interim monitoring.
Lan‐DeMetsAlpha‐Spending Function
Authors: DeMets David L. , Gordon Lan K. K. 
Published: September 2014

AbstractRandomized clinical trials are typically monitored periodically for early evidence of convincing benefit or of potential harm for both scientific and ethical reasons by a data monitoring committee. After increments in the number of patients randomized or in the number of observed events, interim statistical analyses may be conducted to evaluate emerging results. This process may be referred to as a group sequential design. However, repeated analysis of accumulating data with conventional statistical testing can lead to substantially increased Type I or false positive error rates. The alpha spending function provides a flexible approach to control the Type I error while allowing the data monitoring committee to review the data as needed without having to specifya prioriwhen and how many such interim analyses will be conducted. Other earlier group sequential methods had these somewhat restrictive requirements. This alpha spending function approach can be applied to several spending functions that approximate some of these earlier methods.
Imaging Science in Medicine
Authors: Hendee William R. 
Published: September 2014

Abstract
The use of imaging technologies has expanded tremendously in medicine over the past two decades. This expansion reflects the evolution of new imaging tools that reveal human anatomy and physiology in ever‐increasing detail, as well as the development of digital data acquisition and processing techniques that extract information from human tissues in ever‐expanding degrees of subtlety. A further factor that is stimulating growth in imaging is the expansion of knowledge about biological systems at the cellular, molecular, and genetic levels. This factor is transforming medical imaging from a “technology‐push” discipline into one where fundamental biological questions, as well as important clinical issues, are the force driving development of new imaging technologies. This transformation demands enhanced knowledge about the interface between biological properties of tissues and the characteristics of imaging systems. Exploration of the existing state of knowledge about the interface, and what is needed for the immediate future, is the subject of this article.
Eligibility and Exclusion Criteria
Authors: Foulkes Mary A. 
Published: September 2014

AbstractDesign characteristics of a clinical trial, such as eligibility and exclusion criteria, are broadly influential in the conduct, interpretation, and impact of trials. The research objectives, the intended inference or generalization of results, the balance of factors affecting the trial recruitment and conduct, all impact the determination of eligibility and exclusion criteria. Clinical contraindications, regulatory and ethical considerations also relate to this determination.
Data Monitoring Committees including Operational Issues
Authors: Ellenberg Susan 
Published: September 2014

AbstractData Monitoring Committees, also known as Data and Safety Monitoring Boards, are often established as a component of clinical trial oversight to review interim data of the ongoing trial so that safety concerns or problematic operational issues can be identified rapidly and corrective measures can be undertaken. Such committees may be independent of the trial sponsor and investigator to ensure recommendations are as objective as possible and unaffected by conflicts of interest. Federal funding and regulatory agencies have issued policy documents concerning the establishment and operation of Data Monitoring Committees, but substantial variation exists in committee processes.